IMR

Volume 41 Number 1 (Spring 2007)101ndash126

101

copy 2007 by the Center for Migration Studies of New York All rights reservedDOI 101111j1747-7379200700058x

Blackwell Publishing LtdOxford UKIMREInternational Migration Review0197-9183copy 2007 by the Center for Migration Studies of New York All rights reservedSpring 2007411Original Article

⅞ ⅝ frac34 ⅞

Migration Estimation Based on the Labour Force Survey An EU-15 Perspective

Moacutenica MartiacuteCarmen Roacutedenas

Universidad de Alicante

It can be observed in the research that the European Union Labour ForceSurvey (EU LFS) only allows a satisfactory estimation of the

stocks

of non-nationals or those born abroad in some countries whereas it proves to beless than adequate in most of them with regard to migration

flows

Webelieve that this very limited success is due to a twofold statistical problemof

imprecision

and

bias

which is intensified by the embarrassing questionof

answer impossible

These difficulties exist among the Member States toa greater or lesser degree depending on the characteristics of the migratorydomain and the particular features that the EU LFS acquires in each country

INTRODUCTION

Knowing how labor migration is measured and whether comparablemeasurements are made throughout European Union (EU) countries are bothwell justified necessities The impulse of new European requirements aroundemployment policy creates a situation in which migration ndash both internal andinternational and both in terms of flows and stocks ndash is very relevant Reliableaccurate and harmonized statistical information is also a challenge in thecontext of a monetary union in which labor mobility is expected to play afundamental role in the macroeconomic adjustment According to the Theoryof Optimal Currency Areas faced with an asymmetric

shock

renouncing theexchange rate as an instrument of economic policy could be compensated forby labor movements Therefore it is important to identify migratory patternsand to achieve this it is essential to have the appropriate statistical information

However in the EU this information is very incomplete and unequalCurrently the only obligation Member States have to fulfill is to periodicallysubmit available migration data to Eurostat and to declare its origin Lackinga specific and harmonized statistical source member countries measure migra-tion from very different starting points Moreover they may use differentsources according to the migration dimension concerned internal

flows

102 I

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international

flows

stock

of foreign population or of active foreign populationThe different sources (Table 1) include the census administrative populationregisters surveys residence permits for foreigners or when evaluating theactive foreign population the Labour Force Survey or work permits Unsur-prisingly the lack of homogeneity among sources impedes the realization ofany kind of comparative analysis

1

In this context it is important to evaluate whether the European UnionLabour Force Survey (EU LFS) could be an adequate option for harmonizedmeasurement of this phenomenon The EU LFS follows a common metho-dology which means that its questionnaire provides information on the laborsituation of immigrants and their socioeconomic characteristics and in theoryits data should be comparable However as shown in Table 1 only a few coun-tries use the EU LFS as a statistical reference source Against all expectationsits use is unequal and for certain migration dimensions infrequent In realitythis source is mainly used for the

stock

of active foreign population which is afigure required by Eurostat and the United Nations Economic Commission forEurope for the

Joint Questionnaire of the United Nations Statistical Division

Moreover some countries ndash such as Belgium Spain France Portugal and theUnited Kingdom ndash only resort to this source for lack of alternatives as theiradministrative registers or censuses have no information on the labor situationof immigrants and they recognize that although they use it measuring this phe-nomenon through LFS data is not trustworthy mainly because of its small samplesize or the fact that it does not sample collective households (Clark

et al

1998)The objective of this study is to evaluate the quality and intercountry

comparability of the EU LFS statistical information on migration Accordinglythe first section describes its principal features and compares its migrationinformation ndash internal and international ndash with that from alternative nationalsources such as censuses and registers In the second section we investigatewhy there are differences in data from different sources concentrating on hownational designs of the LFS influence the estimation of migration figures Inthis second part we tackle the problems of

lack of precision

sources of

bias

andthe specific question of

answer impossible

for the estimation of migration flowsFinally the study closes with its main conclusions and recommendations

1

For example there would be problems in comparing census data as the information is updatedover long and differing periods of time ndash five or ten years according to the country ndash which areunsuitable for measuring mobility There would also be problems with comparisons of data fromadministrative registers or permits as each country has its own rules and attends to its ownadministrative needs and criteria can vary widely from state to state

M

E

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103

TABLE 1C

OUNTRIES

OF

THE

EU

-

15

W

HICH

C

OLLECT

M

IGRATION

D

ATA

BY

D

ATA

T

YPE

AND

S

OURCE

EU-15

International Migration Foreigners

Internal Migration Foreign Population Foreign Workers

Foreign Labor Force

Stocks Inflows Stocks Inflows Stocks Inflows

Eurostat

a

OECD

b

Eurostat

a

OECD

b

Eurostat

a

OECD

b

OECD

b

Eurostat

c

University Queensland Survey

d

Austria (A) C R na R WP WP WP WP RBelgium (B) R amp C R R R Ministry of Labour

and Employment WP WP LFS R

Denmark (DK) R R R R R R RP R RGermany (D) R amp MC R LFS amp MC R LFS amp MC LFS WP LFS amp MC RGreece (EL) C amp RP na RP RP WP na na WP CSpain (E) RP RP R na WP WP WP LFS C amp RFrance (F) C C RP RP LFS LFS WP LFS CIreland (IRL) LFS LFS C C WP LFS WP LFS C R amp LFSItaly (I) C amp RP RP R amp RP RP RP WP WP LFS amp RP C amp RLuxembourg (L) R R R R WP WP WP Social Security

General Inspection C

Netherlands (NL) R R R R LFS WP amp others other sources na LFS RPortugal (P) LFS amp C RP S C amp RP RP RP RP WP RP amp LFS CFinland (FIN) R R R R WP WP WP amp RP ER RSweden (S) R R R amp RP R WP LFS na ER RUnited Kingdom

(UK) LFS LFS S S LFS LFS WP LFS C amp R

Notes C Census ER Employment Register LFS Labour Force Survey MC Microcensus R Population Register or Register of Foreigner RP Residence Permits WP Work PermitsS Special Survey na information not available

Sources

a

Eurostat (20006)

b

OECD (2003a4)

c

Clark

et al

(199813ndash39)

d

Bell Rees and Wilson (2003)

104 I

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THE EU LFS GENERAL CHARACTERISTICS AND ALTERNATIVE MIGRATION MEASURES

The first EU LFSs were made in the 1970s and given the absence ofinternationally accepted definitions there was a great lack of harmonizationamong the surveys imprecision was high and comparability almost nil Effortstowards harmonizing the surveys began according to Eurostat (2003a) in the1980s when EU countries started to apply the recommendations of theInternational Labour Organisation (ILO) and particularly in the 1990s withthe adoption and development of various community regulations

2

which havecontributed greatly towards the harmonization of the design and structure ofthe LFSs of the Member States At present the National Statistics Offices areresponsible for designing their own surveys but they are subject to restrictionsimposed by community regulations The national offices select the populationsample to be surveyed in the EU LFS carry out the interviews and submitthe data to Eurostat using a common codification system Eurostat developsthe program used to analyze the results and processes and circulates theinformation

3

The questionnaires are drawn up by each Member State in their nationallanguages and although the EU LFS questions are identical they can alsoinclude questions of individual interest to each country The 1992 revision ofthe variables covered by the EU LFS allows inclusion of new topics relevant tothe Single Market such as the mobility of people (Eurostat 2003a) Hencealthough the EU LFS was never intended to measure mobility the commonquestionnaire of the survey now includes questions that allow estimation ofboth the

stock

of foreign immigrants and the internal and international flowsof immigrants along with their labor situation With regard to the

stock

offoreigners the survey includes the variables of nationality and placecountry ofbirth Therefore we can estimate the

stock

of either the nonnational populationor the foreign-born population With regard to

flows

the questionnaire asks forthe countryregion of residence one year before the survey By comparing thiswith the current countryregion of residence we can account for immigrantsfrom other countries or internal immigrants accordingly

One method of approaching the analysis of the quality of the EU LFSmigration data is to compare it with that of other sources such as censuses or

2

Basically Council Regulations EEC No 304489 and No 57798

3

Eurostat (19989ndash10)

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105

population registers In theory there should be a certain degree of coincidenceamong different sources although we cannot expect absolute similarity TheEU LFS and censuses measure flows retrospectively they compare currentplace of residence with that of a previous date and therefore capture an im-migrant in cases where the current place of residence does not coincide with thatof the earlier date Registers are made from information provided by thepopulation and are therefore constantly updated

When we compare these three statistical sources which measure thesame phenomenon ndash mobility ndash in different ways there are technical reasonsto expect that flows found through registers will be higher than those foundthrough censuses and the EU LFS This should be the case for variousreasons Firstly registers measure

migrations

and there is nothing to stopindividual

migrants

having more than one migration The differences will begreater the longer the interval between the reference periods of the censusesandor surveys and the higher the number of intermediate migrationsduring the period Secondly registers could show higher results because theycapture residence changes at the moment they occur andor are declaredwhereas in censuses and surveys changes are revealed by

surviving migrants

when they are interviewed always assuming that there are no problems ofhistorical memory

Similarly it is reasonable to think that if the EU LFS is correctly designedand carried out it will capture a higher volume of migrants than censuses Thisis because in censuses the question on previous place of residence usuallycovers a period of five or ten years whereas the EU LFS asks the same questionbut for one year before Formulated in this way the census question inevitablyincurs more personal memory mistakes which will undervalue mobilityMoreover with the census question it is not possible to account for inter-mediate or returning migrants within the five or ten year period whereas theannual LFS better captures these possibilities Finally to estimate

flows

the LFSuses a wider population age range as it includes migrants aged one year or olderwhereas censuses only consider those of five or older or ten or older accordingto the individual country Obviously when the census question covers an inter-val of only one year the census and the LFS would be expected to producecloser results

In Tables 2a and 2b we show the values of six dimensions of the migra-tion phenomenon for the EU-15 These are annual internal and internationalmobility

flows

stock

of foreigners defined through nationality and place ofbirth and finally

stock

of the nonnational labor force and the employed non-national stock Each of these six variables has been taken from the appropriate

106 I

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population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

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107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

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109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

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TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

102 I

M

R

international

flows

stock

of foreign population or of active foreign populationThe different sources (Table 1) include the census administrative populationregisters surveys residence permits for foreigners or when evaluating theactive foreign population the Labour Force Survey or work permits Unsur-prisingly the lack of homogeneity among sources impedes the realization ofany kind of comparative analysis

1

In this context it is important to evaluate whether the European UnionLabour Force Survey (EU LFS) could be an adequate option for harmonizedmeasurement of this phenomenon The EU LFS follows a common metho-dology which means that its questionnaire provides information on the laborsituation of immigrants and their socioeconomic characteristics and in theoryits data should be comparable However as shown in Table 1 only a few coun-tries use the EU LFS as a statistical reference source Against all expectationsits use is unequal and for certain migration dimensions infrequent In realitythis source is mainly used for the

stock

of active foreign population which is afigure required by Eurostat and the United Nations Economic Commission forEurope for the

Joint Questionnaire of the United Nations Statistical Division

Moreover some countries ndash such as Belgium Spain France Portugal and theUnited Kingdom ndash only resort to this source for lack of alternatives as theiradministrative registers or censuses have no information on the labor situationof immigrants and they recognize that although they use it measuring this phe-nomenon through LFS data is not trustworthy mainly because of its small samplesize or the fact that it does not sample collective households (Clark

et al

1998)The objective of this study is to evaluate the quality and intercountry

comparability of the EU LFS statistical information on migration Accordinglythe first section describes its principal features and compares its migrationinformation ndash internal and international ndash with that from alternative nationalsources such as censuses and registers In the second section we investigatewhy there are differences in data from different sources concentrating on hownational designs of the LFS influence the estimation of migration figures Inthis second part we tackle the problems of

lack of precision

sources of

bias

andthe specific question of

answer impossible

for the estimation of migration flowsFinally the study closes with its main conclusions and recommendations

1

For example there would be problems in comparing census data as the information is updatedover long and differing periods of time ndash five or ten years according to the country ndash which areunsuitable for measuring mobility There would also be problems with comparisons of data fromadministrative registers or permits as each country has its own rules and attends to its ownadministrative needs and criteria can vary widely from state to state

M

E

B

L

F

S

103

TABLE 1C

OUNTRIES

OF

THE

EU

-

15

W

HICH

C

OLLECT

M

IGRATION

D

ATA

BY

D

ATA

T

YPE

AND

S

OURCE

EU-15

International Migration Foreigners

Internal Migration Foreign Population Foreign Workers

Foreign Labor Force

Stocks Inflows Stocks Inflows Stocks Inflows

Eurostat

a

OECD

b

Eurostat

a

OECD

b

Eurostat

a

OECD

b

OECD

b

Eurostat

c

University Queensland Survey

d

Austria (A) C R na R WP WP WP WP RBelgium (B) R amp C R R R Ministry of Labour

and Employment WP WP LFS R

Denmark (DK) R R R R R R RP R RGermany (D) R amp MC R LFS amp MC R LFS amp MC LFS WP LFS amp MC RGreece (EL) C amp RP na RP RP WP na na WP CSpain (E) RP RP R na WP WP WP LFS C amp RFrance (F) C C RP RP LFS LFS WP LFS CIreland (IRL) LFS LFS C C WP LFS WP LFS C R amp LFSItaly (I) C amp RP RP R amp RP RP RP WP WP LFS amp RP C amp RLuxembourg (L) R R R R WP WP WP Social Security

General Inspection C

Netherlands (NL) R R R R LFS WP amp others other sources na LFS RPortugal (P) LFS amp C RP S C amp RP RP RP RP WP RP amp LFS CFinland (FIN) R R R R WP WP WP amp RP ER RSweden (S) R R R amp RP R WP LFS na ER RUnited Kingdom

(UK) LFS LFS S S LFS LFS WP LFS C amp R

Notes C Census ER Employment Register LFS Labour Force Survey MC Microcensus R Population Register or Register of Foreigner RP Residence Permits WP Work PermitsS Special Survey na information not available

Sources

a

Eurostat (20006)

b

OECD (2003a4)

c

Clark

et al

(199813ndash39)

d

Bell Rees and Wilson (2003)

104 I

M

R

THE EU LFS GENERAL CHARACTERISTICS AND ALTERNATIVE MIGRATION MEASURES

The first EU LFSs were made in the 1970s and given the absence ofinternationally accepted definitions there was a great lack of harmonizationamong the surveys imprecision was high and comparability almost nil Effortstowards harmonizing the surveys began according to Eurostat (2003a) in the1980s when EU countries started to apply the recommendations of theInternational Labour Organisation (ILO) and particularly in the 1990s withthe adoption and development of various community regulations

2

which havecontributed greatly towards the harmonization of the design and structure ofthe LFSs of the Member States At present the National Statistics Offices areresponsible for designing their own surveys but they are subject to restrictionsimposed by community regulations The national offices select the populationsample to be surveyed in the EU LFS carry out the interviews and submitthe data to Eurostat using a common codification system Eurostat developsthe program used to analyze the results and processes and circulates theinformation

3

The questionnaires are drawn up by each Member State in their nationallanguages and although the EU LFS questions are identical they can alsoinclude questions of individual interest to each country The 1992 revision ofthe variables covered by the EU LFS allows inclusion of new topics relevant tothe Single Market such as the mobility of people (Eurostat 2003a) Hencealthough the EU LFS was never intended to measure mobility the commonquestionnaire of the survey now includes questions that allow estimation ofboth the

stock

of foreign immigrants and the internal and international flowsof immigrants along with their labor situation With regard to the

stock

offoreigners the survey includes the variables of nationality and placecountry ofbirth Therefore we can estimate the

stock

of either the nonnational populationor the foreign-born population With regard to

flows

the questionnaire asks forthe countryregion of residence one year before the survey By comparing thiswith the current countryregion of residence we can account for immigrantsfrom other countries or internal immigrants accordingly

One method of approaching the analysis of the quality of the EU LFSmigration data is to compare it with that of other sources such as censuses or

2

Basically Council Regulations EEC No 304489 and No 57798

3

Eurostat (19989ndash10)

M

E

B

L

F

S

105

population registers In theory there should be a certain degree of coincidenceamong different sources although we cannot expect absolute similarity TheEU LFS and censuses measure flows retrospectively they compare currentplace of residence with that of a previous date and therefore capture an im-migrant in cases where the current place of residence does not coincide with thatof the earlier date Registers are made from information provided by thepopulation and are therefore constantly updated

When we compare these three statistical sources which measure thesame phenomenon ndash mobility ndash in different ways there are technical reasonsto expect that flows found through registers will be higher than those foundthrough censuses and the EU LFS This should be the case for variousreasons Firstly registers measure

migrations

and there is nothing to stopindividual

migrants

having more than one migration The differences will begreater the longer the interval between the reference periods of the censusesandor surveys and the higher the number of intermediate migrationsduring the period Secondly registers could show higher results because theycapture residence changes at the moment they occur andor are declaredwhereas in censuses and surveys changes are revealed by

surviving migrants

when they are interviewed always assuming that there are no problems ofhistorical memory

Similarly it is reasonable to think that if the EU LFS is correctly designedand carried out it will capture a higher volume of migrants than censuses Thisis because in censuses the question on previous place of residence usuallycovers a period of five or ten years whereas the EU LFS asks the same questionbut for one year before Formulated in this way the census question inevitablyincurs more personal memory mistakes which will undervalue mobilityMoreover with the census question it is not possible to account for inter-mediate or returning migrants within the five or ten year period whereas theannual LFS better captures these possibilities Finally to estimate

flows

the LFSuses a wider population age range as it includes migrants aged one year or olderwhereas censuses only consider those of five or older or ten or older accordingto the individual country Obviously when the census question covers an inter-val of only one year the census and the LFS would be expected to producecloser results

In Tables 2a and 2b we show the values of six dimensions of the migra-tion phenomenon for the EU-15 These are annual internal and internationalmobility

flows

stock

of foreigners defined through nationality and place ofbirth and finally

stock

of the nonnational labor force and the employed non-national stock Each of these six variables has been taken from the appropriate

106 I

M

R

population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M

E

B

L

F

S

103

TABLE 1C

OUNTRIES

OF

THE

EU

-

15

W

HICH

C

OLLECT

M

IGRATION

D

ATA

BY

D

ATA

T

YPE

AND

S

OURCE

EU-15

International Migration Foreigners

Internal Migration Foreign Population Foreign Workers

Foreign Labor Force

Stocks Inflows Stocks Inflows Stocks Inflows

Eurostat

a

OECD

b

Eurostat

a

OECD

b

Eurostat

a

OECD

b

OECD

b

Eurostat

c

University Queensland Survey

d

Austria (A) C R na R WP WP WP WP RBelgium (B) R amp C R R R Ministry of Labour

and Employment WP WP LFS R

Denmark (DK) R R R R R R RP R RGermany (D) R amp MC R LFS amp MC R LFS amp MC LFS WP LFS amp MC RGreece (EL) C amp RP na RP RP WP na na WP CSpain (E) RP RP R na WP WP WP LFS C amp RFrance (F) C C RP RP LFS LFS WP LFS CIreland (IRL) LFS LFS C C WP LFS WP LFS C R amp LFSItaly (I) C amp RP RP R amp RP RP RP WP WP LFS amp RP C amp RLuxembourg (L) R R R R WP WP WP Social Security

General Inspection C

Netherlands (NL) R R R R LFS WP amp others other sources na LFS RPortugal (P) LFS amp C RP S C amp RP RP RP RP WP RP amp LFS CFinland (FIN) R R R R WP WP WP amp RP ER RSweden (S) R R R amp RP R WP LFS na ER RUnited Kingdom

(UK) LFS LFS S S LFS LFS WP LFS C amp R

Notes C Census ER Employment Register LFS Labour Force Survey MC Microcensus R Population Register or Register of Foreigner RP Residence Permits WP Work PermitsS Special Survey na information not available

Sources

a

Eurostat (20006)

b

OECD (2003a4)

c

Clark

et al

(199813ndash39)

d

Bell Rees and Wilson (2003)

104 I

M

R

THE EU LFS GENERAL CHARACTERISTICS AND ALTERNATIVE MIGRATION MEASURES

The first EU LFSs were made in the 1970s and given the absence ofinternationally accepted definitions there was a great lack of harmonizationamong the surveys imprecision was high and comparability almost nil Effortstowards harmonizing the surveys began according to Eurostat (2003a) in the1980s when EU countries started to apply the recommendations of theInternational Labour Organisation (ILO) and particularly in the 1990s withthe adoption and development of various community regulations

2

which havecontributed greatly towards the harmonization of the design and structure ofthe LFSs of the Member States At present the National Statistics Offices areresponsible for designing their own surveys but they are subject to restrictionsimposed by community regulations The national offices select the populationsample to be surveyed in the EU LFS carry out the interviews and submitthe data to Eurostat using a common codification system Eurostat developsthe program used to analyze the results and processes and circulates theinformation

3

The questionnaires are drawn up by each Member State in their nationallanguages and although the EU LFS questions are identical they can alsoinclude questions of individual interest to each country The 1992 revision ofthe variables covered by the EU LFS allows inclusion of new topics relevant tothe Single Market such as the mobility of people (Eurostat 2003a) Hencealthough the EU LFS was never intended to measure mobility the commonquestionnaire of the survey now includes questions that allow estimation ofboth the

stock

of foreign immigrants and the internal and international flowsof immigrants along with their labor situation With regard to the

stock

offoreigners the survey includes the variables of nationality and placecountry ofbirth Therefore we can estimate the

stock

of either the nonnational populationor the foreign-born population With regard to

flows

the questionnaire asks forthe countryregion of residence one year before the survey By comparing thiswith the current countryregion of residence we can account for immigrantsfrom other countries or internal immigrants accordingly

One method of approaching the analysis of the quality of the EU LFSmigration data is to compare it with that of other sources such as censuses or

2

Basically Council Regulations EEC No 304489 and No 57798

3

Eurostat (19989ndash10)

M

E

B

L

F

S

105

population registers In theory there should be a certain degree of coincidenceamong different sources although we cannot expect absolute similarity TheEU LFS and censuses measure flows retrospectively they compare currentplace of residence with that of a previous date and therefore capture an im-migrant in cases where the current place of residence does not coincide with thatof the earlier date Registers are made from information provided by thepopulation and are therefore constantly updated

When we compare these three statistical sources which measure thesame phenomenon ndash mobility ndash in different ways there are technical reasonsto expect that flows found through registers will be higher than those foundthrough censuses and the EU LFS This should be the case for variousreasons Firstly registers measure

migrations

and there is nothing to stopindividual

migrants

having more than one migration The differences will begreater the longer the interval between the reference periods of the censusesandor surveys and the higher the number of intermediate migrationsduring the period Secondly registers could show higher results because theycapture residence changes at the moment they occur andor are declaredwhereas in censuses and surveys changes are revealed by

surviving migrants

when they are interviewed always assuming that there are no problems ofhistorical memory

Similarly it is reasonable to think that if the EU LFS is correctly designedand carried out it will capture a higher volume of migrants than censuses Thisis because in censuses the question on previous place of residence usuallycovers a period of five or ten years whereas the EU LFS asks the same questionbut for one year before Formulated in this way the census question inevitablyincurs more personal memory mistakes which will undervalue mobilityMoreover with the census question it is not possible to account for inter-mediate or returning migrants within the five or ten year period whereas theannual LFS better captures these possibilities Finally to estimate

flows

the LFSuses a wider population age range as it includes migrants aged one year or olderwhereas censuses only consider those of five or older or ten or older accordingto the individual country Obviously when the census question covers an inter-val of only one year the census and the LFS would be expected to producecloser results

In Tables 2a and 2b we show the values of six dimensions of the migra-tion phenomenon for the EU-15 These are annual internal and internationalmobility

flows

stock

of foreigners defined through nationality and place ofbirth and finally

stock

of the nonnational labor force and the employed non-national stock Each of these six variables has been taken from the appropriate

106 I

M

R

population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

104 I

M

R

THE EU LFS GENERAL CHARACTERISTICS AND ALTERNATIVE MIGRATION MEASURES

The first EU LFSs were made in the 1970s and given the absence ofinternationally accepted definitions there was a great lack of harmonizationamong the surveys imprecision was high and comparability almost nil Effortstowards harmonizing the surveys began according to Eurostat (2003a) in the1980s when EU countries started to apply the recommendations of theInternational Labour Organisation (ILO) and particularly in the 1990s withthe adoption and development of various community regulations

2

which havecontributed greatly towards the harmonization of the design and structure ofthe LFSs of the Member States At present the National Statistics Offices areresponsible for designing their own surveys but they are subject to restrictionsimposed by community regulations The national offices select the populationsample to be surveyed in the EU LFS carry out the interviews and submitthe data to Eurostat using a common codification system Eurostat developsthe program used to analyze the results and processes and circulates theinformation

3

The questionnaires are drawn up by each Member State in their nationallanguages and although the EU LFS questions are identical they can alsoinclude questions of individual interest to each country The 1992 revision ofthe variables covered by the EU LFS allows inclusion of new topics relevant tothe Single Market such as the mobility of people (Eurostat 2003a) Hencealthough the EU LFS was never intended to measure mobility the commonquestionnaire of the survey now includes questions that allow estimation ofboth the

stock

of foreign immigrants and the internal and international flowsof immigrants along with their labor situation With regard to the

stock

offoreigners the survey includes the variables of nationality and placecountry ofbirth Therefore we can estimate the

stock

of either the nonnational populationor the foreign-born population With regard to

flows

the questionnaire asks forthe countryregion of residence one year before the survey By comparing thiswith the current countryregion of residence we can account for immigrantsfrom other countries or internal immigrants accordingly

One method of approaching the analysis of the quality of the EU LFSmigration data is to compare it with that of other sources such as censuses or

2

Basically Council Regulations EEC No 304489 and No 57798

3

Eurostat (19989ndash10)

M

E

B

L

F

S

105

population registers In theory there should be a certain degree of coincidenceamong different sources although we cannot expect absolute similarity TheEU LFS and censuses measure flows retrospectively they compare currentplace of residence with that of a previous date and therefore capture an im-migrant in cases where the current place of residence does not coincide with thatof the earlier date Registers are made from information provided by thepopulation and are therefore constantly updated

When we compare these three statistical sources which measure thesame phenomenon ndash mobility ndash in different ways there are technical reasonsto expect that flows found through registers will be higher than those foundthrough censuses and the EU LFS This should be the case for variousreasons Firstly registers measure

migrations

and there is nothing to stopindividual

migrants

having more than one migration The differences will begreater the longer the interval between the reference periods of the censusesandor surveys and the higher the number of intermediate migrationsduring the period Secondly registers could show higher results because theycapture residence changes at the moment they occur andor are declaredwhereas in censuses and surveys changes are revealed by

surviving migrants

when they are interviewed always assuming that there are no problems ofhistorical memory

Similarly it is reasonable to think that if the EU LFS is correctly designedand carried out it will capture a higher volume of migrants than censuses Thisis because in censuses the question on previous place of residence usuallycovers a period of five or ten years whereas the EU LFS asks the same questionbut for one year before Formulated in this way the census question inevitablyincurs more personal memory mistakes which will undervalue mobilityMoreover with the census question it is not possible to account for inter-mediate or returning migrants within the five or ten year period whereas theannual LFS better captures these possibilities Finally to estimate

flows

the LFSuses a wider population age range as it includes migrants aged one year or olderwhereas censuses only consider those of five or older or ten or older accordingto the individual country Obviously when the census question covers an inter-val of only one year the census and the LFS would be expected to producecloser results

In Tables 2a and 2b we show the values of six dimensions of the migra-tion phenomenon for the EU-15 These are annual internal and internationalmobility

flows

stock

of foreigners defined through nationality and place ofbirth and finally

stock

of the nonnational labor force and the employed non-national stock Each of these six variables has been taken from the appropriate

106 I

M

R

population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M

E

B

L

F

S

105

population registers In theory there should be a certain degree of coincidenceamong different sources although we cannot expect absolute similarity TheEU LFS and censuses measure flows retrospectively they compare currentplace of residence with that of a previous date and therefore capture an im-migrant in cases where the current place of residence does not coincide with thatof the earlier date Registers are made from information provided by thepopulation and are therefore constantly updated

When we compare these three statistical sources which measure thesame phenomenon ndash mobility ndash in different ways there are technical reasonsto expect that flows found through registers will be higher than those foundthrough censuses and the EU LFS This should be the case for variousreasons Firstly registers measure

migrations

and there is nothing to stopindividual

migrants

having more than one migration The differences will begreater the longer the interval between the reference periods of the censusesandor surveys and the higher the number of intermediate migrationsduring the period Secondly registers could show higher results because theycapture residence changes at the moment they occur andor are declaredwhereas in censuses and surveys changes are revealed by

surviving migrants

when they are interviewed always assuming that there are no problems ofhistorical memory

Similarly it is reasonable to think that if the EU LFS is correctly designedand carried out it will capture a higher volume of migrants than censuses Thisis because in censuses the question on previous place of residence usuallycovers a period of five or ten years whereas the EU LFS asks the same questionbut for one year before Formulated in this way the census question inevitablyincurs more personal memory mistakes which will undervalue mobilityMoreover with the census question it is not possible to account for inter-mediate or returning migrants within the five or ten year period whereas theannual LFS better captures these possibilities Finally to estimate

flows

the LFSuses a wider population age range as it includes migrants aged one year or olderwhereas censuses only consider those of five or older or ten or older accordingto the individual country Obviously when the census question covers an inter-val of only one year the census and the LFS would be expected to producecloser results

In Tables 2a and 2b we show the values of six dimensions of the migra-tion phenomenon for the EU-15 These are annual internal and internationalmobility

flows

stock

of foreigners defined through nationality and place ofbirth and finally

stock

of the nonnational labor force and the employed non-national stock Each of these six variables has been taken from the appropriate

106 I

M

R

population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

106 I

M

R

population census or register and has been compared with the correspondingvalue estimated by the EU LFS

4

Figure I presents the results graphicallyIf we admit differences of

plusmn

10 among the statistical sources the LFSestimations of stocks would be more accurate than those of flows In fact theestimation of migration flows through the LFS presents a high level of dis-crepancy in all cases when compared to information from registers or censusesboth for internal and international flows In general the LFS substantiallyunderestimates annual flows except in the cases of Austria France and Portu-gal for which it overestimates However it performs better when estimatingstocks and in some countries ndash Austria Belgium France LuxembourgSweden and the United Kingdom ndash the degree of coincidence is very high

4We have tried to confirm these figures with the corresponding national statistics offices withthe exception of Spain as we have sufficient knowledge of the statistical system To date we havereceived answers from Portugal Luxembourg Belgium Austria the UK Ireland theNetherlands Sweden and Italy

TABLE 2aMIGRATION DATA BY COUNTRY (FLOWS)

Country Year

Annual Internal Flowa Annual International Flowb

PopulationRegister (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Denmark (DK) 2002 na na ndash 52778 15094 286Italy (I) 2000 359008 88019 245 227471 24816 109Sweden (SE) 2001 na 63182 ndash 60795 na ndashSpain (E) 2001 313731 51110 1629 414772 87299 2105Netherlands (NL) 2002 258956 na ndash 121250 na ndashFinland (FI) 2000 na na ndash 16895 5002 2961Austria (AT) 2001 81946 375929 45875 89928 30909 3437Germany (D) 2002 1153495 675400 5855 842495 255476 302Luxembourg (L) 2001 na na ndash 12135 3048 2511Belgium (B) 2001 na 207322 ndash 77584 5183 668

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Ireland (IE) 2002 260854 na ndash 76104 na ndashUnited Kingdom (UK)d 2001 1010590 766480 7584 370430 61854 1670France (F) 1999 548803c 838960 15287 129831c 178853 13776Portugal (PT) 2001 59606 105786 17748 105705 26320 2490Greece (EL) 2001 166310 19670 1183 67251 13666 2032

Notes aChanges of region of residence (NUTS II level) during the previous yearbInflows (national and nonnational) from abroad during the previous yearcAnnual mean period 1990ndash99dCensus 2001 for England and Wales and changes of region of residence (NUTS I level)na information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) and Database New Cronosfor EU-LFS and OECD (2003b)

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M

E

B

L

F

S

107

TABLE 2bMIGRATION DATA BY COUNTRY (STOCKS)

Country Year

Nonnational Population Stock Foreign-Born Population Stock

Year

Nonnational Labor Force Stock Nonnational Employed Stock

Population Register (R)

LFS(L)

(LR)

Population Register (R)

LFS(L)

(LR)

Register(R)a

LFSb

(L)

(LR)Register

(R)aLFSb

(L)

(LR)

Denmark (DK) 2002 266729 192865 723 308700d 268395d 869 2002 166375 86250 5184 148630 76000 5113Sweden (SE) 2001 475986 429103 902 1027974 778891 758 2001 227000 211000 9295 202000 188500 9332Spain (E) 2001 1370675 597830 4362 1969269 1148740 5830 2001 924220e 387250 4190 764046e 333750 4368Netherlands (NL) 2002 690393 657217 9519 1547079 1733311 11204 2000 na 327500 ndash 235000f 303750 12926Finland (FI) 2000 91100 67862 7449 136200 19650 1443 2000 41400 32000 7729 na 23750 ndashGermany (D) 2002 7335592 7096680 9674 na na ndash 2002 na 3526000 ndash 2008062 3049000 15184Belgium (B) 2001 861685 829356 9625 na 1052146 ndash 2000 386200f 367500 9516 na 309000 ndash

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Census(C)

LFS(L)

(LC)

Italy (I) 2000 1464589 na ndash na na ndash 2001 na na ndash 636499 na ndashAustria (AT) 2001 710926 712847 10027 1003399 892666 8896 2002 370200g 364000 9833 334100g 364250 10902Ireland (IE) 2002 224261 187453 8359 400016 311776 7794 2002 na 101250 ndash na 95500 ndashUnited Kingdom (UK)c 2001 na 2584047 ndash 4643086 4684899 10090 2002 na 1446500 ndash na 1329000 ndashFrance (F) 1999 3263186 3264900 10005 5870000 5461990 9305 2000 1530526 1554000 10153 na 1230000 ndashLuxembourg (L) 2001 162285 162748 10029 144844 131177 9056 2000 na 77000 ndash 152700g 75000 4912Portugal (PT) 2001 258584 184610 7139 651472 459101 7047 2000 na 101250 ndash 99800g 93250 9344Greece (EL) 2001 726191 329520 4538 1122894 491841 4380 2001 na 195500 ndash 391584 172750 4412

Notes aDifferent population and labor market registers (social security andor work permits)bAnnual average (except D L and SE [second quarter] and F [first quarter])cCensus 2001 for England and Wales and changes of region of residence (NUTS I level)d2000eCensus 2001f1999 for Belgium 1998 for the NetherlandsgDifferent registersna information not available

Sources National Statistics Offices (EU-15) Census andor Population Registers Eurostat (2003b) Database New Cronos for EU-LFS OECD (2003b) OECD (2003c)

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

108 I M R

Generally the data shown in the tables and Figure I raise two very clearquestions i) why does the LFS estimate some variables better than others andii) why do some countries have higher coincidence than others when con-sidering different sources Our main hypothesis is that this situation is the resultof the specific national sample design of each country In fact although the EULFS may be homogeneous and Eurostat may have minimum requirements interms of sample error in order to guarantee trustworthy regional representationndash NUTS Level II ndash in the estimations5 the design of the LFS is not identicalin all countries This contrary to expectations is very important in order toproduce correct and comparable results among different countries especiallyin certain items such as those related to mobility In theory the EU LFSappears to be harmonized However the national differences in samplingframe sample stratification rotation pattern final sample unit and amongothers domain size impede the correct and harmonized collection of data

Roacutedenas and Martiacute (1997 Martiacute and Roacutedenas 2004) tackle the problemsof estimating the migration phenomenon as measured through the SpanishLFS and conclude that the Spanish sample design creates significant biasand low precision In view of the data of Tables 2a and 2b and of the nationalsample design differences of the LFS (Table 3) it could be thought that similar

5See Council Regulation EEC No 57798

Figure I Degree of Fit among the Estimations of the EU LFSs and the Different Sources

Source Tables 2a and 2b

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M

E

B

L

F

S

109TABLE 3

TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Austriaa Belgiumb Denmarkc Germanya Greeced Spaine Francef Irelandg

Frequency of Results Quarterly Quarterly Quarterly Annual Quarterly Quarterly Quarterly QuarterlySampling Frame

Basis of sampling frame Austrian HousingCensus

PopulationRegister

The Population Register and The Unemployment Register

For the ldquooldrdquo LaumlnderPopulation Census and The Census of Buildings and Housing of 1987

PopulationCensus

PopulationCensus

PopulationCensus

PopulationCensus

For the ldquonewrdquo Laumlnder Population Register

Updating of the basis Annual na na Annual na Quarterly na QuarterlyLowest level sample unit Dwelling Household Person Clusters of households Household Dwelling Dwelling HouseholdCollective households

sampled No No Yes Yes No No Yes NoCriteria for stratification Region and Region (exists Registered Region Region Region and Region Region

socioeconomic at province level unemployment socioeconomicNUT-II)

Sample DesignSample size 31000 dwellings 48000 16665 people 150000 households 30000 65208 54000 39000

households households dwellings dwellings householdsSampling fraction 07 111 04 045 087 na 017 33Rotation scheme 8 Waves 2 Waves 3 Waves (2-3-1) 4 Waves 6 Waves 6 Waves 6 Waves 5 Waves of the sample being 50 100 6667 25 6667 6667 6667 80

replaced each year One-eighth each One-half each One-third each One-fourth each year One-sixth One-sixth each quarter

One-sixth each One-fifthquarter quarter quarter each quarter quarter each quarter

Data Collection non-response 15 na 29 3 8ndash10 na 136 7Compulsoryvoluntary Voluntary Compulsory Voluntary Voluntary na Voluntary na Voluntary

Weighting ProceduresVariables of

poststratificationSex age region

and nationalitySex age and

provinceGross income age

and education (for people registered as unemployed) or industry (for non registered as unemployed)

Sex age region and nationality

None Sex age and region

Sex and age Sex age and region

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

110I

M

R

TABLE 3 (CONTINUED)TECHNICAL FEATURES OF THE NATIONAL EUROPEAN UNION LABOUR FORCE SURVEYS

Italyh Luxembourgd Netherlandsa Portugald Finlanda Swedeni United Kingdomj

Frequency of Results Quarterly Annual Monthly Quarterly Monthly Monthly QuarterlySampling Frame

Basis of sampling frame Municipal Population Register

CentralPopulationRegister

Geographical BaseRegister (list of alladdresses compiled by the Post Office) and the Register of Houses in Amsterdam

PopulationCensus

Central PopulationRegister

StatisticsSwedenrsquos Registerof the Total Population (RTB) and Swedenrsquos Employment Register (SREG)

Most of Great BritainPostcode Address FileNorth of the Caledonian Telephone directory Northern Ireland the Rating and Valuation Lists

Updating of the basis na na na na Monthly Daily naLowest level sample unit Household Household Household Household Person Person HouseholdCollective households

sampled No No No No Yes Yes YesCriteria for stratification Region None Region Region Region and

demographic (sex and age band)

Region sex age nationality and employment

Region

Sample DesignSample size 75516 households 8500 households 10000 households 20000 households 12000 people 59400 people 57000 householdsSampling fraction 036 5 007 068 02 na 02Rotation scheme 4 Waves (2-2-2) None 4 Waves 6 Waves 5 Waves (3-2-2) 8 Waves 5 Waves of the sample being 50 75 na 6667 60 50 80

replaced each year One-fourth each One-sixth each One-fifth each One-eighth each One-fifth each quarterquarter quarter quarter quarter

Data Collection 22 (1st wave) non-response 5 22 40ndash50 9 15 12ndash14 6 (2ndash5 waves)Compulsoryvoluntary Compulsory Voluntary Voluntary Compulsory Voluntary Voluntary Voluntary

Weighting ProceduresVariables of post-

stratificationSex and age Sex age Sex age region Sex age and Sex age region and Sex age and Sex age and region

nationality and nationality and marital region labor force status labor forcesize of household status status

Note na information not availableSources aQuatember (2002)

bInstitut National de Statistique (2003)cDanmarks Statistik (2001)dEurostat (1998)eInstituto Nacional Estadiacutestica (2002)fGivord (2003)gEurostat (1998) Central Statistics Office (2003)hEurostat (1998) ISTAT(2003)iMirza and Houmlrngren (2002)jQuatember (2002) Office for National Statistics (2003)

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 111

problems would also be present in other EU countries Therefore we willcontinue with a study into how national survey design could influence themeasurement of this phenomenon Underlying this we want to know if it ispossible to use the resources arising from the various LFSs to estimate with atolerable level of precision and without bias a subpopulation such as immigrantsthrough a sample designed and sized to obtain generalized results and provideinformation on the labor market aggregates

HOW THE NATIONAL DESIGN OF THE LFS INFLUENCES THE ESTIMATION OF MIGRATION

The Accuracy of Estimations

The accuracy of an estimation depends on the size of the sample in relation tothe domain size on the heterogeneity of the variable studied in the finalsampling unit and on the efficiency of the stratification technique Withregard to the first aspect which in this case is mobility in the total populationit is known that sample sizes chosen to study general features or to measureparticular parameters cannot be used to analyze very infrequent characteristicsThe permissible error could be substantially increased as the number ofinterviews of people with this feature would not be sufficient

In the case of the EU-15 countries the migration domain sizes examinedare generally reduced and unequal With regard to stocks Figure II shows thatwith the exception of Luxembourg with values of above 30 nonnational pro-portions are around 5 of the national population The stock of the foreign-born population is about 8 and finally the stock of active andor employednonnationals is between 2 and 4 of the national population also with theexception of Luxembourg

Migration domain size is even smaller in the case of flows ndash both inter-national and internal (Figure III) Annual immigration from abroad does notrise above the 3 of Luxembourg and is only 03 in Finland With regard tointernal migration differences among countries are also very significant and arenot due to the effects of different regional scaling Precisely to avoid distortionresulting from different regional dimensions in Figure III we present the rateof total internal mobility residency changes over a year Through this we arealso able to capture the maximum internal migration intensity with which weobtain the largest possible domain size for internal migration In any casenational differences are great more than 6 of the population annually changetheir municipal residence in Greece Ireland Sweden and the UK whereas thisproportion is little more than 2 in Italy and Spain In general the domain

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

112 I M R

sizes for those countries for which we have information are small This is veryimportant when evaluating the quality of statistical migration informationfrom the LFS

Clearly the domain size studied is relevant for the wellness of estimationsobtained through surveys As shown by Purcell and Kish (1979367) when adomain does not reach a sufficient size we cannot always guarantee a satisfac-tory application of traditional sampling This does not only happen with the

Figure II Size of Domain Migration Stocks ( population)

Source Table 2b

Figure III Size of Domain Migration Flows ( population)

Source Table 2a

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 113

so called mini domains where between 001 and 1 of the reference popu-lation presents the characteristic studied which is precisely the case of annualinternational immigration in many countries (Figure III) We can also encoun-ter problems with a minor domain in which the characteristic is found inbetween 1 and 10 of the reference population which is the case in almostall countries for the remaining migration dimensions

Evidently for the same sampling fraction a domain or characteristic thatis more frequent in one country than in another will be better captured in theformer and in addition a low frequency characteristic will be better estimatedwith a larger sampling fraction In Table 3 we show the sampling fraction ofthe LFS in each Member State and it can be seen that the differences aresubstantial In Austria Belgium Greece Ireland Luxembourg and Portugalmore than 05 of the population are sampled whereas in France theNetherlands Finland and the UK the sampling fraction is below 025

In the case of Austria Belgium and Luxembourg the differences(Table 2b) between the LFS stock estimations and those from censuses and regi-sters are relatively small In fact these three countries have considerable samplesizes and domains that ndash at least for stocks ndash are relatively large However largedomains and sampling fractions do not always lead to estimations of sufficientquality in Greece and Ireland with similar characteristics well-fitting estima-tions are not obtained Additionally countries such as Germany France theNetherlands Sweden and the UK have acceptable estimations for relativelylarge domains but have small sampling fractions

It seems therefore that sample size has less influence on the accuracy ofestimations than domain size although this conclusion is very rudimentarybearing in mind the foreseeable influence of other differences in national LFSsHowever migration domain size is without doubt one of the factors thatexplain why practically none of the EU countries are able to accurately estimatemigration flows through the LFS In our opinion it is only appropriate toanalyze international migration through the LFS in terms of stock whosefrequency of appearance is further from critical limits

It is possible that the high level of error found in the estimation of themigration domain could be even higher because of the fact that most countriesdesign their LFS through cluster sampling in which the final sample units arehouseholds or dwellings6 which include one or more individuals For clustersampling to be as precise as simple random sampling for a certain characteristic

6In the majority of EU countries sample units are households or dwellings whereas inDenmark Finland and Sweden the units are individual people

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

114 I M R

there should be no correlation in the variable among the members of the clus-ter in other words no homogeneity in the variable for all the members of thehousehold

It is fairly common for the migration characteristic to affect the wholefamily group especially in countries with strong Catholic roots As demon-strated in Spain by Martiacute and Roacutedenas (2004) this means that when a sampleunit (SU) is interviewed after migration in general there is not only onemigrant captured but the whole household Therefore the larger the house-hold size the greater the number of migrants counted The same applies theother way round when there are problems in capturing immigrants ndash forexample due to domain size nonresponse or sample loss ndash each noncapturedSU results in far fewer individual migrants being counted

In Figure IV we show two variables that can help evaluate these effectsthe average number of people per householddwelling and the proportion ofhouseholdsdwellings with two or more inhabitants When values are close tothe origin such as in Sweden Germany Denmark and Finland the homo-geneity effect of the migration variable in the cluster will be less In contrast in

Figure IV HouseholdDwelling Date by Country

Source Tables 2a and 2b

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 115

southern andor largely Catholic countries such as Greece Portugal SpainIreland and Italy the higher average number of people per household and thefewer number of single person households lead to a very significant homo-geneity effect of the migration characteristic in the cluster This is such that whenmigrations are correctly captured they are captured ldquovery correctlyrdquo and whenthere are any problems they are multiplied

With a domain of these characteristics (reduced size and homogeneousin the final sampling unit) stratified sampling is the technique used to obtaintrustworthy estimations provided it is possible to find a population partitionconsisting mainly of migrants Stratification insofar as in each stratum it ispossible to group units that are homogenous to each other and heterogeneousin relation to the other strata reduces the variance of estimators increases pre-cision and finally contributes to reducing sampling errors For this methodto be efficient the variables used for the stratification should be correlated withthe object variables of the study In the majority of EU countries the stratifi-cation criterion used is geographical If the immigrants in one of these coun-tries are concentrated in a certain region this procedure could perhaps reducesampling errors but if this is not the case (which is most likely) sampling errorswill still be high Sweden is the only country that uses the nationality variableas a stratification criterion (Table 3) meaning that it is possible to reducesampling error in the estimation of the stock of foreign immigrants In fact theestimation of the stock of nonnationals in this country fits fairly well with itsregistry value

Bias in the Estimations

Apart from accuracy-related problems deriving from sample size and domaincharacteristics the estimation of the migration phenomenon is also affected bythe typical sources of bias which in this particular case could have importanteffects We are referring to the suitability of the sampling frame its updatingand to nonresponse

With regard to the sampling frame suitability it can be seen in Table 3that more than half of the countries only use private households to constructtheir samples and do not sample collective households This sampling plancharacteristic could be a source of underestimation In the case of foreignimmigration flows the first places of residence for many foreign immigrantsare reception centers hostels or similar establishments7 and in the case of

7For example OECD (2003a5)

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

116 I M R

internal migration flows many high-migration groups such as military person-nel or students8 are left out of the surveys

A fundamental requirement for good estimations from periodic surveysis the frequent updating of the sampling frame Accordingly it could bethought that samples based on continually updated population registers wouldprobably obtain better results than those based on population censuses whichare usually made every ten years Belgium Denmark Italy Luxembourg Fin-land and Sweden base their samples on population registers The Netherlandsand the UK use a sampling frame based on addresses supplied by the postalservice which are continually updated However Austria Germany GreeceSpain France Ireland and Portugal design their samples from censuses9

which could in principle be affected by this problemIn countries in which the sample is census-based demographic growth

areas due to migration could go unaccounted for until the next census and itis also possible that in the meantime the flow may have reduced or that themigrants may have moved to other places Therefore when samples are basedon an infrequently updated sampling frame it will be more difficult to capturemigration movements as and where they happen The solution to the problemlies in assiduous updating of the sampling frame Austria and Germany annu-ally update their census information and Spain and Ireland update quarterlythus minimizing the problem

In any case the most important source of bias in the LFS comes fromnonresponse either from there being no people residing in the household ordue to reluctance or refusal to cooperate with the survey Although non-response is a nonsampling error that is difficult to quantify in Figure V we showthe nonresponse percentages of thirteen EU countries Obviously in countrieswhere participation in the survey is obligatory ndash such as Belgium Italy andPortugal ndash there is higher participation

Chief among the countries with high levels of nonresponse (over 15)are Denmark Luxembourg the Netherlands and the UK10 Nonresponse initself is not what generates bias this happens when it is correlated with popu-lation characteristics If this correlation is not adequately corrected bias is pro-duced in the estimation of these characteristics There are various indicationsthat migration is probably correlated to nonresponse Some studies11 show that

8Rees and Kupiszewski (1999562)9See Table 310UK 22 in the first survey and 6 in the others11CSO and ONS (199823) or Clark et al (19988)

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 117

migrants as opposed to nonmigrants have a greater probability of formingpart of the nonresponse group and that this applies especially to singlemigrants In Denmark analysis of nonresponse patterns shows that there is ahigh level among foreigners12

The reweighting or poststratification procedure serves to correct the biasbrought about by nonresponse Correction bias requires the use of auxiliaryvariables related to the migration phenomenon in order to reestablish theweight or representation of the phenomenon in the population as a whole Theuse of this technique is naturally subject to the availability in each country ofa statistical source of control that provides updated auxiliary information Asshown in Table 3 there are no countries working with variables related to inter-nal migration and only four of the fifteen ndash Austria Germany the Netherlandsand Luxembourg ndash introduce the nationality variable into their poststratifica-tion criteria These are the only countries where bias in the estimation of thestock of foreigners is corrected In fact in these four countries (Table 2b) theestimation of the stock of foreigners through the nationality variable coincideswith the value given by censuses and registers

12Danmarks Statistik (2001)

Figure V Nonresponse Rates by Country

Source Table 3

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

118 I M R

The Problem of Answer Impossible The Question to Capture Migration Flows and the Sample Rotation Pattern

In the earlier sections we refer to problems common to the estimation of anydomain but when we estimate the migration phenomenon in terms of flowthere is an additional problem that increases imprecision (by reducing samplesize) and inevitably undervalues all migration estimations if no correctionfactors are introduced This problem comes from the question designed tocalculate migration flows in the LFS ldquoWhat was your place of residence one yearago rdquo The formulation of this item with a temporal limit of one year to asample in which the rotation scheme divides it into subsamples with differentparticipation durations leads to part of the sample never being able to give apositive response as they have been in the sample and by definition the sameplace of residence for over one year13

The frequency of the EU LFS and the refreshment patterns of the sur-veyed samples are controlled by community regulations14 In cases where thereis only one annual LFS it must be made in spring and at least a quarter of theSU must come from the previous yearrsquos survey In countries with monthly orquarterly LFSs community legislation makes no obligations with regard to thedetermination of rotation schemes Hence the dynamics of the sample panelare decided by each country

This means that in annual LFSs by law the individuals of 25 of thesample households can never answer that their countryregion of residence oneyear ago was different to the present this possibility is incompatible with theirsurvival in the sample This is only a minimum percentage so there is nothingto stop countries from establishing higher percentages and repetition rates asshown in columns (1) and (2) of Figure VI

From column (2) it can be seen that the proportion of the SU that is repeateda year later varies from the minimum in Belgium with no repetition to the 75of Germany In the case of countries with annual LFS sample refreshment thispercentage coincides exactly with that of the SU with answer impossible (column[3]) ie with those people that having remained in the sample over one yearcould not have migrated in the previous twelve months as in Germany andLuxembourg However for the countries that have established quarterly samplerotation schemes the calculation of the percentages of column (3) is more complex

13If a sample unit makes a migratory movement it generally stops being part of the LFS sampleas this survey does not ldquochaserdquo sample units14Council Regulation EEC No 57798

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 119

In national surveys designed with rotating sample panels an SU remainsin the sample over a limited number of interviews before being substituted Inthese cases a predetermined percentage of the sample is replaced every quarterafter each interview (column [1] of Figure VI) For this the full sample isdivided into subsamples or waves For example suppose that it is establishedthat an SU is interviewed on only six occasions this means that the sample isdivided into six and after each interview one-sixth are replaced by new unitsto refresh the sample In any given period a sixth of the sample is interviewedfor the final time another sixth for the fifth time etc In general whenthe retrospective question is made (in moment t) the sample is divided into ldquonrdquosubsamples or waves with a certain number of immigrants in each one distributedas shown in Figure VII

Before we continue and in order to simplify things we make the follow-ing assumptions15 Firstly that all individual interviews are made at a particularmoment for example on the first day of the reference period Secondly thatin each SU the human group is of only one person who is found and is suit-able for the survey Thirdly that these people make no migration movementsfrom the moment they join the sample group until the end of their period ofstay With the first assumption we standardize the treatment of the reference

15See Roacutedenas and Martiacute (1997)

Figure VI Sample Replacement and Migrants Not Counted by Country

EU Countries

Percentage and Frequency of Sample

Replacement (1)

Percentage of Sample Not Replaced

Annually (2)

Answer Impossible Rate (3)

Austria (AT) 125 quarterly 500 6875Belgium (B) 500 quarterly 00 00Denmark (DK) 333 quarterly 333 417Germany (D) 250 annually 750 750Greece (EL) 167 quarterly 333 583Spain (E) 167 quarterly 333 583France (F) 167 quarterly 333 583Ireland (IE) 200 quarterly 200 500Italy (I) 250 quarterly 500 5625Luxembourg (L) 750 annually 250 250Netherlands (NL) na na naPortugal (PT) 167 quarterly 333 583Finland (FI) 200 quarterly 400 550Sweden (SE) 125 quarterly 500 6875United Kingdom (UK) 200 quarterly 200 500

Source Table 3

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

120 I M R

periods with the second we simplify nonresponse and the treatment of thehuman group and we standardize the different SUs and with the third weretain migrants until the moment of the interview There is no reason for anyof these assumptions to be realistic but they control loss of sample information

These three assumptions relate to individual participation in the LFSsample and the following two assumptions control the type of mobility Hencefourthly multiple movements (various previous migrations made by the sameperson) are simplified with the imposition that each interviewed immigranthas only made one migration before the interview Finally with the fifthassumption we temporally restrict the possibility of ex-ante movement to theprevious year which is counted from the moment at which the person is firstinterviewed With these two assumptions we can divide the immigrants ofeach wave into four subgroups according to the quarter in which migrationtook place Each Mij represents the immigrants of wave ldquoirdquo with ldquojrdquo quartersfrom when they migrated to when they were captured by the survey and madetheir first interview

Following the above assumptions if we had a priori knowledge of all thesample immigrants of year t in the second quarter t(2)16 they could be distributedas in Figure VIII when the interviews are conducted in consecutive quartersand as in Figure IX when they alternate with rest periods as is the case inDenmark Italy and Finland

In Figure VIII we simultaneously show three different sample designsthat of Austria and Sweden of Greece Spain France and Portugal and finallythat of Ireland and the UK In Austria and Sweden the sample is divided intoeight waves and in each quarter an eighth of the sample is replaced in GreeceSpain France and Portugal there are six waves with a sixth of the samplebeing replaced every quarter and in Ireland and the UK there are five waves

16The information from the EU LFS is from the second quarter of each year

Figure VII Distribution of the Sample by Waves An N-Waves Example

Full Sample (n-waves)Subsample or

Wave (Wi)Subgroup of Migrants in the Subsample (Mi)

1n sample rarr First interview W1 M11n sample rarr Second interview W2 M21n sample rarr i interview Wi Mi1n sample rarr n interview Wn Mn

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 121

meaning that every quarter a fifth of the sample is replaced Hence if on thefirst day of the second quarter of year t respondents are questioned about theirplace of residence exactly one year previous the quantity would notbe counted as immigrants

and as immigrants there would be the figure

Figure VIII Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves

t-3(3) Austria and Sweden M84t-3(4) Greece Spain France and Portugal M74 M83

t-2(1) Ireland and United Kingdom M64 M73 M82t-2(2) M54 M63 M72 M81t-2(3) M44 M53 M62 M71 W8t-2(4) M34 M43 M52 M61 W7 W8t-1(1) M24 M33 M42 M51 W6 W7 W8

t-1(2) M14 M23 M32 M41 W5 W6 W7 W8t-1(3) M13 M22 M31 W4 W5 W6 W7 W8t-1(4) M12 M21 W3 W4 W5 W6 W7 W8t (1) M11 W2 W3 W4 W5 W6 W7 W8

t (2) W1 W2 W3 W4 W5 W6 W7 W8

Notes Subgroups of migrants counted Subgroups of migrants not counted

Figure IX Migrants by Wave and Quarter in Which They Move

Year (quarter) Waves Waves Waves

t-3(3) Denmark Italy Finlandt-3(4) M54t-2(1) M34 M44 M44 M53t-2(2) M33 M34 M43 M43 M52t-2(3) M32 M33 M42 M42 M51t-2(4) M31 M32 M41 M34 M41 W5t-1(1) M24 W3 M24 M31 W4 M24 M33 W4 W5

t-1(2) M14 M23 W3 M14 M23 W3 W4 M14 M23 M32 W4 W5t-1(3) M13 M22 M13 M22 W3 M13 M22 M31 W4t-1(4) M12 M21 M12 M21 M12 M21 W3t (1) M11 W2 M11 W2 W4 M11 W2 W3 W5

t (2) W1 W2 W3 W1 W2 W3 W4 W1 W2 W3 W4 W5

Notes Subgroups of migrants counted Subgroups of migrants not counted

Mno countt

( )2

M M M M Mno countt

jj jj i jji

n

( ) 2

2 4 33

442

4

1

4

5= + + += = ==sum sum sumsum

Mcountt ( )2

M M M M Mcountt

jj jj jj( )

211

421

33 4 11

2= + + += = =sum sum sum

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

122 I M R

In this way of the thirty-two initial immigrant subgroups in Austria andSweden only ten are captured and the remaining twenty-two will never becounted as immigrants in moment t(2) which under our original assumptionsrepresents 6875 of the sample immigrants In Greece Spain France andPortugal of the twenty-four subgroups fourteen would not be counted(5833) and in Ireland and the UK half of the twenty subgroups (50) arenot captured As we can see the larger the number of waves the greater the per-centage of sample individuals that can never answer that their place of resi-dence one year previous was different to the present

In Denmark Italy and Finland after various interviews the SUs are tem-porarily substituted and are later reincorporated into the sample To be precisein Denmark the sample is divided into three waves each being interviewedover two consecutive quarters and later after one year In Italy and Finland thesample is divided into four and five waves respectively with rest periods of twoquarters between the second and third surveys Therefore in Denmark(Figure IX) for example in moment t(2) a third of the sample are interviewedfor the first time another third for the second time and the remaining thirdafter having had a one-year rest period are interviewed for the third and final time

In the case of Denmark of a total of twelve subgroups of immigrants(three waves by four quarters) five can never answer that their place of resi-dence was different one year previous (417 of the sample immigrants) in thecase of Italy nine of the sixteen subgroups (5625) and finally in Finlandeleven from a total of twenty (55 of the sample immigrants) If we comparethe answer impossible percentages of Finland and the UK both with five LFSwaves we observe that the Finnish sampling pattern which includes restperiods raises the percentage even further

As with the case of the number of waves affecting the answer impossiblepercentage ndash more waves leading to a higher percentage ndash the inclusion of restquarters after interviews also affects the proportion of the sample that can nevergive a positive answer to the migration question Moreover also relevant is themoment at which a wave takes its rest period Therefore in Figure X we jointlyevaluate the effects of crossing the number of rest quarters (one two three ormore) after which the wave returns to the sample with the moment at whichthe rest period is taken (after the first second third fourth or later interview)Assuming samples with four or more waves each cell contains the migrant sub-groups that would not be captured if the pattern included rest periods accord-ing to when and for how long they are taken

It can be seen that rest periods immediately after the first interview areenormously detrimental to migration estimation and that the answer impossible

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 123

rate grows with the number of rest quarters However rest periods of over threequarters have no additional effects on the answer impossible rate Likewise thenegative effects of rest periods decrease the further away they move from thefirst interview in such a way that after the fourth or subsequent interviewintroducing rest periods has no additional effects on the answer impossible rateThis is obviously the case because there can no longer be any more timingproblems with the migration item Compared to continuous sampling patternsndash such as those of Figure VIII the additional answer impossible cases created byincorporating three or more rest quarters after the first interview raise thepercentage from 375 to 75 in cases with four waves and from 685 to87 in cases with eight waves17

Conclusions and Main Recommendations

The objective of the study is to evaluate whether the current EU LFS could bean alternative statistical source to national censuses and registers for theharmonized measurement of migration After analyzing consistency amongnational data from various sources we conclude that the degree of coincidenceamong them is only acceptable in some countries when measuring themigration variable in terms of stocks but not for migration flows

We think that this limited success is due to a twofold statistical problemof lack of precision and of bias These two serious hindrances are presentthroughout the Member States to a greater or lesser extent according to thecharacteristics of the migration domain and the particular features of the LFSin each country

17For samples with three waves in the worst case having a rest period of up to three quartersafter the first survey results in answer impossible accounting for 67 of the sample

Figure X Additional Migrant Subgroups with Answer Impossible According to Moment of Interview and Duration of Rest (samples with four or more waves)

After

Rest for

One Quarter Two Quarters Three or More Quarters

The First Interview M23 M32 M41 M22 M23 M31 M32 M41 M21 M22 M23 M31 M32 M41The Second Interview M32 M41 M31 M32 M41 M31 M32 M41The Third Interview M41 M41 M41The Fourth or

Subsequent Interview 0 0 0

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

124 I M R

The size of the migration domain in terms of flows ndash which except forinternal migration in the UK and Sweden does not rise above 7 percent of thepopulation ndash results in it being a minor or mini domain In these conditions ndashthe domain does not reach a sufficient size ndash we cannot guarantee the satisfactoryapplication of traditional sampling which is what happens in the Europeancase Therefore we believe that part of the lack of precision comes from thecombination of reduced domain size and the relatively small sampling fractionsof the national LFSs This imprecision is also brought about by the homoge-nous distribution of the migratory characteristic in households especially inpredominately Catholic countries

However if the root of the problem were only lack of precision we wouldexpect that by either taking various years for the same country or by takingvarious countries for the same year we would find that on some occasionsthe LFS data would coincide with that of censuses andor registers As this isnot verified we have to add the problem of bias in the LFS estimations ofmigration

The updating of the sampling frame and the inclusion or not of collectivehouseholds in the survey are also elements that bring about bias in measuringmigration both in terms of stocks and flows But there is no doubt thatnonresponse is the main source of bias There are few EU countries in whichparticipation in the LFS is compulsory which means that in the rest thepercentage of the sample presenting nonresponse is sufficiently large to thinkthat it could generate problems of bias in the estimation of migration as it ispositively correlated with this characteristic As this bias is not adequatelycorrected by the different national reweighting or poststratification proceduresthe LFS estimations are generally far removed from those of censuses andregisters However in countries that poststratify through the nationalityvariable (Austria Luxembourg Germany and the Netherlands) the estima-tion of stocks fits reasonably well even in the Netherlands where nonresponseis very high

Finally it has been shown that the migration question and the samplerotation pattern generate high answer impossible rates which has repercussionson both precision and bias in the estimations of migration flows Apart fromthe number of waves affecting the answer impossible rate ndash more waves leadingto a higher percentage ndash the inclusion of rest quarters after some interviews alsoaffects the proportion of the sample that can never give an affirmative answerto the migration question In the extreme case when there are rest periods afterthe first interview up to 87 of the respondents ndash in cases of eight waves ndash can-not answer the migration question A simple way to solve this problem is to

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

M E B L F S 125

only ask questions relative to the previous year on the first wave of the surveyand subsequently apply specific evaluation factors as with the Spanish LFS18

In summary we consider that the set of factors that differentiate the designof the national LFSs ndash such as sampling frame sampling fractions samplerotation patterns or stratification criteria ndash do not support the conclusion thatmigration data from this source is harmonized We should also bear in mind thedifferences that both domain size and homogeneity in the final sampling unitcan generate in each country We think that a greater amount of common practicein the procedures of the EU LFSs is worthless unless urgent attention is given tothe serious bias problems generated by nonresponse and its subsequent treatment

REFERENCES

Bell M P Rees and T Wilson 2003 ldquoComparing Internal Migration Between Countries Who Collects Whatrdquo Paper pre-

pared for the European Population Conference Warsaw Poland August Pp 26ndash30lthttpwwwgeospuqeduaucprdatabaseImdataImdatahtmgt

Central Statistics Office (CSO) and Office for National Statistics (ONS) 1998 ldquoIndicators of Migration between the Republic of Ireland and the United Kingdomrdquo

Eurostat Working Paper no E31998-1 Luxembourg European Community

Central Statistics Office (CSO) 2003 Quarterly National Household Survey 2nd Quarter 2003 Dublin CSO

Clark J et al 1998 ldquoDocumentation of Eurostatrsquos Database on International Migration Labour Datardquo

Eurostat Working Paper no E31998-16 Luxembourg European Community

Council Regulation (EEC) 1998 ldquoNo 57798 of 9 March 1998 on the Organisation of the EU Labour Force Surveyrdquo

Official Journal of the European Communities L77 14(3)

Council Regulation (EEC) 1989 ldquoNo 304489 of 6 October 1989 on the Organisation of a Labour Force Sample Survey

in the Spring of 1990 and 1991rdquo

Danmarks Statistik 2001 Internal Quality Report Standard Form for LFS Data Internal document

Eurostat 1998 Labour Force Survey Methods and Definitions Luxembourg European Communitiesmdashmdashmdash 2000 European Social Statistics Migration Luxembourg European Communities

18This possibility was proposed in Roacutedenas and Martiacute (1997170) to improve the estimation ofmigration in the Spanish LFS and has been applied by the National Institute of Statistics since1999 The result is a substantial increase in the figures reaching almost double the annual flowof internal migration

126 I M R

mdashmdashmdash 2003a The European Union Labour Force Survey Methods and Definitions ndash 2001 Luxembourg

European Communitiesmdashmdashmdash 2003b European Yearbook ndash 2003 Luxembourg European Communities

mdashmdashmdash 2003c New Cronos EU-LFS Database lthttpeurostatceceuintportalpage_pageid=1996

45323734amp_dad=portalamp_schema=PORTALampscreen=welcomerefampopean=ampproduct=EU_MAIN_TREEampdepth=1gt

Givord P2003 ldquoUne nouvelle Enquecircte Emploirdquo Eacuteconomie et Statistique INSEE No 362 Pp 59ndash66

Instituto Nacional Estadiacutestica 2002 Encuesta de Poblacioacuten Activa Informe teacutecnico Madrid

Institut National de Statistique 2003 La nouvelle enquecircte sur les forces de travail lthttpstatbelfgovbelgt

ISTAT 2003 Forze di lavoro Annuario 8 ISTAT lthttpwwwistatitgt

Martiacute M and C Roacutedenas2004 ldquoMigrantes y migraciones de nuevo la divergencia en las fuentes estadiacutesticasrdquo Estadiacutestica

Espantildeola 156(2)293ndash321

Mirza H and J Houmlrngren2002 ldquoThe Sampling and the Estimation Procedure in the Swedish Labour Force Surveyrdquo RampD

Report 20024 Sweden Statistics Sweden

Quatember A2002 ldquoA Comparison of the Five Labour Force Surveys of the DACSEIS Project from a

Sampling Theory Point of Viewrdquo DACSEIS Research Paper Series No 3 lthttpw210ubuni_tuebingendedbtvolltexte2002547pdfDRPS3pdfgt

OECD 2003a Sources and Comparability of Migration Statistics Statistical Annex lthttp

wwwoecdorgdocument3602340en_2825_494553_2515108_1_1_1_100htmlgt

mdashmdashmdash 2003b Migration Statistics lthttpwwwoecdorgdocument3602340en_2825_494553_

2515108_1_1_1_100htmlgt

mdashmdashmdash 2003c ldquoTable A23 Stocks of Foreign and Foreign-Born Labour Force in Selected

OECD Countriesrdquo lthttpwwwoecdorgdocument3602340en_2825_494553_2515108_1_1_1_100htmlgt

Office for National Statistics 2003 ldquoBackground amp Methodologyrdquo LFS User Guide vol 1

Purcell N and L Kish1979 ldquoEstimation for Small Domainsrdquo Biometrics 35365ndash384

Rees P and M Kupiszewski1999 ldquoInternal Migration What Data Are Available in Europerdquo Journal of Official Statistics

15(4)551ndash586

Roacutedenas C and M Martiacute1997 ldquoiquestSon bajos los flujos migratorios en Espantildeardquo Revista de Economiacutea Aplicada 5(15)155ndash171