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Combined use of data from registers and sample surveys
Eric Schulte Nordholt
Statistics Netherlands
Division Socio-economic and spatial statistics
Statistical Training Course on Use of Administrative Registers in Production of Statistics in Warsaw (October
2014)
2
Contents General
• Social Statistics
• System of social statistical datasets (SSD)
• Group work on registers and surveys
• The Dutch virtual census
• Time for questions and discussion
3
Contents Social Statistics
• Requirements for modern Social Statistics
• Driving forces
• Policy implications
• Life cycle model
• Relevant statistical information for policy and society
• Strategy for data collection
• Secondary data
• How to get consistency of different data sources?
• Prototype of a micro database
• Conclusions
4
Requirements for modern Social Statistics
Product quality (Eurostat Code of Practice):
1. Relevance
2. Accuracy
3. Timeliness and punctuality
4. Comparability and coherence
5. Accessibility and clarity
5
Driving Forces
More coherence, more thematic publications, more detail (small areas, population groups) and more flexibility in the statistical output (will lead to a better product)
ICT developments: more registers
High nonresponse rates in social surveys
To cut down processing costs: standardisation
To lower response burden: less questions, EDI (or EDC) and diminish ‘irritation factor’
6
Policy implications
• From primary to secondary data collection
– Wherever possible use data available in existing registers and other administrative sources
– Primary data collection only, if no (timely) data available (or of bad quality)
– Statistics Netherlands Act
• From traditional to electronic data collection
• Standardisation of statistical processes; multi-data-source statistics; efficient sampling
• Challenges must be faced while the available budget is constantly being reduced
7
Socialcapital
Labour marketposition
Income
Consumption
Housing
Time use … Well-being
Demography
Health
Education
Life cycle model (1)
Labour market position - Working/non working- Occupation- Economic activityDemography
- Year of birth- Nationality- Household composition- Etc.
8
Socialcapital
Labour marketposition
Income
Consumption
Housing
Time use … Well-being
Demography
Health
Education
Life cycle model (2)
9
T+2T+1
Cas
es
Variables
Time
T
Life cycle model (3)
10
Life cycle model (4)
11
• Transitions between states
• State
Life cycle model (5)
• Duration time in a certain state
Time
Analysis possibilities:
12
Life cycle model (6)
Time
13
Relevant statistical informationfor policy and society
• Domain specific
• Transitions and durations within a domain
• Relations between domains
• Relations between transitions and durations between domains
• Monitor information (long period)
14
Strategy for data collection (1)
• Start with registers (e.g. population register, housing register, business register)
• Add data from other administrative sources
• Add data from business and household surveys
• Match all these data at the micro level
• Create a ‘data clearing house’ within the statistical office
15
Surveys
Variables
Registers
All inhabitants N
etherlands
1
n
.
.
Strategy for data collection (2)
16
RINR
IN
Matching method for individual data
Longitudinal
Population Register
Administrative or
survey data
Strategy for data collection (3)
17
Secondary data (1)
Quality• Quality may be good for some basic registers,
but not for all registers; monitoring quality is important
• No sampling errors• No unit nonresponse• Many sources of non-sampling errors remain:
– Item nonresponse
– Measurement errors
– Coverage errors
18
Secondary data (2)
Challenges• Impact on the organisation, coordination,
crossing departmental boundaries, change in culture
• Influence of a statistical office on contents of registers is limited
• Communication with register holders, e.g. about quality and changes
• Quality control system (control surveys?)• Comprehensive, standardised metadata system• Version control system for updates• Changing form surveys to registers without
causing a trend break
19
How to get consistency of different data sources?
• Harmonisation! (coverage, definitions, reference periods, etc.)
• Editing of all records at micro level by automated procedures
• Only edit what needs to be edited (clear instructions are necessary!)
• Make use of the technique of repeated weighting for survey data
20
Prototype of a micro database (1)
LFS
HS
X1…XK Y1…YM Z1…ZR U1…US
21
Output inspired harmonisation: the one figure for one phenomenon idea
StatLine:all statistical information on the web(via home page of Statistics Netherlands)
http://www.cbs.nl/en-GB/menu/home/default.htm
Prototype of a micro database (2)
22
Conclusions
Social Statistics develop in the direction of a
permanent virtual census to be able to
produce: – More crosstables over different domains– More longitudinal information– More flexible policy relevant output
23
Contents System of social statistical datasets (SSD)
• Introduction to Statistics Netherlands
• Examples of registers
• Definition and driving forces of the SSD
• The scope of the SSD
• Core and satellites
• The process
• Linking the sources
• Micro integration
• Estimation aspects
• Statistical confidentiality
• Conclusions
24
Introduction to Statistics Netherlands (1)
The Central Statistical Office (CBS)• almost all official statistics in the Netherlands
• no regional offices
• two buildings: The Hague (in the West)
25
Introduction to Statistics Netherlands (2)and Heerlen (in the South); both have about 1000 employees
MissionThe mission of Statistics Netherlands is to publish reliable and coherent statistical information that meets the needs of society.
Position of the Statistical OfficeStatistics Netherlands is since 2004 a semi-independent organisation (still government funding) with about 2000 employees
26
Examples of registers
Three kinds of registers• Population Register (PR)• Job register• Self-employed register• Education register• Occupation register• Income register• Social security register• Unemployment register• Pension register• Other registers on persons, families and households• Housing register• Other registers on properties, buildings and dwellings• General business register• Other registers on enterprises and establishmentsCommon identifier: (numerical) address
27
Definition and driving forces of the SSD
Definition:set of integrated microdata files with coherent and detailed demographic and socio-economic data on persons, households, jobs and benefits
No remaining internal conflicting information
Driving forces:
• Virtual Census of 2001
• Better products: more coherence and flexibility
28
The scope of the SSD
All relevant variables in the life cycle • Demography• Health• Education• Labour market position• Income• Consumption• Housing• Time use• Etc.
29
SSD-core
satellite
sate
llite
sate
llite
satellitesatellite
sate
llite
satellite
satellite
Core and satellites (1)
30
Core and satellites (2)
Core:
• contains only integral register information
• contains the most important demographic and socio-economic information
• contains only information that is used in at least two satellites
31
Core and satellites (3)
Satellites are produced in two steps:
• Copying and derivation of the relevant information from the core SSD
• Adding of the unique information on a specific theme from registers and surveys
32
Core and satellites (4)
Examples of current SSD satellites:• Labour market• Social security• Income• Education• Health care• Justice and security • Ethnic minorities• Social cohesion
The development of more SSD-satellites has been planned
33
The process
Already discussed:– Specify the information needed– Collection of registers– Surveys only additional
Still to discuss:– Linking the sources– Micro integration– Estimation aspects– Statistical confidentiality
34
Linking the sources (1)
• The Population Register is the backbone of the system for persons
• All other files are matched exactly to the Population Register,
• such that the true matches are maximised (aim: no missed matches) and the false matches (mismatches) are minimised
35
Linking the sources (2)
Matching variables:
• Social security and fiscal (SOFI) number (effectiveness close to 100%), since 2007 Citizen Service Number
• Other personal identifiers: sex, date of birth, and address (effectiveness close to 100%)
• Number of mismatches very low (close to 0%)
36
Micro integration (1)
The aim of micro integration is:
– To check the linked data and modify incorrect records,
– in such a way that the results that are to be published are of higher quality than the original sources
37
Micro integration (2)
To fulfil this demand an integrated process of:
• data editing,
• derivation of statistical variables,
• and imputation
is executed
38
Micro integration (3)
Constraints and limitations:
- Only variables that are to be published are micro integrated
- Identity rules are necessary, e.g. the same variable in two sources or a relationship between two or more variables in one or more sources
- No mass imputation
39
Estimation aspects
Surveys are samples from the population
If surveys are enriched with register information, estimations of the register part of the enriched survey will lead to inconsistencies with the counts from the entire register
Statistics Netherlands developed the method of repeated weighting to solve these inconsistencies (aim: numerically consistent estimations)
40
Statistical confidentiality
IDs Variables
Characteristics
Identifiers (PINs, sex,date of birth, address)
PERSONS BACKBONEfull range of all persons as from 1995
Administrative sources
IDs Variables
Household surveys
IDs in sources are replaced by randomRecord Identification Numbers (RINs)
41
Conclusions
The SSD diminishes the administrative burden and increases:– The efficiency of statistics production– The accuracy of statistical outputs – The possibilities for social policy research
Safeguarding confidentiality is vital for the process of record linkage
42
Group work on registers and surveys (1)
Key question: which census variables are missing in all the registers? Consider the following thirteen census variables:
1.Sex
2.Age
3.Country of citizenship
4.Marital status
5.Household position
6.Religious denomination
7.Country of birth
8.Household size
43
Group work on registers and surveys (2)
9. Place of residence one year prior to the census
10.Economic status
11.Level of educational attainment
12.Occupation
13.Branch of current economic activity
A. Discuss the situation in the countries represented in your group or select some countries for further discussion
44
Group work on registers and surveys (3)
B. Are those missing variables available is any survey? Discuss where those surveys may be used (legal aspect and agreement with survey organiser) for producing official statisticsC. Can the surveys and registers be linked? Is this exact matching or is statistical matching necessary?
Are there other important issues that affect the overall situation?
45
Group work on registers and surveys (4)
D. Possibilities and limitations for further development of combining registers and surveys. What is the policy in the NSIs for further development? What are the possibilities and limitations for such a development?E. Prepare a short presentation (5 minutes per group)
46
Contents The Dutch virtual census (1)
• History of the Dutch Census
• The Dutch Census of 2011
• Data sources
• Combining sources: micro linkage
• Combining sources: micro integration
• Conditions facilitating use of administrative sources
• Miscellaneous aspects
• Census tables
• Micro macro method
• Result on 2011 economic activity
47
Contents The Dutch virtual census (2)
• Comparison with other countries
• Comparison with other years
• Harmonisation
• Microdata availability
• Data integration activities between the 2001 Census and the 2011 Census
• Preparing the 2011 Census
• Conclusions
48
History of the Dutch Census (1)
TRADITIONAL CENSUS
Ministry of Home Affairs:
1829, 1839, 1849, 1859, 1869, 1879 and 1889
Statistics Netherlands:
1899, 1909, 1920, 1930, 1947, 1960 and 1971
Unwillingness (nonresponse) and reduction expenses no more traditional censuses
49
History of the Dutch Census (2)
ALTERNATIVE: VIRTUAL CENSUS1981 and 1991: limited virtual censuses based on Population Register and surveys
development 90’s: more registers → integrated set of registers and surveys, SSD
2001 and 2011: complete virtual censuses based on the SSD with information at the municipality level
50
The Dutch Census of 2011
is based on the Social Statistical Database (SSD) which• is a set of integrated microdata files with coherent and detailed demographic and socio-economic data on persons, households, jobs and benefits• has no remaining internal conflicting information
is part of the European Census• Eurostat: coordinator of EU, accession and EFTA countries in the European Census Rounds• Census Table Programme, every 10 years
Social statistics in the Netherlands develop in the direction of a permanent Virtual Census to be able to produce: • More crosstables over different domains• More longitudinal information• More flexible policy relevant output
51
Data sources
Registers:• Population Register (PR) → illegal people excluded, homeless counted at last known address• Jobs file, containing all employees • Self-employed file, containing all self-employed• Fiscal administration• Social Security administrations• Pensions and life insurance benefits• Housing registers
Surveys:• Survey on Employment and Earnings (SEE) stopped• Labour Force Survey data around Census Day• Housing surveys no longer necessary for the Census
52
Combining sources: micro linkage
• Linkage key:Registers
Citizen Service Number, unique
Surveys Sex, date of birth, address (postal code and house number)
• Linkage key replaced by RIN-person
• Linkage strategyOptimizing number of matchesMinimizing number of mismatches and missed matches
53
Combining sources: micro integration
• Collecting data from several sources more comprehensive and coherent information on aspects of a person’s life
• Compare sources - coverage - conflicting information (reliability of sources)
• Integration rules - checks - adjustments - imputations
• Optimal use of information quality improves
• Example: job period vs. benefit period
54
Conditions facilitating use of administrative sources
• Legal base (Statistics Act)• Public approval (‘Big Brother is watching you’)• Cooperation among authorities (mainly
government organisations)• Comprehensive and reliable register system
(administrative versus statistical quality)• Unified identification system (preferably unique
ID-numbers)
55
Miscellaneous aspects (1)
• Stable identifiers
• Stability of registers
• Only edit what needs to be edited (by automated procedures)
• Dates of real events versus dates of registration
• Derived variables (example: current activity status)
• Impact on the organisation (change of culture)
• Communication with register holders
56
Output inspired harmonisation (coverage, definitions, reference periods): the one figure for one phenomenon idea
StatLine:all statistical information on the web(via home page of Statistics Netherlands)
http://www.cbs.nl/en-GB/menu/home/default.htm
Miscellaneous aspects (2)
57
Census tables (1)
Preliminary work before tabulating
Census Programme definitions: not always clear and unambiguous, e.g. economic activity
Priority rules• (characteristics of) main job (highest wage)• employee or employer• job or (partially) unemployed• job or attending education• job or retired• engaged in family duties or retired• age restrictions
Tabulating register variables: Simply straightforward counting from SSD register data
58
Census tables (2)
Tabulating survey (and register) variables
Mass imputation?•Pro’s: reproducible results •Con’s: danger of oddities in estimates (e.g. highly educated baby)
Traditional Weighting?•Pro’s: simple, reproducible results (if same microdata and
weights)•Con’s: no overall numerical consistency between survey
and register estimates
Demand for overall numerical consistency • one figure for one phenomenon idea• all tables based on different sources (e.g. surveys) should be mutually consistent
59
Census tables (3)Ethnicity: registerEducation: survey 1 and survey 2Employment status: survey 2Estimate: T1: educ x ethnic and T2: educ x employ
Survey 1
Survey 2
Register
ethnic1...k educLo...Hi employ1...m
educ x
ethnicnot-NL
NL Total
educLo 20 29 49
educHi 9 42 51
Total 29 71 100
employ
x educemployed non-
employedTotal
educLo 32 20 52
educHi 28 20 48
Total 60 40 100
Register Survey 1
Survey 2Survey 2
7030Total
NLnot-NL
ethnic
60
Census tables (4)
Repeated Weighting (RW) : tool to achieve numerical consistency (VRD-software)
Basic principles of RW:• estimate table on most reliable source (mostly source with most records, e.g. register)
• estimate tables by calibrating on common margins of the current table and tables already estimated (auxiliary information)
• repeatedly use of regression estimator: - initial weights (e.g. survey weights) calibrated as minimal as possible - lower variances - no excessive increase of (non-response) bias (as long as cell size>>0)
• each table has its own set of weights
61
Census tables (5)
Survey 1
Survey 2
Register
ethnic1...k educLo...Hi employ1...m
sam
plin
g un
its
Register Survey 1
Survey 2Survey 2educ x ethnic
not-NL
NL Total
educLo
educHi
Total
employ
x educemployed non-
employedTotal
educLo
educHi
Total
50
50
100
31 19
30 20
61 39
ethnic not-NL
NL
Total
30 70 100
20 30
10 40
ethnic not-NL
NL
Total 30 70
50
50
2
1
3
Calibrate on ethnic, then on educ x ethnic
62
Micro macro method (1)
Repeated Weighting works nicely, but in the 2011 Census a new requirement was introduced: hypercubes (= high dimensional tables)
Problem:Very detailed tables contain many sample zeros that RW cannot handle
Solution 1: estimate subhypercubesSolution 2: micro macro method (an IPF method) was introduced to estimate the interior of subhypercubes containing LFS variables
63
Micro macro method (2)
Results of the micro macro method are published if two conditions are fullfilled:1. table margins estimated with RW are small enough2. number of records in estmated cells are large enough
Criteria:1. estimated relative inaccuracy of at most 20 percent (i.e. the estimated margins amount to 40 percent at most) which corresponds to a threshold of 25 persons2. only table cells based on 5 or more persons are published
64
Result on 2011 economic activity
65
Comparison with other countries
Traditional Census (complete enumeration): Most countries in the world (including the UK and the US)Traditional Census (partial enumeration) and Registers: Some countries (e.g. Germany, Poland and Switzerland)Rolling Census: FranceFully or largely register-based (Virtual) Census: Five Nordic countries (Iceland,Norway, Sweden, Finland and Denmark), the Netherlands, Belgium, Austria and Slovenia
66
Comparison with other years
67
Harmonisation (1)
More information about the Dutch traditional Censuses (including those of 1960 and 1971):http://www.volkstellingen.nl/en/
For 1960 and 1971 the same variables as for 2001• if not available: constructed based on existing variables in Census data
Variables not internationally harmonised (e.g. sex, age, marital status, household position, country of birth, economic status, household size and country of citizenship)• same classification and priority rules as for 2001
68
Harmonisation (2)
Household size and country of citizenship:• missing for 1960
Religious denomination (philosophy of life):• only for 1960 and 1971
Place of residence one year prior to the census:• only for 2001
International classifications• Branch of current economic activity: ISIC / NACE• Occupation: ISCO• Level of educational attainment: ISCED
69
Harmonisation (3) 1960 1971 2001
Sex X X X
Age X X X
Country of citizenship X X
Marital status X X X
Household position X X X
Religious denomination X X
Country of birth X X X
Household size X X
Place of residence one year prior to the census
X
Economic status X X X
Level of educational attainment
X X X
Occupation X X X
Branch of current economic activity
X X X
70
Microdata availability
One percent samples for three years (1960, 1971 and 2001)IPUMS (Integrated Public Use Microdata Series):http://www.ipums.org/international/index.html
Weighting to population totals
Protecting according to rules for public use files
Microdata sets for all three years available for research!DANS (Data Archiving and Networked Services):http://www.dans.knaw.nl/en/
71
Data integration activities between the 2001 Census and the 2011 Census (1)
• Tables (http://www.cbs.nl/nl-NL/menu/themas/dossiers/historische-reeksen/publicaties/volkstelling-2001/2003-volkstelling-excel.htm)
• Book and extra chapter (http://www.cbs.nl/nl-NL/menu/themas/dossiers/historische-reeksen/publicaties/volkstelling-2001/2001-b57-pub.htm)
72
Data integration activities between the 2001 Census and the 2011 Census (2)• Integrated Public Use Microdata Series
(https://international.ipums.org/international)• Lectures (Conferences, Universities, Research
institutes, Statististical offices)• ESTP-course Registers in Statistics (Oslo)• International Statistical Seminar Eustat in Bilbao
(http://www.eustat.es/prodserv/seminario_i.html)• Digitalizing (http://www.volkstellingen.nl/en/)• Recommendations and register-based statistics• CENEX on ISAD (http://cenex-isad.istat.it)• European census regulations
73
Preparing the 2011 Census
• Sources (the PR as backbone of the census, changes in contents and quality of registers, remaining information from LFS)
• Estimation method (repeated weighting, new version of the software, fall-back option of weighting to PR, zero cells problem)
• Statistical Disclosure Control of the hypercubes (Workshop on SDC of Census Data in April 2012)
• Tabular data in SDMX format and the Census Hub
74
Conclusions (1)
• A Dutch Virtual Census: yes, we can!• Micro integration remains important• Repeated weighting was a success
Advantages:• Relatively cheap (small cost per inhabitant)• Quick (short production time)
Disadvantages:• Dependent on register holders (statistics is not their priority), timeliness of registers, concepts and population of registers may differ from what is needed (keep good relations with the register holders!)• Publication of small subpopulations sometimes difficult or even impossible because of limited information
75
Conclusions (2)
Other aspects:• Less attention for the results of a virtual census than for a traditional one• Difficult to keep knowledge and software up-to-date (Census is running every ten years)• Enormous international interest in virtual censuses• A lot of interesting census work in the coming years!
76
Time for questions and discussion