NCRM, Session 27, 1 July 20081 Handling data on occupations, educational qualifications, and ethnicity Paul Lambert & Vernon Gayle, Univ. Stirling Talk

NCRM, Session 27, 1 July 2008 1

Handling data on occupations, educational qualifications, and

ethnicity

Paul Lambert & Vernon Gayle, Univ. Stirling

Talk to the workshop ‘Resources for Data Management and Handling Social Science Data’

ESRC Research Methods Festival, Oxford, 1 July 2008


Handling variables

• DAMES project (www.dames.org.uk) - specialist data services on three major social science topics (occupations, education, ethnicity)

• ‘GE*DE’ – ‘Grid Enabled Specialist Data Environments’

• From: www.geode.stir.ac.uk

http://www.dames.org.uk/


Handing social science variables – general themes

• Common v’s best practice – Recording the derivation/variable construction process– Reviewing alternative measures

• Comparability (between contexts - countries, times) – Input or output harmonisation?– Measurement or functional equivalence?– See esp. ‘Variable constructions in longitudinal research’,

http://www.longitudinal.stir.ac.uk/variables/

– Existing standards of National Statistics Institutes and international bodies (during data collection)


Handling variables – general themes, ctd.

• The unit of analysis – Individual, spouse, household, etc. – Current time; career summary, etc.

• Concept and measures – Variety of academic preferences – NSI standard measures


Key variables: concepts and measures

Variable Concept Something useful Occupation Class; stratification;

unemploymentwww.geode.stir.ac.uk

Education Credentials; Ability; Merit

www.equalsoc.org/8

Ethnic group Ethnicity; race; religion; national origins

[Bosveld et al 2006]

Age Age; life course stage; cohort

Abbott, A. (2006) ‘Mobility: What, when, how?’, in Morgan et al., Mobility & Inequality, Stanford UP.

Gender Gender; household / family context

www.genet.ac.uk

Income Income; wealth; poverty; www.data-archive.ac.uk [SN 3909]


Key variables: comments & speculation

(from www.longitudinal.stir.ac.uk/variables/Coefficients.html )

a) Data manipulation skills and inertia

• I would speculate that around 80% of applications using key variables don’t consult literature and evaluate alternative measures, but choose the first convenient and/or accessible variable in the dataset Data supply decisions (‘what is on the archive version’) are critical

• Much of the explanation lies with lack of confidence in data manipulation / linking data

• Too many under-used resources – cf. www.esds.ac.uk


b) Software and key variables – a personal view

• Stata is the superior package for secondary survey data analysis:

• Advanced data management and data analysis functionality• Supports easy evaluation of alternative measures (e.g. est

store)• Culture of transparency of programming/data manipulation

• Problems with Stata• Not available to all users • {Slow estimation times}


c) Endogeneity and key variables

• ‘everything depends on everything else’ [Crouchley and Fligelstone 2004]

• We know a lot about simple properties of key variables– Key variables often change the main effects of other variables– Simple decisions about contrast categories can influence

interpretations – Interaction terms are often significant and influential

• We have only scratched the surface of understanding key variables in multivariate context and interpretation – Key variables are often endogenous (because they are ‘key’!)– Work on standards / techniques for multi-process systems

and/or comparing structural breaks involving key variables is attractive


d) Social science variables and functional form

Functional form = the way in which measures are arithmetically incorporated in quantitative analysis

With occupations, education, ethnicity, and elsewhere, we tend to be too willing to make simplifying categorisations

An alternative - scaling and relative positions – is better suited for complex analytical procedures


1. Data and research on occupations

• In the social sciences, occupation is seen as one of the most important things to know about a personDirect indicator of economic circumstancesProxy Indicator of ‘social class’ or ‘stratification’

• GEODE – how social scientists use data on occupations

• DAMES – extending GEODE resources • Expanding range• Improving usability

Stage 1 - Collecting Occupational Data (and making a mess)

Example 1: BHPS Occ description Employment status SOC-2000 EMPST

Miner (coal) Employee 8122 7

Police officer (Serg.) Supervisor 3312 6

Electrical engineer Employee 2123 7

Retail dealer (cars) Self-employed w/e 1234 2

Example 2: European Social Survey, parent’s dataOcc description SOC-2000 EMPST

Miner ?8122 ?6/7

Police officer ?3312 ?6/7

Engineer ?? ??

Self employed businessman ?? ?1/2


www.geode.stir.ac.uk/ougs.html

13

Occupations: we agree on what we should do: Preserve two levels of data

Source data: Occupational unit groups, employment status Social classifications and other outputs

Use transparent (published) methods [i.e. OIR’s] for classifying index units for translating index units into social classifications

for instance.. Bechhofer, F. 1969. 'Occupations' in Stacey, M. (ed.) Comparability in Social Research.

London: Heinemann. Jacoby, A. 1986. 'The Measurement of Social Class' Proceedings from the Social

Research Association seminar on "Measuring Employment Status and Social Class". London: Social Research Association.

Lambert, P.S. 2002. 'Handling Occupational Information'. Building Research Capacity 4: 9-12.

Rose, D. and Pevalin, D.J. 2003. 'A Researcher's Guide to the National Statistics Socio-economic Classification'. London: Sage.

14

…in practice we don’t keep to this...

Inconsistent preservation of source data• Alternative OUG schemes

• SOC-90; SOC-2000; ISCO; SOC-90 (my special version)

• Inconsistencies in other index factors • ‘employment status’; supervisory status; number of employees• Individual or household; current job or career

Inconsistent exploitation of Occupational Information• Numerous alternative occupational information files

• (time; country; format)• Substantive choices over social classifications

• Inconsistent translations to social classifications – ‘by file or by fiat’• Dynamic updates to occupational information resources • Strict security constraints on users’ micro-social survey data• Low uptake of existing occupational information resources


GEODE provides services to help social scientists deal with occupational information resources

1) disseminate, and access other, Occupational Information Resources

2) Link together their (secure) micro-data with OIR’s

External user

(micro-social data)

Occ info (index file) (aggregate)

User’s output

(micro-social data)

id oug sex . oug CS-M CS-F EGP id oug CS

1 110 1 . 110 60 58 I 1 110 60 .

2 320 1 . 320 69 71 II 2 320 69 .

3 320 2 . 874 39 51 VIIa 3 320 71 .

4 874 1 . 4 874 39 .

5 874 2 . 5 874 51 .


Occupational information resources: small electronic files about OUGs…

Index units # distinct files (average size kb)

Updates?

CAMSIS, www.camsis.stir.ac.uk

Local OUG*(e.s.)

200 (100) y

CAMSIS value labelswww.camsis.stir.ac.uk

Local OUG 50 (50) n

ISEI tools, home.fsw.vu.nl/~ganzeboom

Int. OUG 20 (50) y

E-Sec matrices www.iser.essex.ac.uk/esec

Int. OUG*(e.s.)

20 (200) n

Hakim gender seg codes (Hakim 1998)

Local OUG 2 (paper) n


For example: ISCO-88 Skill levels classification


and: UK 1980 CAMSIS scales and CAMCON classes


Summary on occupations and data management

• Extensive debate about occupation-based social classifications • Document your procedures.. • ..as you may be asked to do something different..

• If you need to choose between occupation-based measures…– They all measure, mostly, the same things – Don’t assume concepts measure measures

• Lambert, P. S., & Bihagen, E. (2007). Concepts and Measures: Empirical evidence on the interpretation of ESeC and other occupation-based social classifications. Paper presented at the ISA RC28 conference, Montreal (14-17 August), www.camsis.stir.ac.uk/stratif/archive/lambert_bihagen_2007_version1.pdf .


Men and Women (categorical social classifications)

0.1

.2.3

.4.5

.6.7

.8.9

1R

or

pseu

do-R

ES5

E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Promotion / retention Pay - bonus / increments Hours and level of monitoring

Labour contract type Subjective skill requirements

Men and Women (metric social classifications)

0.1

.2.3

.4.5

.6.7

.8.9

1R

or

pseu

do-R

CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN

Britain Sweden

(2.6) Associations - Employment Relations and Conditions


-.01

.01

.03

.05

.07

.09

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CGISEI

SIOPAWM

WG3GN

Pseudo R-squared Increase in BIC

Britain, Males

-.06

-.04

-.02

0.0

2.0

4.0

6

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CM2CF2

ISEI

SIOPAWM

WG1WG2

GN

Sweden, Males

(3.4a) R-2 and BIC for predicted unemployment risk


July 2008: Existing resources on occupations

Popular websites: • http://www2.warwick.ac.uk/fac/soc/ier/publications/software/cascot/ • http://home.fsw.vu.nl/~ganzeboom/pisa/ • www.iser.essex.ac.uk/esec/ • www.camsis.stir.ac.uk/occunits/distribution.html

Emerging resource: http://www.geode.stir.ac.uk/

Some papers: – Chan, T. W., & Goldthorpe, J. H. (2007). Class and Status: The Conceptual

Distinction and its Empirical Relevance. American Sociological Review, 72, 512-532.

– Rose, D., & Harrison, E. (2007). The European Socio-economic Classification: A New Social Class Scheme for Comparative European Research. European Societies, 9(3), 459-490.

– Lambert, P. S., Tan, K. L. L., Gayle, V., Prandy, K., & Bergman, M. M. (2008). The importance of specificity in occupation-based social classifications. International Journal of Sociology and Social Policy, 28(5/6), 179-192.

http://home.fsw.vu.nl/~ganzeboom/pisa/


Using data on occupations – further speculation

• Growing interest in longitudinal analysis and use of longitudinal summary data on occupations

• Intuitive measures (e.g. ever in Class I) Lampard, R. (2007). Is Social Mobility an Echo of Educational Mobility?

Sociological Research Online, 12(5).

• Empirical career trajectories / sequences Halpin, B., & Chan, T. W. (1998). Class Careers as Sequences. European

Sociological Review, 14(2), 111-130.

• Growing cross-national comparisons– Ganzeboom, H. B. G. (2005). On the Cost of Being Crude: A Comparison of

Detailed and Coarse Occupational Coding. In J. H. P. Hoffmeyer-Zlotnick & J. Harkness (Eds.), Methodological Aspects in Cross-National Research (pp. 241-257). Mannheim: ZUMA, Nachrichten Spezial.

• Treatment of the non-working populations• Seldom adequate to treat non-working as a category• ‘Selection modelling’ approaches expanding


2. Data and research on education

• Although there have been standardisation attempts, data on an individual’s level of education is notoriously difficult to collect and compare between studies

• Between countries• Between regions• Between time periods • Even between short time periods (Example of the

UK Youth Cohort Study)


In international research..

There are two leading standards

• ISCED www.unesco.org/education/information/nfsunesco/doc/isced_1997.htm

• CASMIN education http://www.equalsoc.org/publications/show/40

– But not all researchers adopt them, or are satisfied with them when they do

http://www.unesco.org/education/information/nfsunesco/doc/isced_1997.htm

http://www.equalsoc.org/publications/show/40


In UK research..

• There are some recommended standard data collection schemes…

• Simplified measure (‘other primary standard’) at: www.statistics.gov.uk/about/data/harmonisation/

• ..but many studies build up unstandardised data on highest levels of qualifications

• Often hundreds of unique qualification titles• Little standardisation on relative levels• Many surveys collect multiple response data (multiple

qualifications held by an individual)


BHPS exampleCount

323 0 0 0 0 323

982 0 0 0 0 982

0 425 0 0 0 425

0 1597 0 0 0 1597

0 0 340 0 0 340

0 0 3434 0 0 3434

0 0 161 0 0 161

0 0 0 1811 0 1811

0 0 0 0 2518 2518

0 0 0 331 0 331

0 0 0 0 421 421

0 0 0 257 0 257

102 0 0 0 0 102

0 0 0 0 2787 2787

138 0 0 0 0 138

1545 2022 3935 2399 5726 15627

-9 Missing or wild

-7 Proxy respondent

1 Higher Degree

2 First Degree

3 Teaching QF

4 Other Higher QF

5 Nursing QF

6 GCE A Levels

7 GCE O Levels or Equiv

8 Commercial QF, No OLevels

9 CSE Grade 2-5,ScotGrade 4-5

10 Apprenticeship

11 Other QF

12 No QF

13 Still At School No QF

Highesteducationalqualification

Total

-9.001.00

Degree2.00

Diploma

3.00 Higherschool orvocational

4.00 Schoollevel orbelow

educ4

Total


Family and Working Lives Survey (54 vars per educ record)

29

Data on education levels cf. occupations

Underlying qualification units• There are few obvious ‘educational unit groups’• There are many publicly defined alternative schemes

Manipulation of educational data • Few published ‘educational information resources’ • Many open-access sources of data about educational

qualifications – e.g. national statistics website reports

• There has been less previous recognition of value of standardisation – Though this is emerging in comparative research

• Educational data is dynamic and rapidly expanding


Educational data and cohort change

• A critical consideration concerns cohort change in educational qualifications and distributions

• Appreciating relative value of education level given context

• Multivariate analytical procedures• Mean benefit of education within cohort?


Summary on education and data management

• We should document measures because.. • Some way away from agreeing on preferred measures• Dynamic nature of educational distributions• Debate between categorisers and scorers…

• Some useful resources: • Schneider, Silke L. (ed.) (2008), The International Standard

Classification of Education (ISCED-97). An Evaluation of Content and Criterion Validity for 15 European Countries. Mannheim: MZES. ISBN 978-3-00-024388-2

• ISMF educational databases and recodes: http://home.fsw.vu.nl/hbg.ganzeboom/ISMF/ismf.htm


3. Data and research on ethnicity

• Rapid growth in social science interest, and data, on ‘ethnic minority groups’, ‘immigration’, ‘immigrants’

• Data includes: – Generic & specialist studies collecting ethnic ‘referents’ ‘ethnic identity’; ‘nationality’, parents’ nationality; country of birth;

language spoken; religion; ‘race’

• National research and data management: – Most countries have evolving standard definitions of ethnic groups

• International research and data management – Seen as highly problematic in many fields except immigration data– Lambert, P.S. (2005). Ethnicity and the Comparative Analysis of

Contemporary Survey Data. In J. H. P. Hoffmeyer-Zlotnick & J. Harkness (Eds.), Methodological Aspects in Cross-National Research (pp. 259-277). Manheim: ZUMA-Nachrichten Spezial 11.


Ethnic group in the World Values Survey - Britain

Count

18 0 0 0 18

0 1484 0 999 2483

0 0 1 0 1

15 0 0 0 15

1 0 0 0 1

0 0 3 0 3

0 0 11 0 11

0 0 1 0 1

0 0 4 0 4

0 0 12 0 12

9 0 2 0 11

0 0 7 0 7

1124 0 1044 0 2168

0 0 8 0 8

1167 1484 1093 999 4743

-5 Missing; Unknown

-4 Not asked

-1 Don´t know

40 Asian

70 Asian - Central (Arabic)

80 Asian - East (Chinese,Japanese)

90 Asian - South (Indian,Hindu, Pakistani,Bangladeshi)

130 Bangladeshi

200 Black African

210 Black-Caribbean

220 Black-Other / Black

810 Pakistani

1400 White / CaucasianWhite

8000 Other

Total

1981-1984 1989-1993 1994-1999 1999-2004

Wave

Total


Ethnic group in the World Values Survey - Mexico

Count

0 1 0 1

0 0 29 29

0 832 0 832

0 364 0 364

5 8 0 13

0 84 0 84

7 14 3 24

544 0 0 544

240 0 564 804

346 0 648 994

86 0 0 86

0 0 25 25

303 335 254 892

0 685 12 697

1531 2323 1535 5389

-5 Missing; Unknown

-2 No answer

-1 Don´t know

70 Asian - Central (Arabic)

80 Asian - East (Chinese,Japanese)

90 Asian - South (Indian, Hindu,Pakistani, Bangladeshi)

220 Black-Other / Black

310 Coloured (medium)

320 Coloured (dark)

330 Coloured (light)

630 Indian (American)

640 Indigenous

1400 White / Caucasian White

8000 Other

Total

1989-1993 1994-1999 1999-2004

Wave

Total


UK: ONS & ESDS data guides• Input harmonisation within decades

• Output harmonisation between decades• Bosveld, K., Connolly, H., & Rendall, M. S. (2006).

A guide to comparing 1991 and 2001 Census ethnic group data. London: Office for National Statistics.

– Academic strategies – ad hoc ‘black’ group, etc

– Addition of extra categories over time

– Mixed ethnicities, marriages…

• UK Focus on ‘ethnic identity’, lack of attention to alternative referents


Comparative research solutions?

• Measurement equivalence might be achieved by:

• Survey data collection • Connecting related groups• Longitudinal linkage

• Functional equivalence for categories: • Simplified categorical distinctions • Immigrant cohorts • Scaling ethnic categories


Ethnicity and the DAMES project

• Hard subject to collate information on Few recognisable ‘ethnic unit groups’ Limited previous ‘data management’ reflection Very few published databases on ethnicity Important question of sparse distributionsDynamic, & rapidly expanding

• Likely role is to give new guidance on emerging strategies for analysing and exploiting data


Concluding summary: Handling data on occupations, educational qualifications

and ethnicity

Principles for data management: 1) Keep clear records

– Recodes and transformations

2) Use existing standards3) Do something, not nothing

– Distributional differences by cohorts

4) Learn how to match files − Exploiting wider resources / other research

Documents

NCRM, Session 27, 1 July 20081 Handling data on occupations, educational qualifications, and ethnicity Paul Lambert & Vernon Gayle, Univ. Stirling Talk