Challenges and strategies when exploiting data on ethnicity from

social survey datasets

Paul Lambert, University of Stirling

Talk presented to the NCRM seminar ‘What is ethnicity? What methods best capture it?’, part of the NCRM series ‘’Promoting methodological innovation and capacity building in research on ethnicity’, University of Essex, 14 th May 2010.

This work draws upon materials from the GEMDE project, a component of DAMES (www.dames.org.uk), an ESRC funded research Node working on ‘Data

Management through e-Social Science’

Summary of claims

1) Well known challenges exploiting survey measures of ethnicity

..our response is usually too conservative..

2) Better ‘data management’ could/should allow us to get much more from data

Take account of more precise ethnic differencesLongitudinal/cross-national comparisonsComplex multivariate models, interaction effects

3) We have something to offer here: ‘GEMDE’

2

…why is working with ethnicity data in surveys so hard…?

- It’s sparse - It’s collinear (e.g. to age)

- It’s dynamic (cf. comparative research)

3

4

Data includes: Generic & specialist studies collecting ethnic ‘referents’

‘ethnic identity’; nationality, parents’ nationality; country of birth; language spoken; religion; ‘race’

National research: Most countries have evolving standard definitions of ethnic groups,

though not all surveys follow them• Some surveys cover large numbers from many/all groups• Most surveys only have sparse representation of most groups

Comparative research (international/longitudinal) : Seen as highly problematic in many fields except immigration studies

o 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.

5

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

He said that ‘our response is usually too conservative’? I’m not conservative!

Social theory is dynamic, fluid, ‘intersectional’, but representative empirical analyses struggles to engage with its terms

Empirical studies are bivariate; descriptive; use low numbers of groups & normalising assumptions

This is ‘conservative’ because..o Administrative pressure to reify descriptive groups o Analyses simplify, or ignore, rather than incorporate, extra

information on ethnic locations (e.g. language, religion)o Analytical results tend to be easily anticipated (basic

descriptions, ignoring complex collinear contexts)6

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2) Data management for categorical data

Principal social survey datumo Basis of most social research reports/analyses/comparisons

It’s rich and complex o We’re often interested in very fine levels of detail / differenceo We usually recode categories in some way for analysis

…how categorical data is managed is of great consequence to the results of analysis…Choices about recoding, boundaries, contrasts made[e.g. RAE analysis: Lambert & Gayle 2009]

Management itself influences analytical approaches

8

EFFNATIS sample (1999): Subjective ethnic identity

30. English, White, Cosmopolitan (2,10, 1 0.12 86.0329. English, European and White-British 20 2.43 85.91 28. European and White-British (11,13) 7 0.85 83.48 27. White-Italian 1 0.12 82.62 26. English & White-British (2,13) 8 0.97 82.5025. White-British & Cosmopolitan (13,16 5 0.61 81.53 24. English, White-British (2,13) 41 4.98 80.92 23. English & White (2,10) 54 6.56 75.9422. Indian-British, Asian-British (7,15 9 1.09 69.38 21. Indian & British (1,4) 1 0.12 68.29 19. British Moslem 6 0.73 68.17 18. Indian, Asian (4,9) 2 0.24 67.44 17. Moslem 12 1.46 67.19 16. Cosmopolitan 7 0.85 65.74 15. Asian-British 65 7.90 64.88 13. White-British 60 7.29 56.99 11. European 6 0.73 49.70 10. White 7 0.85 48.97 9. Asian 6 0.73 48.12 8. Bangladeshi-British 22 2.67 47.39 7. Indian-British 34 4.13 44.71 6. Pakistani-British 73 8.87 40.58 5. Bangladeshi 10 1.22 31.71 4. Indian 10 1.22 30.50 3. Pakistani 25 3.04 29.28 2. English 79 9.60 26.25 1. British 137 16.65 16.65 yourself? Freq. Percent Cum. describes how you would describe Q.129 Which of the following best

90. White-British, Ukrainian 1 0.12 98.91 89. Indian, Asian-British (4,15) 1 0.12 98.7888. Indian, Black, Asian-British (4,12, 1 0.12 98.6687. Indian-British, Black-British (7,14 1 0.12 98.54 86. English, Indian (2,4) 4 0.49 98.42 85. European, White-British, Irish 1 0.12 97.93 83. Pakistani-British, Italian 1 0.12 97.8182. English, Pakistani-British, Asian ( 1 0.12 97.69 80. Human being 2 0.24 97.5779. English, Indian-British, White, Asi 1 0.12 97.33 78. Pakistani-British, Asian, Moslem 1 0.12 97.21 77. English, Pakistani (2,3) 3 0.36 97.08 73. British with Baltic-Slav origins 1 0.12 96.7272. English, White-British, Cosmopolita 2 0.24 96.60 71. Irish-English 1 0.12 96.35 70. English, White, European (2,10,11) 3 0.36 96.23 69. English, White, Irish 1 0.12 95.8768. Pakistani, Asian-British, Moslem, F 1 0.12 95.75 67. Pakistani, Asian-British (3,15) 1 0.12 95.63 66. Humanoid 1 0.12 95.50 65. Lancastrian 3 0.36 95.38 64. British, European (1,11) 2 0.24 95.02 63. Bangladeshi, Asian, Black (5,9,12) 1 0.12 94.7862. Pakistani-British, Black, Asian-Bri 2 0.24 94.65 61. English, Pakistani-British (2,6) 2 0.24 94.4160. British, White, European, Cosmopoli 1 0.12 94.1759. English, European, White-British, C 3 0.36 94.05 58. Individual 2 0.24 93.68 56. English, European (2,11) 2 0.24 93.44 55. Scottish 1 0.12 93.20 54. British, White (1,10) 2 0.24 93.07 53. British, English (1,2) 9 1.09 92.8352. English, Indian, Asian-British (2,4 1 0.12 91.7451. Pakistani-British, Indian-British ( 1 0.12 91.62 50. English, Asian-British (2,15) 2 0.24 91.49 49. English, Indian-British (2,7) 2 0.24 91.2548. English, Indian-British, Black, Asi 2 0.24 91.01 47. Indian-British, Asian (7,9) 5 0.61 90.7746. Indian, Asian, Asian-British (4,9,1 1 0.12 90.16 45. Neapolitan 1 0.12 90.0444. English, Pakistani-British, Indian- 2 0.24 89.9143. English, Indian-British, Asian-Brit 1 0.12 89.67 42. Pakistani-British, Asian (6,9) 6 0.73 89.55 41. Black, Asian-British (12,15) 2 0.24 88.8240. Pakistani-British, Asian-British (6 2 0.24 88.58 39. Pakistani-born British 1 0.12 88.34 38. Pakistani, Black (3,12) 1 0.12 88.2137. Pakistani-British, Asian, Black-Bri 1 0.12 88.0936. Pakistani-British, Black-British, A 1 0.12 87.9735. Pakistani-Moslem living in Great Br 1 0.12 87.85 34. Kashmiri 4 0.49 87.73 33. Pakistani, Asian (3,9) 3 0.36 87.2432. Pakistani-British, Asian-British (6 6 0.73 86.8831. English, Indian-British, Asian-Brit 1 0.12 86.15

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UK EFFNATIS survey (1999)

[Heckmann et al 2001];

[Penn & Lambert 2009]

79. Islamic 0 0 1 0 1 78. Dramatic 0 0 4 0 4 77. Bollywood 0 0 1 0 1 75. Ambient 1 0 0 0 1 72. Lo-fi 1 0 0 0 1 68. Urdu 0 2 0 0 2 65. Hindi 0 1 2 0 3 64. Classical Hindi 0 1 1 0 2 63. English 0 2 0 0 2 60. Dance Floor 2 0 0 0 2 51. Goth 1 0 0 0 1 50. Break Beat 1 1 0 0 2 48. Swing 0 1 0 0 1 47. Blues 1 0 0 0 1 46. Love songs / ball 0 2 0 0 2 45. Rock 'n' Roll 1 0 0 0 1 44. Soft Rock 2 1 0 0 3 43. Alternative 2 0 0 0 2 41. Drum 'n' Bass 3 0 0 0 3 40. All 19 5 2 0 26 38. Asian Pop Music 0 2 0 0 2 37. Jungle 1 0 0 0 1 36. Bangra 0 5 2 0 7 35. Indian 0 11 3 1 15 31. Punk 3 0 0 0 3 30. Country 2 0 0 0 2 28. Reggae 0 3 2 1 6 27. Easy Listening 3 0 0 0 3 26. Grunge 1 0 0 0 1 24. Eighties 2 0 0 0 2 23. Seventies 3 0 0 0 3 22. Sixties 4 0 0 0 4 21. Classical 4 2 1 1 8 20. Jazz 2 1 1 0 4 18. Folk 1 0 0 0 1 17. Motown 1 0 0 0 1 16. Rock / Heavy Meta 32 2 2 0 36 15. Indie / Britpop 46 4 3 1 54 14. Rap 4 11 2 1 18 13. Acid Jazz 1 0 0 0 1 12. Hardcore 3 0 0 0 3 11. Garage 0 2 1 0 3 10. Techno 1 0 0 0 1 9. House 18 0 1 0 19 8. Trance 0 0 1 0 1 7. Asian Music 0 11 12 2 25 6. Hip Hop 5 6 2 0 13 5. R'n'B 19 16 5 5 45 4. Soul 8 31 10 3 52 3. Dance 94 5 9 1 109 2. Chart 27 2 0 0 29 1. Pop 107 32 20 4 163 music1b 1. Autoch 2. Pakist 3. Indian 4. Bangla Total

Total 97 601 698 4. Bangladeshi 6 14 20 3. Indian 32 56 88 2. Pakistani 58 105 163 1. Autochthonous 1 426 427 uketh2 1. Ethnic 2. No EM Total music, by ethnicity Favourite type of

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A ‘data management’ contribution?

Preserve information on what was done with categorical data Communicate information on what should/could be done

11

Standardizing categorical data

‘Standardization’ refers to treating variables for the purposes of analysis, in order to aid comparison between variables

o {In the terminology of survey research analysts}

1. Arithmetic standardization to re-scale metric values [zi = (xi – x) / sd]

2. Ex-ante or Ex-post harmonisation [during data production, or adaptation after the event]

3. Measurement or Meaning/Functional equivalence [Much comparative research flounders on the apparent impossibility of measurement equivalence and lack of options for functional equivalence, e.g. Van Deth, 2003]

‘One size doesn’t fit all so we can’t go on’

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Meaning equivalence

For categorical data, equivalence for comparisons is often best approached in terms of meaning equivalence

(because of non-linear relations between categories and shifting underlying distributions)

(even if measurement equivalence seems possible)

Arithmetic standardisation offers a convenient form of meaning equivalence by indicating relative position with the structure defined by the current context

For categorical data, this can be achieved/approximated by scaling categories in one or more dimension of difference

13

Managers and Administrators

Professional

Associate professional and technical

Clerical and secretarial

Craft and related

Personal and protective servicesSales

Plant and machine operativesOther occupations

.

higher degree

first degree

teaching qf

other higher qf

nursing qf

gce a levels

gce o levels or equiv

commercial qf, no o levels

cse grade 2-5,scot grade 4-5apprenticeship

other qf

no qf

.white

black-carib

black-african

black-other

indianpakistani

bangladeshi

chinese

other ethnic grp

2030

4050

0 1 2 3Source: British Household Panel Survey 2007, adults aged 18+ and father's Cambridge Scale score.Points at 1-3 show category mean. Points at 0 show individual values (scaled mean=28, sd=6; pop. mean=28, sd=18).

‘Effect proportional scaling’ using parents’ occupational advantage

What was that then?

We can represent categories through positions on a scale In turn, we can use position in the dimension as a category

score which then plugs into a further analysis (e.g. regression main and interaction effects)

..Some options for data on ethnicity.. Stereotyped Ordered Logistic Regression (SOR) models, summarize

dimensions of difference according to regression predictor values

[e.g. Lambert and Penn, 2001]

Geometric data analysis for distances between people, or things [cf. Prandy, 1979; Bennett et al., 2009]

Assign category scores by hand (a priori or by selected average)

14

15

WhB-a

WhO-b

Ind-c

WhB-c

PkB-a

WhO-c

WhI-c

WhB-b

WhO-a

Oth-b

BA-b

BC-b

Chi-bPkB-bBC-a

BC-c

Ind-b

Chi-c

Oth-a

PkB-c

BA-a

WhI-a

Ind-a

WhI-b

Oth-c

BA-cChi-a

I/II

IIIaIVabcV/VIVII/IIIb

UnemployedInactive

-2-1

01

2D

imen

sion

2 (

22.

1%)

-2.5 -2 -1.5 -1 -.5 0 .5Dimension 1 (58.4%)

a = Born in UK; b = Came to UK before 1970; c = came to UK 1970 or laterN=640295 (Data: Li and Heath, 2008)

LFS pooled data for men, 1991-2005Correspondence analysis dimension scores

UK born Not UK born

Whi Iri 0 (base)

Whi Other -5.4 -3.4

Black Car. -9.6 -1.0

Black Afr -12.7 -5.9

Indian -16.1 -1.0

Pak/Bang. -16.8 -3.2

Chinese -15.8 -6.5

Other -11.0 -3.916

educ6_6 -.2346746 .0869549 -2.70 0.007 -.4051031 -.0642461 educ6_5 -.3082588 .1122945 -2.75 0.006 -.528352 -.0881655 educ6_4 -.2971853 .1083325 -2.74 0.006 -.5095131 -.0848574 educ6_3 -.4110193 .1492615 -2.75 0.006 -.7035665 -.1184722 educ6_1 -.0771515 .0341444 -2.26 0.024 -.1440733 -.0102298 yob -.0298194 .0107592 -2.77 0.006 -.0509071 -.0087317 ethim2 Coef. Std. Err. z P>|z| [95% Conf. Interval] ( 1) [phi1_1]_cons = 1

Log likelihood = -47960.017 Prob > chi2 = 0.2616 Wald chi2(6) = 7.69Stereotype logistic regression Number of obs = 21999

Is scaling useful?

..sometimes.. Intrinsically revealing as an exploratory exercise

Parsimonious functional form in explanatory modelling

o Esp. if ethnicity is a control variableo If interaction effects are consideredo If a story of a linear functional form is persuasive (e.g.

exponential increase)

17

Predicting poor subjective health, BHPS w15

18

legend: * p<0.05; ** p<0.01; *** p<0.001 bic 32364 32409 32468 32219 32363 32362 32485 ll -16154 -16139 -16131 -16072 -16148 -16143 -16134 r2 .071 .073 .074 .073 .072 .072 .074 N 12294 12294 12294 12239 12294 12294 12294 _cons 1.9*** 1.9*** 1.9*** 1.9*** 1.9*** 1.7*** 1.9*** _Iethim_29 .36* _Iethim_28 -.064 _Iethim_27 .96** _Iethim_26 .27 _Iethim_25 .32** _Iethim_24 .65 _Iethim_23 -.023 _Iethim_22 .66** _Iethim_21 -.057 _Iethim_19 .34 _Iethim_18 -.055 _Iethim_17 .44 _Iethim_16 -.066 _Iethim_15 .29 _Iethim_14 .044 _Iethim_13 .074 _Iethim_12 .0048 ethsa -.0011** ethscore -.018** .03 _Irace2_3 .26*** _Irace2_2 .19 _IracXoage_9 -5.2e-05 _IracXoage_8 -.0054 _IracXoage_7 .057** _IracXoage_6 .017 _IracXoage_5 .0057 _IracXoage_4 .022 _IracXoage_3 -.0029 _IracXoage_2 .016 _Irace_9 .35** .35 _Irace_8 -.06 .18 _Irace_7 .83** -1.1 _Irace_6 .14 -.54 _Irace_5 .31** .064 _Irace_4 .18 -.6 _Irace_3 .023 .14 _Irace_2 .29 -.42 educ4_4 .31*** .3*** .3*** .3*** .3*** .3*** .3*** educ4_2 -.1*** -.1*** -.1*** -.1*** -.1*** -.1*** -.1*** educ4_1 -.24*** -.25*** -.25*** -.25*** -.25*** -.25*** -.25*** oage .0062*** .0063*** .0062*** .0063*** .0062*** .011*** .0063*** fem .075*** .077*** .077*** .079*** .075*** .075*** .077*** Variable m1 m2 m2b m3 m5 m5b m4

19

What we do and what we ought to do

Research applications tend to select a single simplifying collinear categorisation of a concept Due to coordinated instructions [e.g. Blossfeld et al. 2006] Due to perceived lack of available alternatives Due to perceived convenience

To make statistical analyses more robust we should… Operationalise and deploy various scalings and arithmetic

measures Try out various categorisations and explore their distributional

properties … and keep a replicable trail of all these activities..

3) Some contributions from DAMES

3 themes in DAMES ought, in our perspective, to help here

1)Replicability / transparency

2)Plurality of approaches

3)Ease of access (to off-putting operations)

20

Replicability / transparency

Document your own recodes Access somebody else’s recodes Identify commonly used recodes (& use them..!)

21

Plurality of approaches Diminishing excuses for not trying out

multiple operationalisations…

22

0.0

5.1

.15

ES5

ES2E9

E6E5

E3E2

G13G11

G10G7

G5G3

G2K4

R7WR

WR9O17

O8O4

MNI9

I99CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN1

Increase in R-squared Increase in BIC

Britain-.

050

.05

.1.1

5

ES5

ES2E9

E6E5

E3E2

G13G11

G10G7

G5G3

G2K4

R7WR

WR9O17

O8O4

MNI9

I99CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN1

Sweden

Source: BHPS and LNU 1991, adults aged 23-55 in work in 1991, N=4536 Britain, 2504 Sweden. Model 1: ISEI = linear age + gender ; Model 2: ISEI = (Model 1) + occupation-based social classificationGraph shows improvement in R2 for OLS regression, Model 2 v's Model 1,plus scaled BIC statistic (Model 2 BIC - Model 1 BIC / Model 1 BIC). Unweighted data.

Explanatory power of schemes in predicting father's ISEI

Figure 12: R-2 and BIC for Fathers ISEI

Making complex things easier

Organising complex categorical dataLabelling, recoding, etc

Effect proportional scalingStandardisation Interaction terms

23

1. White

2. Mixed 3. Indian

5. Bangladeshi

6. Other Asian

7. Black-Caribbean

8. Black African

9. Other Black

10. Chinese

11. Other ethnic group

4. Pakistani

-2-1

01

2

Source: BHPS wave 17, n = 12626, % 'White' = 97.3

Identified principally by age, gender attitudes and household incomeSOR model dimension scores for BHPS ethnic groups

24

GESDE: Grid Enabled Specialist Data Environments

Facilities for collecting together, and distributing, specialist data resourcesOccupations: GEODE project began 2005Education and Ethnicity: GEEDE and GEMDE began

Feb. 2008

Capacity building aims: improving use of measures of these concepts by improving access to relevant information providing training / advice on good practice

25

GEODE: Organising and distributing specialist data resources (on occupations)

The GEODE model for GEMDE? Occupational Information Resources Occupational Unit Groups

26

Our approach to GEMDE

….A service for MUGs and MIRs…

o Define/register ‘Minority Unit Groups’

o Define/register ‘Minority Information Resources’

o Explore data resources and obtain help in approaching analysis of complex, sparse data

What's a MIR? 'Minority Information Resource'.

o This is our own terminology. By a MIR, we mean any piece of information which supplies systematic data on a minority unit group (MUG) classification. We've used this term to be deliberately similar to the phrase 'Occupational Information Resources' that we used on GEODE

E.g. summary statistical data about the categories from and documentation or information

E.g. recodings which have been used in a particular studyo Social scientists are not in general aware of the existence of MIRs (cf. wides

use of popular Occupational Information Resources). In GEMDE we seek to publicise little know resources and promote their uptake: We argue that better communication and dissemination of MIRs is in fact an important step towards better scientific practice of replication and standardisation of research.

In our terms, every MIR necessarily links to a MUG (but not every MUG has a MIR).

The GEMDE prototype‘Liferay portal’ with access to MUGs and MIRs

Current facilities

Shibboleth accessDeposit MUGs/MIRsSearch/browse

deposited resourcesFeedback on

resources (user ratings)

Still to come

Additional guest access

Review live data (e.g. pooled LFS records)

Expert and user quality ratings

=> …development over 2010...

Screenshot here!

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Data used Department for Education and Employment. (1997). Family and Working Lives

Survey, 1994-1995 [computer file]. Colchester, Essex: UK Data Archive [distributor], SN: 3704.

Heckmann, F., Penn, R. D., & Schnapper, D. (Eds.). (2001). Effectiveness of National Integration Strategies Towards Second Generation Migrant Youth in a Comparative Perspective - EFFNATIS. Bamberg: European Forum for Migration Studies, University of Bamberg.

Inglehart, R. (2000). World Values Surveys and European Values Surveys 1981-4, 1990-3, 1995-7 [Computer file] (Vol. 2000). Ann Arbor, MI: Institute for Social Research [Producer]; Inter-university Consortium for Political and Social Research [Distributor].

Li, Y., & Heath, A. F. (2008). Socio-Economic Position and Political Support of Black and Ethnic Minority Groups in the United Kingdom, 1972-2005 [computer file]. 2nd Edition. Colchester, Essex: UK Data Archive [distributor], SN: 5666.

University of Essex, & Institute for Social and Economic Research. (2009). British Household Panel Survey: Waves 1-17, 1991-2008 [computer file], 5th Edition. Colchester, Essex: UK Data Archive [distributor], March 2009, SN 5151.

33

References Blossfeld, H. P., Mills, M., & Bernardi, F. (Eds.). (2006). Globalization, Uncertainty and

Men's Careers: An International Comparison. Cheltenham: Edward Elgar. Bennett, T., Savage, M., Silva, E. B., Warde, A., Gayo-Cal, M., Wright, D., et al. (2009).

Culture, Class, Distinction. London: Routledge. Lambert, P. S., & Gayle, V. (2009). Data management and standardisation: A

methodological comment on using results from the UK Research Assessment Exercise 2008. Stirling: University of Stirling, Technical paper 2008-3 of the Data Management through e-Social Science research Node (www.dames.org.uk)

Lambert, P. S., & Penn, R. D. (2001). SOR models and Ethnicity data in LIS and LES : Country by Country Report. Syracuse University, Syracuse, New York 13244-1020: Luxembourg Income Study Paper No. 260, Maxwell School of Citizenship and Public Affairs.

Penn, R. D., & Lambert, P. S. (2009). Children of International Migrants in Europe: Comparative Perspectives. Basingstoke: Palgrave.

Prandy, K. (1979). Ethnic discrimination in employment and housing. Ethnic and Racial Studies, 2(1), 66-79.

Simpson, L., & Akinwale, B. (2006). Quantifying Stablity and Change in Ethnic Group. Manchester: University of Manchester, CCSR Working Paper 2006-05.

van Deth, J. W. (2003). Using Published Survey Data. In J. A. Harkness, F. J. R. van de Vijver & P. P. Mohler (Eds.), Cross-Cultural Survey Methods (pp. 329-346). New York: Wiley.