Handling data on occupations, educational qualifications, and ethnicity

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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

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Handling variables

• DAMES project (www.dames.org.uk) - specialist
  data services on three major social science
  topics (occupations, education, ethnicity)
• GEDE Grid Enabled Specialist Data
  Environments
• From www.geode.stir.ac.uk

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Handing social science variables general themes

• Common vs 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/vari
  ables/
• Existing standards of National Statistics
  Institutes and international bodies (during data
  collection)

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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

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Key variables concepts and measures
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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 dont 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

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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

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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

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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

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1. Data and research on occupations

• In the social sciences, occupation is seen as one
  of the most important things to know about a
  person
• Direct indicator of economic circumstances
• Proxy Indicator of social class or
  stratification
• GEODE how social scientists use data on
  occupations
• DAMES extending GEODE resources
• Expanding range
• Improving usability

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Stage 1 - Collecting Occupational Data (and
making a mess)
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www.geode.stir.ac.uk/ougs.html
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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. OIRs
• 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.

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in practice we dont 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

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

• disseminate, and access other, Occupational
  Information Resources
• Link together their (secure) micro-data with OIRs

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Occupational information resources small
electronic files about OUGs
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For example ISCO-88 Skill levels classification
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and UK 1980 CAMSIS scales and CAMCON classes
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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
• Dont 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_biha
  gen_2007_version1.pdf .

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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.

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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

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2. Data and research on education

• Although there have been standardisation
  attempts, data on an individuals 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)

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In international research..

• There are two leading standards
• ISCED
• www.unesco.org/education/information/nfsunesc
  o/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

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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)

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

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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?

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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
  

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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.

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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

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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

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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 distributions
• Dynamic, rapidly expanding
• Likely role is to give new guidance on emerging
  strategies for analysing and exploiting data

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Concluding summary Handling data on occupations,
educational qualifications and ethnicity

• Principles for data management
• Keep clear records
• Recodes and transformations
• Use existing standards
• Do something, not nothing
• Distributional differences by cohorts
• Learn how to match files
• Exploiting wider resources / other research