Occupation, Education and Social Inequalities: A Case Study Linking Survey Data Sources to an Urban Microsimulation Analysis

  • Paul Lambert
  • Mark Birkin
Part of the Advances in Spatial Science book series (ADVSPATIAL)


This chapter describes how a dynamic microsimulation model of the urban population, initiated from census data, can be productively linked with rich socio-economic survey data resources in order to give a more effective understanding of the development of occupations and educational qualifications through time and hence socio-economic inequalities. Small area modelling of these elements is a key feature of understanding labour markets and hence the distribution of employment. A selection of strategies for linking data resources are discussed, some analytical results presented, and discussion given on how these approaches can be facilitated by the ‘NeISS’ infrastructural provision.

In the urban simulation model, the city is represented as a complete but synthetic array of households and their constituent individuals. The model is capable of projections forward in time through the incorporation of demographic processes relating not only to the major events of fertility, mortality and migration, but also the formation and dissolution of households and changes in individual health status. The model is operational for every city or region in Great Britain. The chapter looks at how this model can be linked with survey data from the British Household Panel Survey, and the combined results used to explore the evolution of two major socio-economic variables – occupation and education – within the microsimulation model in a manner designed to engage with extended research traditions in the sociological study of social stratification.


Social Mobility Educational Qualification Upward Mobility British Household Panel Survey Occupational Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been supported by the JISC funded project ‘National e-Infrastructure for Social Simulation’ (NeISS, and as part of the GeoSpatial Analysis node (TALISMAN) of the ESRC National Centre for Research Methods (


  1. Ballas D, Clarke G, Dorling D, Eyre H, Thomas B, Rossiter D (2005) SimBritain: A spatial microsimulation approach to population dynamics. Popul Space Place 11(1):13–34CrossRefGoogle Scholar
  2. Bell G, Hey T, Szalay A (2009) Beyond the data deluge. Science 323:1297–1298CrossRefGoogle Scholar
  3. Bills DB (2004) The sociology of education and work. Blackwell, LondonGoogle Scholar
  4. Birkin M, Clarke M (1988) SYNTHESIS: a SYNTHetic Spatial Information System for urban modelling and spatial planning. Environ Plann A 20:1645–1671CrossRefGoogle Scholar
  5. Birkin M, Wu B, Rees P (2009) Moses: dynamic spatial microsimulation with demographic interactions. In: Zaidi A, Harding A, Williamson P (eds) New frontiers in microsimulation modelling. Ashgate, FarnhamGoogle Scholar
  6. Birkin M, Procter R, Allan R, Bechhofer S, Buchan I, Goble C, Hudson-Smith A, Lambert PS, de Roure D, Sinnott RO (2010) The elements of a computational infrastructure for social simulation. Philos Trans R Soc A 368(1925):3797–3812CrossRefGoogle Scholar
  7. Blossfeld HP, Hofmeister H (2005) GLOBALIFE – life courses in the globalization process: final report, Faculty of Social and Economic Science, Otto Freidrich University of Bamberg, Bamberg, and Accessed 31 Aug 2012
  8. Bottero W (2005) Stratification: social division and inequality. Routledge, LondonCrossRefGoogle Scholar
  9. CCSR (2005) The 2001 household CAMS codebook, Version 1.2. Cathie Marsh Centre for Census and Survey Research, University of Manchester, ManchesterGoogle Scholar
  10. Chan TW (2010) The social status scale: its construction and properties. In: Chan TW (ed) Social status and cultural consumption. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  11. Erikson R, Goldthorpe JH (1992) The constant flux: a study of class mobility in industrial societies. Clarendon, OxfordGoogle Scholar
  12. Gampe J, Zinn S, Willekens F, van den Gaag N (2007) Population forecasting via microsimulation: the software design of the MicMac project. Eurostat: methodologies and working papers; theme: population and social conditions. Office for Official Publications of the European Communities, Luxembourg, pp 229–233Google Scholar
  13. Goldstein H (2003) Multilevel statistical models, 3rd edn. Arnold, LondonGoogle Scholar
  14. Goldthorpe JH, Hope K (1974) The social grading of occupations. Clarendon, OxfordGoogle Scholar
  15. Harland K, Heppenstall A, Smith D, Birkin M (2012) Creating realistic synthetic populations at varying spatial scales: a comparative critique of population synthesis techniques. J Artif Soc Social Simulation 15(1):1Google Scholar
  16. Jenkins SP (2011) Changing fortunes: income mobility and poverty dynamics in Britain. Oxford University Press, OxfordGoogle Scholar
  17. Jonsson JO, Grusky DB, Di Carlo M, Pollak R, Brinton MC (2009) Microclass mobility: social reproduction in four countries. Am J Sociol 114(4):977–1036CrossRefGoogle Scholar
  18. Lambert PS, Bihagen E (2012) Stratification research and occupation-based classifications. In: Connelly R, Lambert PS, Blackburn RM, Gayle V (eds) Social stratification: trends and processes. Ashgate, Aldershot forthcoming. ISBN 9781409430964Google Scholar
  19. Muller W, Wolbers MHJ (2003) Educational attainment in the European Union: recent trends in qualification patterns. In: Muller W, Gangl M (eds) Transitions from education to work in Europe. Oxford University Press, OxfordCrossRefGoogle Scholar
  20. Oesch D (2006) Redrawing the class map: stratification and institutions in Britain, German, Sweden and Switzerland. Palgrave, BasingstokeGoogle Scholar
  21. ONS (2000) Standard occupational classification 2000, vol. 1: Structure and description of unit groups. Office for National Statistics, LondonGoogle Scholar
  22. ONS (2010) Sub-national population projections – 2008-based projections. Office for National Statistics, LondonGoogle Scholar
  23. Platt L (2011) Understanding inequalities: stratification and difference. Polity, CambridgeGoogle Scholar
  24. Prandy K (1990) The revised Cambridge scale of occupations. Social - J Brit Social Assoc 24(4):629–655CrossRefGoogle Scholar
  25. Rose D (ed) (2000) Researching social and economic change: the uses of household panel studies. Routledge, LondonGoogle Scholar
  26. Rose D, Harrison E (eds) (2010) Social class in Europe: an introduction to the European socio-economic classification. Routledge, LondonGoogle Scholar
  27. Rose D, Pevalin DJ (eds) (2003) A researcher’s guide to the national statistics socio-economic classification. Sage, LondonGoogle Scholar
  28. Shaw M, Galobardes B, Lawlor DA, Lynch J, Wheeler B, Davey Smith G (2007) The handbook of inequality and socioeconomic position: concepts and measures. Policy Press, BristolGoogle Scholar
  29. Smits J (2003) Social closure among the higher educated: trends in educational homogamy in 55 countries. Soc Sci Res 32(2):251–277CrossRefGoogle Scholar
  30. Smith DM, Clarke GP, Harland K (2009) Improving the synthetic data generation process in spatial microsimulation models. Environ Plann A 41(5):1251–1268CrossRefGoogle Scholar
  31. Stewart A, Prandy K, Blackburn RM (1980) Social stratification and occupations. Macmillan, LondonGoogle Scholar
  32. Szreter SRS (1984) The genesis of the registrar-general: social classification of occupations. Brit J Sociol 35(4):522–546CrossRefGoogle Scholar
  33. Treiman DJ (1977) Occupational prestige in comparative perspective. Academic, New YorkGoogle Scholar
  34. University of Essex and Institute for Social and Economic Research (2010) British household panel survey: waves 1–18, 1991–2009 [computer file], 7th edn. UK Data Archive [distributor], Colchester, SN: 5151, July 2010Google Scholar
  35. van Imhoff E, Post W (1998) Microsimulation methods for population projection. Popul Engl Sel 10:97–138Google Scholar
  36. Wu B, Birkin M, Rees P (2011) A dynamic microsimulation model with agent elements for spatial demographic forecasting. Social Sci Comput Review 29(1):145–160CrossRefGoogle Scholar
  37. Zaidi A, Evandrou M, Falkingham J, Johnson P, Scott A (2009) Employment transitions and earnings dynamics in the SAGE model. In: Zaidi A, Harding A, Williamson P (eds) New frontiers in microsimulation modelling. Ashgate, ViennaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Paul Lambert
    • 1
  • Mark Birkin
    • 2
  1. 1.School of Applied Social ScienceUniversity of StirlingStirlingUK
  2. 2.School of GeographyUniversity of LeedsLeedsUK

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