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

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

Abstract

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.

Keywords

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.

Notes

Acknowledgement

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

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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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