Abstract
Spatial microsimulation models can be used to produce small area output for a deeper understanding of inequality. Dynamic spatial microsimulation models can be used to model transitions such as leaving home, entering school, university, the labour market, etc. This chapter presents a dynamic spatial microsimulation approach to the analysis of educational inequalities. The method simulates individual units (potential students) over a period of time. This chapter describes a model that utilises the BHPS dataset to build a dynamic spatial microsimulation model for the analysis of social and spatial inequalities in educational attainment. Educational attainment is particularly suitable for the development and application of a dynamic spatial microsimulation model given the influence that education has on a person’s life outcomes. The dynamic spatial microsimulation model described in this chapter has been used in a case study to analyse social and spatial inequalities in higher education entry and attainment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ainsworth, J. W. (2002). Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces, 81, 117.
Allen, J., & van der Velden, R. (2001). Educational mismatches versus skill mismatches: Effects on wages, job satisfaction, and on-the-job search. Oxford Economic Papers, 53(3), 434–452.
Azzoni, C. R., & Servo, L. M. S. (2002). Education, cost of living and regional wage inequality in Brazil. Papers in regional science, 81(2), 157–175.
Ballas, D. (2004). Simulating trends in poverty and income inequality on the basis of 1991 and 2001 census data: a tale of two cities. Area, 36(2), 146–163.
Ballas, D., Clarke, Dorling, Eyre, Rossiter & Thomas. (2005a). SimBritain: A spatial microsimulation approach to population dynamics. Population, Space and Place, 11(1), 13–34.
Ballas, D., Clarke, G. P., & Wiemers, E. (2005b). Building a dynamic spatial microsimulation model for Ireland. Population, Space and Place, 11(3), 157–172.
Ballas, D., Clarke, G., Dorling, D., & Rossiter, D. (2007). Using SimBritain to model the geographical impact of national government policies. Geographical Analysis, 39(1), 44–77.
Birkin, M., Clark, G. P., & Clark, M. (1996). Urban and regional modelling at the microscale. In G. Clarke (Ed.), Microsimulation for urban and regional policy analysis (pp. 10–27). London: Pion.
Census Dissemination. (2008). CAS-WEB. Available at: http://casweb.mimas.ac.uk/. Accessed 15 Aug 2011.
Cheshire, P., & Sheppard, S. (2004). Capitalising the value of free schools: The impact of supply characteristics and uncertainty. The Economic Journal, 114, 397–424.
Clarke, C. (2003). Pupil-centered learning: Using data to improve performance. London: Department for Education and Science.
Clarke, G., & Langley, R. (1996). A review of the potential of GIS and spatial modelling for planning in the new education market. Environment and Planning C: Government and policy, 14(3), 301–323.
Cole, K., & Dale, C. M. A. (1993). The 1991 local base and small area statistics. In The 1991 census user’s guide (pp. 201–247). London: HMSO.
Demack, S., Drew, D., & Grimsley, M. (2000). Minding the gap: Ethnic, gender and social class differences in attainment at 16, 1988–95. Race, Ethnicity and Education, 3(2), 117–143.
Duranton, G., & Monastiriotis, V. (2001). The evolution of the UK North-South divide: Should we mind the gap? European Investment Bank: Cahiers BEI, 6(2), 42–57.
Duranton, G., & Monastiriotis, V. (2002). Mind the gaps: The evolution of regional earnings inequalities in the UK, 1982–1997. Journal of Regional Science, 42(2), 219–256.
Fryer, R. H. (1997). Learning for twenty-first century. London: National Advisory group for Continuing Education and Lifelong Learning.
Gibbons, P. (2002). Scaffolding language, scaffolding learning: Teaching second language learners in the mainstream classroom. Portsmouth: Heinemann.
Gibbons, S., & Machin, S. (2003). Valuing English primary schools. Journal of Urban Economics, 53(2), 197–219.
Gilbert, N., & Troitzsch, K. G. (1999). Simulation for the social scientist (1st ed.). Philadelphia: Open University Press.
Hall, S. (1961). The new student. Higher Education Quarterly, 15(2), 152–163.
Harris, R., & Johnston, R. (2008). Primary schools, markets and choice: Studying polarization and the core catchment areas of schools. Applied Spatial Analysis and Policy, 1(1), 59–84.
IISER. (2006). Quality profile: British household panel survey version 2.0. Available at http://www.iser.essex.ac.uk/bhps/quality-profile/. Accessed 30 Aug 2011.
Kasarda, J. D. (1993). Inner-city concentrated poverty and neighborhood distress: 1970 to 1990. Housing Policy Debate, 4(3), 253–302.
Kavroudakis, D. (2009). Spatial microsimulation for researching social and spatial inequalities of educational attainment. PhD thesis, University of Sheffield, Sheffield, UK.
López Bazo, E., & Motellón, E. (2009). Human capital and regional wage gaps. Documents de Treball (IREA), 24, 1–10.
Miranti, R., Harding, A., McNamara, J., Vu, Q. N., & Tanton, R. (2010). Children with jobless parents: National and small area trends for Australia in the past decade. Australian Journal of Labour Economics, 13(1), 27–47.
Pacione, M. (1997). The geography of educational disadvantage in Glasgow. Applied Geography, 17(3), 169–192.
RodrĂguez-Pose, A., & Tselios, V. (2009). Education and income inequality in the regions of the European Union. Journal of Regional Science, 49, 411–437.
Rossiter, D., Ballas, D., Clarke, G., & Dorling, D. (2009). Dynamic spatial microsimulation using the concept of GHOSTs. International Journal of Microsimulation, 2(2), 15–26.
Singleton, A. D. (2009). Data mining course choice sets and behaviours for target marketing of higher education. Journal of Targeting, Measurement and Analysis for Marketing, 17(3), 157–170.
Taylor, M. F., Brice, J., Buck, N., & Prentice-Lane, E. (2001). British household panel survey user manual volume A: Introduction. Colchester: University of Essex.
Thomas, Pritchard, Ballas, Vickers & Dorling (2009). A tale of two cities: The Sheffield project. Sheffield: Social & Spatial Inequalities Research Group, Department of Geography, University of Sheffield. Available at http://www.sasi.group.shef.ac.uk/research/sheffield/a_tale_of_2_cities_sheffield_project_final_report.pdf. Accessed 30 Jan 2011.
UCAS. (2008). UCAS: statistical services. Available at http://www.ucas.ac.uk/about_us/stat_services. Accessed 15 Aug 2011.
White Rose. (2010). Home. White Rose University Consortium. Available at http://www.whiterose.ac.uk/. Accessed 24 Jan 2011.
Wilkinson, R., & Pickett, K. (2009). The spirit level: Why greater equality makes societies stronger. New York: Bloomsbury Pub Plc.
Williamson, P. (1999). Microsimulation: An idea whose time has come?. In 39th European Regional Science Association Congress, University College Dublin, Dublin, Ireland (p. 27). Dublin: University College Dublin.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht.
About this chapter
Cite this chapter
Kavroudakis, D., Ballas, D., Birkin, M. (2012). SimEducation: A Dynamic Spatial Microsimulation Model for Understanding Educational Inequalities. In: Tanton, R., Edwards, K. (eds) Spatial Microsimulation: A Reference Guide for Users. Understanding Population Trends and Processes, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4623-7_13
Download citation
DOI: https://doi.org/10.1007/978-94-007-4623-7_13
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4622-0
Online ISBN: 978-94-007-4623-7
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)