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Demographic Aging and Employment Dynamics in German Regions: Modeling Regional Heterogeneity

  • Thomas de Graaff
  • Daniel Arribas-Bel
  • Ceren Ozgen
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Persistence of high youth unemployment and dismal labour market outcomes are imminent concerns for most European economies. The relationship between demographic ageing and employment outcomes is even more worrying once the relationship is scrutinized at the regional level. We focus on modelling regional heterogeneity. We argue that an average impact across regions is often not very useful, and that—conditional on the region’s characteristics—impacts may differ significantly. We advocate the use of modelling varying level and slope effects, and specifically to cluster them by the use of latent class or finite mixture models (FMMs). Moreover, in order to fully exploit the output from the FMM, we adopt self-organizing maps to understand the composition of the resulting segmentation and as a way to depict the underlying regional similarities that would otherwise be missed if a standard approach was adopted. We apply our proposed method to a case-study of Germany where we show that the regional impact of young age cohorts on the labor market is indeed very heterogeneous across regions and our results are robust against potential endogeneity bias.

Notes

Acknowledgements

Daniel Arribas-Bel and Ceren Ozgen gratefully acknowledge research funding by “Population Ageing and Regional Labour Market Development” project. An edited version of the paper will appear in Modelling Aging and Migration Effects on Spatial Labor Markets book by Springer-Verlag.

References

  1. Alfo M, Trovato G, Waldmann RJ (2008) Testing for country heterogeneity in growth models using a finite mixture approach. J Appl Econ 23(4):487–514CrossRefGoogle Scholar
  2. Arcidiacono P, Jones JB (2003) Finite mixture distributions, sequential likelihood and the em algorithm. Econometrica 71(3):933–946CrossRefGoogle Scholar
  3. Biagi F, Lucifora C (2008) Demographic and education effects on unemployment in europe. Labour Econ 15(5):1076–1101CrossRefGoogle Scholar
  4. Bloom DE, Freeman RB, Korenman SD (1988) The labour-market consequences of generational crowding. Eur J Popul 3(2):131–176CrossRefGoogle Scholar
  5. De Graaff T, Van Leuvensteijn M (2013) A european cross-country comparison of the impact of homeownership and transaction costs on job tenure. Reg Stud 47(9):1443–1461CrossRefGoogle Scholar
  6. Deb P, Trivedi PK et al (1997) Demand for medical care by the elderly: a finite mixture approach. J Appl Econ 12(3):313–336CrossRefGoogle Scholar
  7. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B Methodol 39:1–38Google Scholar
  8. Foote CL (2007) Space and time in macroeconomic panel data: young workers and state-level unemployment revisitedGoogle Scholar
  9. Garloff A, Pohl C, Schanne N (2013) Do small labor market entry cohorts reduce unemployment? Demogr Res 29:379CrossRefGoogle Scholar
  10. Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res B Methodol 37(8):681–698CrossRefGoogle Scholar
  11. Kohonen T (2001) Self-organizing maps. Springer series in information sciences, vol 30. Springer, BerlinGoogle Scholar
  12. Korenman S, Neumark D (2000) Cohort crowding and youth labor markets: a cross-national analysis, number January, University of Chicago Press, ChicagoGoogle Scholar
  13. Kosfeld R, Werner D-ÖA (2012), Deutsche arbeitsmarktregionen–neuabgrenzung nach den kreisgebietsreformen 2007–2011. Raumforsch Raumordn 70(1):49–64CrossRefGoogle Scholar
  14. Lancaster T (1992) The econometric analysis of transition data, vol 17. Cambridge university press, CambridgeGoogle Scholar
  15. Moffat J, Roth D (2013) The cohort size-wage relationship in europe, technical report. Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)Google Scholar
  16. Munch JR, Rosholm M, Svarer M (2006) Are homeowners really more unemployed? Econ J 116(514):991–1013CrossRefGoogle Scholar
  17. Sander N (2014) Internal migration in germany, 1995–2010: new insights into east-west migration and re-urbanisation. Comp Popul Stud 39(2):217–246Google Scholar
  18. Shimer R (2001) The impact of young workers on the aggregate labor market. Q J Econ 116:969–1007CrossRefGoogle Scholar
  19. Skans ON (2005) Age effects in Swedish local labor markets. Econ Lett 86:419–426CrossRefGoogle Scholar
  20. Spielman S, Folch DC (2015) Chapter 9. Social area analysis and self-organizing maps. In: Brunsdon C, Singleton A (eds) Geocomputation: a practical primer. Sage, Los Angeles, pp 152–168Google Scholar
  21. Stock JH, Watson MW (2003) Introduction to econometrics, vol 104. Addison Wesley, BostonGoogle Scholar
  22. Wedel M, Kamakura WA (2012) Market segmentation: conceptual and methodological foundations, vol 8. Springer Science & Business Media, New YorkGoogle Scholar
  23. Wehrens R, Buydens LM et al (2007) Self-and super-organizing maps in r: the kohonen package. J Stat Softw 21(5):1–19CrossRefGoogle Scholar
  24. Yan J, Thill J-C (2009) Visual data mining in spatial interaction analysis with self-organizing maps. Environ Plann B Plann Des 36(3):466–486CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Thomas de Graaff
    • 1
  • Daniel Arribas-Bel
    • 2
  • Ceren Ozgen
    • 3
    • 4
  1. 1.Vrije Universiteit AmsterdamAmsterdamNetherlands
  2. 2.University of LiverpoolLiverpoolUK
  3. 3.University of BirminghamBirminghamUK
  4. 4.IZABonnGermany

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