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.
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Notes
- 1.
http://ec.europa.eu/social/main.jsp?catId=1036, Accessed on September 23rd, 2016.
- 2.
One can apply second-stage models, where the first stage estimates the fixed effects and the second stage analyses the determinants of those fixed effects. However, note again, that this is only an analysis on the levels and not on the slopes.
- 3.
Interestingly, the underlying algorithm and implementation where only the constants α c are allowed to vary over groups is heavily applied in labour economics by, e.g., Lancaster (1992), Munch et al. (2006), and De Graaff and Van Leuvensteijn (2013), usually in a multivariate setting where the constants α c are then argued to remove unobserved heterogeneity.
- 4.
- 5.
Nomenclature of Territorial Units for Statistics.
- 6.
The particular measure we employ reads as:
$$\displaystyle \begin{aligned} \hat{L}_{rt}=\sum_k \left[\frac{E_{k,t}}{E_{k,t-1}} E_{rk,t-1}\right], \end{aligned} $$(11.10)where \(\hat {L}\) is the weighted sum of employment across all sectors k in region r in and year t − 1, with the weights being given by the rate of sector-specific employment in year t and year t − 1 at the national level. As such, this variable represents the level of employment in region r that is predicted for the case in which employment in each sector grows at the same rate as the corresponding sector at the national level indicated by E. This variable is used as an exogenous measure for demand changes for labour.
- 7.
Because of strong temporal autocorrelation in both the employment rates and the youth shares, we estimate model (11.2) by applying first differencing. Thus, we estimate: \(\ln (E_{r,t})- \ln (E_{r,t-1}) = \beta _1 (\ln (YS_{r,t})-\ln (YS_{r,t-1})) + (\epsilon _{r,t}-\epsilon _{r,t-1})\). Although less efficient than the usual within estimator, first differencing requires less strong identification assumptions. For the linear model, this should only affect the standard errors and indeed, both fixed effects estimation strategies lead to similar results. It matters however for the clustering analysis.
- 8.
Sander (2014) points out that the internal migration patterns in Germany have been predominantly within East Germany, while significant trends to urban cores from nearby suburban areas as well as metropolitan hinterlands during our study period. At the same time young adults with families out-migrated to urban agglomerations in many non-metropolitan cities. We expect that using labour market areas which includes daily commuting patterns and FMM for our analysis to some extent should tackle with potential bias internal migration patterns might cause.
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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.
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Graaff, T.d., Arribas-Bel, D., Ozgen, C. (2018). Demographic Aging and Employment Dynamics in German Regions: Modeling Regional Heterogeneity. In: R. Stough, R., Kourtit, K., Nijkamp, P., Blien, U. (eds) Modelling Aging and Migration Effects on Spatial Labor Markets. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-68563-2_11
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