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Empirical Modelling in Regional Science pp 53–82Cite as

Testing the Neoclassical Migration Model: Overall and Age-Group Specific Results for German Regions

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Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 657))

Abstract

This chapter tests the empirical validity of the neoclassical migration model in predicting German internal migration flows. We estimate static and dynamic migration functions for 97 Spatial Planning Regions between 1996 and 2006 using key labor market signals including income and unemployment differences among a broader set of explanatory variables. Besides an aggregate specification we also estimate the model for age-group related subsamples. Our results give empirical support for the main transmission channels identified by the neoclassical framework—both at the aggregate level as well as for age-group specific estimates. Thereby, the impact of labor market signals is tested to be of greatest magnitude for workforce relevant age-groups and especially young cohorts between 18 to 25 and 25 to 30 years. This latter result underlines the prominent role played by labor market conditions in determining internal migration rates of the working population in Germany.

A shorter version of this chapter has been previously published as “Testing the Neoclassical Migration Model: Overall and Age-Group Specific Results for German Regions”, in: Zeitschrift für Arbeitsmarktforschung/Journal for Labour Market Research, Vol. 43, No. 4 (2011), pp. 277–299. We kindly acknowledge the permission of Springer to reprint the article in this monograph.

Jointly with Janina Reinkowski. Ifo Institute for Economic Research, Department Social Policy and Labour Markets, e-mail: Reinkowski@ifo.de.

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Notes

  1. 1.

    The opposite effect on NM ij holds for an increase in HK↑, INTCOMP↑ and POPDENS↑ in region j.

  2. 2.

    See e.g. Maza and Villaverde (2004) for a similar definition of the dependent variable.

  3. 3.

    Of course, a full account of the simultaneity problem may call for a system approach that is also likely to increase the estimation efficiency if there are significant cross-correlations in the error terms for functional forms of the migration and labor market variable equations. However, a fully specified system approach goes beyond the scope of this paper.

  4. 4.

    The restricted (sub-)set of moment conditions thereby only includes instruments from regressors in the vector X i,t (according to (3.11)) that remain strongly exogenous in the sense that their factor loadings are mutually uncorrelated with the cross-section specific parameter of the common factor. Sarafidis et al. (2009) propose to likewise test for the exogeneity of a subset of regressors by means of the standard Sargan/Hansen test for overidentifying restrictions in a first step.

  5. 5.

    This result is also confirmed by Brücker and Trübswetter (2004). The latter study also focuses on the role of self-selection in East–West migration, finding that East–West migrants receive a higher individual wage compared to their non-migrating counterparts after controlling for the human capital level.

  6. 6.

    In this paper we account for regional and macro regional results by including East German and state level fixed effects. However, future work should also explicitly test for the poolability of the data for regional subgroups in a partial clustering framework.

  7. 7.

    We restrict our estimation approach to this period since regional boundaries of the German Spatial Planning Regions changed before and after, which may introduce a measurement problem that is likely to bias our empirical results.

  8. 8.

    We also checked for the sensitivity of the results, when using composite indicators of human capital as discussed by Dreger et al. (2009), accounting for human capital potential (measured in terms of high school graduates with university qualification per total population between 18–20 years) as well as science and technology related indicators (e.g., patent intensity). The results did not change.

  9. 9.

    It was only for the (rest of the country) aggregate of the unemployment rate that the Levin–Lin–Chu test could not reject the null of non-stationarity. However, the LLC-test rejects the null hypothesis of an integrated time series if the unemployment rate is transformed into regional differences (\(\tilde{u}_{ij,t}\)).

  10. 10.

    The latter study found that along with a second wave of East–West movements in early 2000 net flows out of East Germany were much higher than expected after controlling for its labor market and macroeconomic performance. Since this trend was accompanied by a gradual fading out of economic distortions, this supports the view of ‘repressed’ migration flows for that period.

  11. 11.

    Detailed regression results for the state dummies are reported in Table 3.8 in Appendix A.

  12. 12.

    The authors argue that throughout the process of demographic change in Germany city core regions may gain in demographic terms from young migrants, while suburban and rural areas are expected to face increasing migration losses.

  13. 13.

    Detailed estimation results for the models are given in Appendix B.

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Authors and Affiliations

  1. Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Essen, Germany

    Dr. Timo Mitze

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Appendices

Appendix A: Estimated State Level Effects in Migration Models

Table 3.8 State level effects in baseline and augmented migration model
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Appendix B: Baseline and Augmented Regression Results by Age Groups

Table 3.9 Baseline migration model based on system GMM estimation
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Table 3.10 Augmented migration model based on system GMM estimation
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Mitze, T. (2012). Testing the Neoclassical Migration Model: Overall and Age-Group Specific Results for German Regions. In: Empirical Modelling in Regional Science. Lecture Notes in Economics and Mathematical Systems, vol 657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22901-5_3

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