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Internal migration, regional labor markets and the role of agglomeration economies

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Abstract

We analyze the determinants and regional implications of internal migration flows across Danish municipalities in 2006–2012. Besides assessing the role of labor market and housing market factors in driving a region’s net migration rate, we particularly focus on agglomeration factors identified by “new” migration theories related to regional growth models and the new economic geography. The work contributes to the field in the following way: we extend the scarce literature on the different channels through which agglomeration economies act as an attractor for mobile labor. Moreover, we account for the role of space–time dynamic adjustment processes and simultaneity among migration and labor market variables and finally test for heterogeneity in the migration response to regional labor market disparities among low- and high-skilled migrants. Our results support the view that agglomeration economies are indeed key drivers of internal migration flows in Denmark. That is, while we obtain mixed evidence with regard to the role of traditional labor and housing market variables, most of the included proxies for agglomeration economies such as the region’s population density, patent intensity, endowment with human capital as well as the region’s employment share of knowledge-intensive services are positively correlated with the region’s net in-migration rate. Regarding the regional implications of internal migration flows, the results hint at a process of cumulative causation for the time period of analysis running from agglomeration economies to the inflow of mobile labor and subsequent regional income development.

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Notes

  1. See information provided by Elhorst (2014) for download at: http://epp.eurostat.ec.europa.eu/statistics_explained/index.php?title=File:Gross-_domestic_expenditure_on_R%26D_by_country_2005_and_2011.png&filetimestamp=20131028145400 (last accessed October 4, 2014).

  2. Dall Schmidt and Sandholt Jensen (2013) consider whether international migration to Danish regions creates new job opportunities for the incumbent workers.

  3. Related to the above discussion, the spatial mobility of labor is also at the heart of different regional growth theories with a focus on the infusion of new or different human capital structures. Endogenous growth models stress the importance of human capital and innovation as a growth driver through spillover effects (Romer 1986, 1990, 1994; Nijkamp and Poot 1998; Baldwin and Martin 2004). Human capital and innovation are rival but may only in part be excludable and therefore result in spillovers. This could take the form of worker productivity depending on the aggregate human capital level (Lucas 1988) or that workers with more human capital generate more innovations (Romer 1990). Human capital externalities in the form of learning spillovers may occur as a result from social learning (Marshall 1890; Jovanovic and Rob 1989; Glaeser 1999).

  4. As default value, we have chosen a one-period time lag for the set of regressors.

  5. The latter transformed variable can be interpreted as follows: taking the unemployment rate as an example, the regional difference in the unemployment rate (\({\tilde{u}}_{i,t} )\) is the log of the ratio of the unemployment rate in municipality i and the Danish average unemployment rate (excluding municipality i). Thus, for the capital region of Copenhagen in 2012, the relative regional unemployment rate is \(\log (7.8\,\%/5.8\,\%)=\log (1.343)\).

  6. In order to arrive at a matrix matching with the overall number of sample observations, we use the transformation \(\mathbf{I}_T \otimes \mathbf{I}_S \otimes \mathbf{W}\), where \(\mathbf{I}_T \) and \(\mathbf{I}_S \) are identity matrices of dimension T and S, respectively.

  7. Here, \(\sigma ^{2}\) is the corrected variance term of the Lee and Yu (2010) ML estimator.

  8. We only use 95 municipalities out of the total of 98 municipalities. This is caused by data restrictions for the small island municipalities Fanø, Samsø and Læsø.

  9. The reform specifically split a number of the 270 municipalities and assigned only parts of these to one of the new 98 municipalities. An aggregation procedure arriving from 270 municipalities to 98 municipalities prior to 2006 is therefore not accessible.

  10. The overall sample size is thus 95 municipalities \(\times \) 6 years \(\times \) 4 education strata = 2280 observations.

  11. See Maraut et al. (2008) for a documentation of the RegPat database.

  12. Due to a general “crisis awareness” in the population following a public debate on problems with productivity in Denmark, union members and unions were in general more concerned with maintaining employment than increasing wages. Unions’ being concerned with maintaining employment is not new, as it is also reflected in some of the thoughts on organizing the Danish labor market at the meeting of the economic council of cooperation on May 6, 1977, with participation of the major union organization. Retaining wage demands is seen as a vehicle of securing jobs or decrease job depletion in crisis time.

  13. Excluding the already introduced high-tech manufacturing and knowledge-intensive service industries. The need to control for employment-based specialization patterns as a source for unobserved regional heterogeneity has been previously pointed out by Robson (2009).

  14. Detailed results for each interaction term are not reported in Table 3 but can be obtained from the authors upon request.

  15. Non-spatial DFE estimation results can be obtained from the authors upon request. As for the migration equation, both the DFE and SDM-DFE approach obtain parameter estimates of similar magnitude and statistical significance for the regressors’ direct effects.

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Acknowledgments

The authors would like to thank three anonymous referees for valuable comments on an earlier version of the manuscript. We also acknowledge the advice and support given by the guest editors of the special issue “The Geography of Innovation”, Mikaela Backman and Hans Lööf.

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Authors and Affiliations

  1. Department of Border Region Studies, University of Southern Denmark (SDU), Alsion 2, 6400, Sønderborg, Denmark

    Timo Mitze & Torben Dall Schmidt

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  1. Timo Mitze
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Correspondence to Timo Mitze.

Appendix

Appendix

See Fig. 4 and Table 7.

Fig. 4
figure 4

Measures of agglomeration economies among Danish municipalities, 2012. Source: Register data from Statistics Denmark and own calculations; see Table 2

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Table 7 STMI test statistic for univariate spatial autocorrelation in sample variables
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Mitze, T., Schmidt, T.D. Internal migration, regional labor markets and the role of agglomeration economies. Ann Reg Sci 55, 61–101 (2015). https://doi.org/10.1007/s00168-015-0683-z

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