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On the Mutual Dynamics of Interregional Gross Migration Flows in Space and Time

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Spatial Econometric Interaction Modelling

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

This paper applies spatial dynamic panel data models to analyse the labor market dimension of interregional population flows among German federal states in the period 1993–2009. Making use of recent improvements in the estimation of space-time dynamic panel data models and the computation of meaningful scalar summary measures for the obtained regression coefficients, the empirical results show that the network of German interregional gross migration flows is subject to serial and spatial autocorrelation patterns, which affect the interpretation of the considered regional labor market signals. Using a time-dynamic spatial Durbin model as preferred empirical specification, the results indicate that regional differences in real income growth, the labor participation rate and real-estate prices impact on interregional out-migration flows. The estimated coefficients signs of the obtained space-time summary measures thereby hint at the validity of the neoclassical migration model in predicting gross out-migration flows. In order to take a closer look at the dynamic evolution the direct and indirect network effects over time, cumulative multipliers for a time horizon of up to 10 years have been computed for each variable. The results show that the speed of adjustment of the migration response towards the long-run impact of labor market signals is fast and mostly occurs within the first 3 years. Regarding the interplay of the direct and indirect effects, the estimation results uniformly hint at additive linkages for all variables. Overall, the obtained results underline the importance of a decomposition of the total effects of labor market signals on interregional migration flows by means of their spatial and temporal network dynamics.

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Notes

  1. 1.

    In the migration literature, typically the terms ‘interregional’ and ‘internal’ migration are used interchangeably to define pairwise migratory movements between different regions of a national territory. Throughout the remainder of this manuscript, I will use the former term.

  2. 2.

    The micro-foundation of the neoclassical migration model was first introduced by Sjaastad (1962). For a recent overview of different migration theories see, for instance, Hagen-Zanker (2008).

  3. 3.

    For a general classification scheme of different combination including time-space lags in a regression framework, see Anselin et al. (2007).

  4. 4.

    Additionally, it reduces the likely problem of multicollinearity among the regressors given that spatial dependence tends to arise from temporal dependence.

  5. 5.

    The first two sample years after German re-unification are dropped due to missing data and structural breaks in the time series.

  6. 6.

    A description of variable definitions and data sources is given in the appendix (see Table A.1).

  7. 7.

    Regression details for the auxiliary wage equation can be obtained from the author upon request.

  8. 8.

    Distance between two states is thereby calculated as the road distance in kilometers between a population weighted average of combination of pairs among the (up to three) major cities for each federal state.

  9. 9.

    For T = 19, the sample range includes all available time periods from 1991–2009. As outlined above, for estimation purposes, the first 2 years after German re-unification will be dropped later on.

  10. 10.

    Detailed test results can be obtained from the author upon request.

  11. 11.

    Earlier concepts have already been proposed by (Cliff and Ord 1981) as well as Griffith (1981).

  12. 12.

    For static SARAR specifications, Zimmer (2008) reports values for ρ ranging from 0.23 to 0.48.

  13. 13.

    Detailed test results are given in Table A.2 the appendix.

  14. 14.

    As Elhorst (2011) points out, although the calculation of the above effects is straightforward, no direct statistical inference on these measures can be performed. The reason is that they are composed of different coefficient estimates according to complex mathematical formulas and the dispersion of the effects depends on the dispersion of all coefficient estimates involved. This paper thus follows LeSage and Pace (2009), Elhorst (2011) and simulates the distribution of the effects using the estimated variance-covariance of the regression analysis.

  15. 15.

    We restrict the computation of weight matrices to a minimum threshold distance of d = 175 km in order to avoid modelling network flows without neighbors in the network.

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Acknowledgements

I thank the editors of this book, Giuseppe Arbia and Roberto Patuelli, as well as an anonymous reviewer for helpful comments. Moreover, I acknowledge the help from Gordon Hughes by sharing his Stata code.

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Authors and Affiliations

  1. Department of Business and Economics, University of Southern Denmark, Alsion 2, DK-6400, Sønderborg, Denmark

    Timo Mitze

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

    Timo Mitze

  3. The Rimini Centre for Economic Analysis, Rimini, Italy

    Timo Mitze

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  1. Timo Mitze
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Correspondence to Timo Mitze .

Editor information

Editors and Affiliations

  1. Department of Economics, University of Bologna, Rimini, Italy

    Roberto Patuelli

  2. Università Cattolica del Sacro Cuore, Rome, Italy

    Giuseppe Arbia

Variable Definitions

Variable Definitions

See Tables A.1 and A.2.

Table A.1 Data description and source
Full size table
Table A.2 Test for common factor restriction \(H_{0}:\gamma +\rho \beta = 0\) in DFE-SDM
Full size table

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Mitze, T. (2016). On the Mutual Dynamics of Interregional Gross Migration Flows in Space and Time. In: Patuelli, R., Arbia, G. (eds) Spatial Econometric Interaction Modelling. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30196-9_16

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