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Network Dependency in Migration Flows – A Space-time Analysis for Germany since Re-unification

  • Timo Mitze


The contribution of this paper is to analyse the role of network interdependencies in a dynamic panel data model for German internal migration fl ows since re-unification. So far, a capacious account of spatial patterns in German migration data is still missing in the empirical literature. In the context of this paper, network dependencies are associated with correlations of migration flows strictly attributable to proximate flows in geographic space. Using the neoclassical migration model, we start from its aspatial specification and show by means of residual testing that network dependency eff ects are highly present. We then construct spatial weighting matrices for our system of interregional flow data and apply spatial regression techniques to properly handle the underlying space-time interrelations. Besides spatial extensions to the Blundell-Bond (1998) system GMM estimator in form of the commonly known spatial lag and unconstrained spatial Durbin model, we also apply system GMM to spatially filtered variables. Finally, combining both approaches to a mixed spatial filteringregression specification shows a remarkably good performance in terms of capturing spatial dependence in our migration equation and at the same time qualify the model to pass essential IV diagnostic tests. The basic message for future research is that space-time dynamics is highly relevant for modelling German internal migration flows.

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Paper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0205.

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Length: 43 pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:rwi:repape:0205
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  1. Badinger, Harald & Müller, Werner G. & Tondl, Gabriele, 2002. "Regional convergence in the European Union (1985 - 1999) : a spatial dynamic panel analysis," HWWA Discussion Papers 210, Hamburg Institute of International Economics (HWWA).
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  3. Mitze, Timo, 2009. "Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or not IV?," Ruhr Economic Papers 83, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
  5. Pesaran, M.H., 2003. "A Simple Panel Unit Root Test in the Presence of Cross Section Dependence," Cambridge Working Papers in Economics 0346, Faculty of Economics, University of Cambridge.
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  7. Michele Battisti & Gianfranco Vaio, 2008. "A spatially filtered mixture of β-convergence regressions for EU regions, 1980–2002," Empirical Economics, Springer, vol. 34(1), pages 203-203, February.
  8. Kukenova, Madina & Monteiro, Jose-Antonio, 2008. "Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation," MPRA Paper 13405, University Library of Munich, Germany, revised Feb 2009.
  9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
  10. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(02), pages 564-597, April.
  11. Jesús Mur & Ana Angulo, 2006. "The Spatial Durbin Model and the Common Factor Tests," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 207-226.
  12. Salima Bouayad-Agha & Lionel Védrine, 2010. "Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(2), pages 205-227.
  13. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
  14. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  15. Matías Mayor & Ana López, 2008. "Spatial shift-share analysis versus spatial filtering: an application to Spanish employment data," Empirical Economics, Springer, vol. 34(1), pages 123-142, February.
  16. repec:zbw:rwirep:0083 is not listed on IDEAS
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