Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach

Abstract

"Many European regions currently experience a significant population decline and, related to this, are increasingly confronted with labour shortage. Migration is a main driver of changes in regional labour supply and the local level of human capital. A region’s ability to attract residents thus becomes more and more important for its growth prospects. We use a large panel data set for the period 2003 to 2017 to investigate the relationship between local attributes and the migration balance of regions in Germany. In particular, we examine whether the factors that determine the migration balance of regions significantly differ across age and skill groups because their contribution to regional human capital likely varies. Our econometric specification can be understood as an aggregate formulation of a two-region random utility model. The data set includes 30 factors that might potentially influence a region’s migration balance. Given this large number of explanatory variables and significant multicollinearity issues, we apply machine learning techniques (Lasso, Complete-Subset-Regression) to identify important local characteristics. Our results point to a robust negative relationship between the net migration rate and population density, yet locations in close proximity to large urban centres seem to be rather attractive destination regions and the size of the effects differs significantly across age and skill groups. Moreover, labour market conditions and some amenities are significantly correlated with the region’s migration balance. However, the former and, in particular, facilities for vocational training matter primarily for young workers." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))

Cite article

Meister, M., Niebuhr, A., Peters, J. & Stiller, J. (2023): Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach. In: Regional Science Policy & Practice, Vol. 15, No. 4, p. 794-825. DOI:10.1111/rsp3.12652