No abstract

Many European regions are currently experiencing 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 dataset 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 dataset 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 [least absolute shrinkage and selection operator (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.

This paper investigates how important measurement issues such as the modifiable areal unit problem (MAUP), random unevenness and spatial autocorrelation affect cross-sectional studies of ethnic segregation. We use geocoded data for German cities to investigate the impact of these measurement problems on the average level of segregation and on the ranking of cities. The findings on the average level of residential segregation turn out to be rather robust. The ranking of cities is, however, sensitive to the assumptions regarding reallocation of population across neighbourhoods that the use of different segregation measures involves. Moreover, the results suggest that standard aspatial approaches tend to underrate the degree of segregation because they ignore the spatial clustering of ethnic groups. In contrast, non-consideration of random unevenness gives rise to a moderate upward bias of the mean segregation level and involves minor changes in the ranking of cities if the minority group is large. However, the importance of random segregation significantly increases as the size of the minority group declines. If the size of specific ethnic groups differs across regions, this may also affect the ranking of regions. Thus, the necessity to properly account for measurement issues increases as segregation analyses become more detailed and consider specific (small) minority groups.