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

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.

Classification of regions according to the dominant innovation barriers: The characteristics and stability of region types in Germany

“…Although many characteristics of these regions are average or only slightly below average, skill shortage is a big issue, and consequently, founding activity is relatively low. This cluster shows that non-peripheral regions might also face strong skill shortage problems and not only peripheral regions due to out-migration (as identified by Meister et al, 2023). Strong competition for skilled workers might be responsible for the skill shortage in such regions, as was found by König (2023), although for a region that is not innovation weak.…”

“…A large number of existing case studies depict these differences of lagging regions (Boltho et al, 2018; Doloreux & Dionne, 2008; Ejdemo & Örtqvist, 2021; Polese & Shearmur, 2006; Rodríguez‐Pose & Wilkie, 2019; Terluin, 2003), which are often caused by historical developments and political decisions, and in particular limited access to resources and networks, less innovative industries, worse finance conditions, and a shortage of specialized labor. Furthermore, different spatial circumstances create different dynamics in such regions (e.g., shown for migration in the case of rural regions close to metropolitan areas compared with peripheral regions by Meister et al, 2023).…”