We document substantial misallocation of female workers and especially married women across space using data on the universe of German workers. To rationalize these facts, we extend the standard quantitative spatial model to include couples who use intra-household bargaining to decide on a joint location. We estimate the model using administrative German employment data (IEB). We study the allocation and welfare consequences of various counterfactuals that change couples’ location decisions. 

Presented at: UEA EU 2025, Berlin; Women in Macro 2025, Berlin; Rockwool Foundation Berlin; UEA 2025 NA, Monréal

As gentrification is rising across the globe, research aims to disentangle the mechanisms behind it. A major driver is the sorting of initial gentrifiers: the first wave of high-status individuals with high education and income sorting into poor neighborhoods. However, self-selection, reversed causality, and especially the knock-on effect of attracting further high-status individuals, complicate causal analyses. Hence, prior research fails to estimate this effect causally. Recognizing sorting patterns, we instrument the inflow of high-status individuals with that of prior inflowing artists to facilitate a causal estimation approach. Using highly reliable administrative data on 500m x 500m neighborhoods for all major German cities over 17 years, we find that an artist-induced increase in status inflow by education and income leads to a significant increase in the probability that neighborhoods gentrify by up to 13 percentage points. 

Increasing density in German urban areas caused by the sorting of well-educated high earners is putting pressure on local labor and rental markets. Using fine-grain geocoded data for Germany and a spatial equilibrium model, this project examines the welfare and labor market consequences of gentrification with special focus on skill sorting. 

This article shows the potentials of georeferenced data for labor market research. We review developments in the literature and highlight areas that can benefit from exploiting georeferenced data. Moreover, we share our experiences in geocoding administrative employment data including wage and socioeconomic information of almost the entire German workforce between 2000 and 2017. To make the data easily accessible for research, we create 1-square-kilometer grid cells aggregating a rich set of labor market characteristics and sociodemographics of unprecedented spatial precision. These unique data provide detailed insights into inner-city distributions for all German cities with more than 100,000 inhabitants. Accordingly, we provide an extensive series of maps in the Additional file 1 and describe Berlin and Munich in greater detail. The small-scale maps reveal substantial differences in various labor market aspects within and across cities. 

Regions differ not only in terms of socio-demographic characteristics, but also in terms of their industry structure. This is accompanied by different unemployment rates and wages (even for otherwise identical jobs) and means that regions are affected to varying degrees by the digital, ecological and demographic transformation. We investigate the stability of regions based on their industry structure and labor market outcomes. 

Regionen unterscheiden sich nicht nur im Hinblick auf soziodemografische Merkmale, sondern auch in ihrer Branchenstruktur. Dies geht mit unterschiedlichen Arbeitslosenquoten und Löhnen (auch für sonst gleiche Tätigkeiten) einher und bewirkt, dass Regionen unterschiedlich stark von der digitalen, ökologischen und demografischen Transformation betroffen sind. Für die Bundesregierung 2025 wurden diese Themen zusammengefasst.