The growing supply of high-skilled labor puts pressure on urban labor and housing markets. This paper examines whether it contributes to the displacement of low- and medium-skilled workers from urban neighborhoods. Using fine-grained geocoded administrative employment data for the 14 largest German cities from 2001 to 2017, I document that high-skilled workers increasingly live in city centers while working more spatially dispersed. Medium-skilled workers tend to live and work farther from the center, whereas low-skilled workers remain disproportionately employed in central areas. Together with rising commuting distances among medium- and low-skilled workers, these patterns are consistent with potential displacement. I estimate how changes in high-skilled residents relate to changes in other skill groups within neighborhoods. To address endogeneity from joint sorting, I use a shift-share approach based on initial residential patterns. First estimates suggest that increases in high-skilled residents are associated with declines in both other groups and with higher sale and rental prices, although the rent relationship is less robust when controlling for density. To interpret these patterns in equilibrium, I develop a quantitative spatial equilibrium model with heterogeneous agents and homophilic amenity elasticities.

Presented at: CREST Urban and Spatial Workshop, Paris 2025; IAB Urban Labor Markt Workshop, Nuremberg 2025; Young Female Researchers in Urban Geography and Spatial Economics LSE, March 2026. 

Rising inflows of educated high-income individuals into metropolitan areas raise concerns about inequality and prices in cities worldwide. This paper examines the consequences of high-status inflows on neighborhood change, with particular focus on the initial wave of pioneer gentrifiers. For our analysis, we use fine-grained georeferenced administrative panel data 500x500m grid cells) covering the 80 largest German cities from 2001 to 2017, split into top cities and other metropolitan areas. First, we document a rising inflow of high-status individuals, measured by income and university education. Second, we estimate the effect of these inflows on neighborhood inequality, rents, and out-mobility. As they play a crucial role in initiating gentrification by attracting subsequent inflows, we isolate the effect of the pioneer high-status in-movers. Acknowledging sorting biases and feedback dynamics, we leverage artists who locate in disadvantaged neighborhoods and attract high-status individuals as an instrumental variable. The results show that a one percent increase in the status of pioneer gentrifiers significantly raises neighborhood inequality measured by the income Gini by up to 0.04. In addition, higher status inflows relate to rising housing prices and to higher out-mobility.

Presented at: CREST Urban and Spatial Workshop Paris 2025 (by Kerstin Ostermann), Next presentation: UEA Eu 2026, Barcelona. 

Rising living costs, particularly rents, may reduce the residential sorting options of working-class households, especially in dense urban markets with inelastic housing supply. This may particularly affect blue-collar workers, such as craft workers, vocationally trained workers, and workers in health-related occupations. In the context of gentrification, these individuals may face constrained choices: leaving high-cost areas, adjusting their labor-market behavior, for example by changing jobs, or bearing higher commuting costs and associated welfare losses. Using rich panel data on price indices and worker characteristics, I examine which German labor markets experienced the strongest rent increases over the past two decades and how different worker groups respond. I focus especially on Germany’s top regions and whether wages adjust to potential worker shortages. I further examine adjustments through commuting, retraining, unemployment, or relocation.

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

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. 

Related to the research project on how gentrification and rising rents affect labor shortage, I ask the question on how politics can tackle high rents and labor market consequences of adjustments by firms and workers. 

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.