Current projects
Current projects
Single-authored
Revise and Resubmit at Sociological Methods and Research
Studying the relationship between neighborhoods and individual-level outcomes such as crime, labor market success, or intergenerational mobility has a long history in the social sciences. As local processes such as gentrification or residential mobility constantly change neighborhoods’ composition and spatial expansion, time-constant one-size-fits-all neighborhood measures fail to capture important local dynamics. This paper presents a flexible and data-driven approach for efficiently estimating overlapping and arbitrarily shaped neighborhoods with time-dynamic boundaries. Constructed in a two-stage clustering design, the first stage identifies homogeneous groups within a city (using an automated K-Means algorithm), while the second stage clusters homogeneous groups by spatial proximity (using the HDBSCAN algorithm). In an analysis of 86 million person-year observations from 76 German cities, the paper shows that a larger spatial expansion of neighborhoods with a high socioeconomic status negatively correlates with city crime cases, while higher neighborhood fragmentation and heterogeneity correlate positively with crime rates. The findings stress the importance of flexible neighborhood estimation techniques and the necessity to view neighborhoods as non-constant entities. By modeling contexts as such agentic players, the two-staged algorithm depicts a novel and transparent tool to consider the spatial embeddedness of individuals, firms, or regions in sociological research.
Preprint here: SocArXiv Papers | Beyond Proximity: Constructing Organic Neighborhoods Using a Two-Stage Unsupervised Learning Approach
with Alexander Patzina (University Bamberg) and Katy Morris (SOFI)
Revise and Resubmit at European Sociological Review
While the association between upper secondary dropout and negative labor market consequences is well-documented, it is less clear whether this link is causal link and whether the effect of dropout is socially stratified. Using geo-referenced administrative data from Germany and a conditional instrumental variable approach that exploits distances between place of residence and large firms within a local labor market area, we find negative consequences but show that the dropout penalty is wholly concentrated among individuals from disadvantaged backgrounds. We further show that these stratified scars partially reflect unequal educational re-enrollment rates and speeds as well as unequal employment outcomes among dropouts who do not re-enroll. The stratified nature of the scars associated with dropout suggests there is a strong case for adopting a more targeted approach to dropout prevention. Furthermore, our study suggests that social origins have the potential to offset predictions of established signaling and credentialing theories even within highly stratified labor markets.
Preprint here: SocArXiv Papers | Stratified Scars: Social inequality in the labor market consequences of upper secondary dropout
with Leonard Wendering and Nan Zhang (both University of Mannheim)
Scholars posit that childhood exposure to ethnic outgroups may durably improve intergroup relations. To date, however, few studies are able to track the consequences of childhood experiences across multiple decades. Moreover, existing research focuses overwhelming on majority populations and largely overlooks the consequences of exposure for minority groups. Using linked geocoded US census records from 1880 to 1900/1910, we analyze the impact of having childhood neighbors of a different ethnicity on subsequent marriage patterns for over 400,000 American, German and Irish men. To account for residential self-selection, we apply a machine-learning algorithm to identify historic ``ethnic" neighborhoods and compare individuals similar on sociodemographic and neighborhood characteristics, but who differ in the identity of their next-door neighbors. We consistently find that exposure to an ethnic outgroup in 1880 increases the likelihood of marriage to a member of that group in later life, while decreasing the likelihood of endogamous marriages. Overall, these findings underscore the potential for childhood experiences to erode ethnic group boundaries.
with Sebastian Bähr (IAB)
While the fundamental link between place and inequality is well investigated, causal studies on neighborhood effects are limited. Using nationwide administrative data from Germany and a quasi-experimental identification approach, we investigate how employed network ties and role models in the residential neighborhood shape individual-level employment. In exploiting variation over time, within cities and between 1x1 kilometer grid cells, we provide a causal estimate of gendered neighborhood employment effects on refugee women's employment probability. Results support direct job referral effects of full-time employed female neighbors, which is most potent for other neighborhood women from refugee countries. Analyses of locally prevalent female work norms show a positive one-off effect of higher part-time employment shares of native neighbors indicating that neighbors serve as role models only before other structures are settled. In analyzing neighborhood effects by sex and nationality, our study reveals that even weak neighborhood ties can provide valuable resources for disadvantaged social groups in the labor market. Hence, the study stresses the necessity to break down dichotomies such as gender and ethnicity when not only explaining but also finding alternative pathways for circumventing combined hurdles of intersectionality.
Preprint here: SocArXiv Papers | Empowering through proximity: How female neighbors serve as network ties and role models for refugee women
with Marie-Fleur Philipp (University Tübingen) and Eileen Peters (WSI)
Individuals’ beliefs regarding maternal employment play a pivotal role in reproducing gender inequalities in employment and family life. Understanding their formation is key for developing effective, evidence-based policies to promote gender equality. While prior research has linked gender beliefs to personal characteristics and national policies, the influence of local contexts remains underexplored. This paper investigates how neighborhood-level female employment patterns shape beliefs about maternal full-time work in Germany. We argue that neighborhoods function as socio-cultural reference frames where local norms and opportunity structures reflect and reinforce gendered expectations and normative beliefs. Using the German Panel Study “Labour Market and Social Security” (2011 and 2016) and aggregated administrative data on the level of 1x1km grid cells, we link respondents’ beliefs to local female employment shares. Our study (1) introduces a micro-geographical perspective to move beyond broad regional analyses; (2) illuminates how local employment patterns shape normative beliefs regarding maternal employment; and (3) goes beyond common gender ideology measures regarding maternal employment by capturing beliefs regarding the appropriate age of a child at which mothers can return to work. We find that higher rates of marginal employment among female neighbors are positively, and higher rates of full-time employment are negatively associated with support for mothers’ later return to full-time work even if we control for the neighborhood’s general employment level. The study provides with a rich combination of survey and population data new insights into how exposure to female employment patterns at the neighborhood level shapes individuals’ normative beliefs regarding maternal employment.
with Sebastian Lang (LifBi)
Under Review at Work, Employment and Society
This article investigates characteristics of neighbourhoods as a mechanism to explain stigma-consciousness among the unemployed. In doing so, the article relies on the labelling approach and social contagion models to derive hypotheses about the effect of informal societal control and the scope of the employment norm. After combining rich survey data (PASS) with highly reliable georeferenced administrative 1x1km grid cell data on neighbourhood unemployment, multi-level models reveal a tipping point: The neighbourhood’s unemployment negatively affects the individuals' stigma-consciousness up to an unemployment rate of about 10% and positively affects the individual’s stigma-consciousness when the local quota exceeds the threshold of 30%. Especially the unemployed in neighbourhoods with pronounced income inequality suffer the most from higher neighbourhood unemployment quotas in terms of stigma-consciousness. Beyond highlighting the importance of local norms and how they shape individuals’ perceptions in general, the article sheds light on how norms operate differently across different spatial levels.
with Nina Gläser (IAB, University Bamberg)
Income inequality and residential segregation have intensified across major German cities, contributing to growing urban divides and reinforcing spatial patterns of social inequality. While gentrification research widely acknowledges the pivotal role of high-status individuals in transforming low-income neighborhoods, identifying their causal impact remains difficult due to strong self-selection and feedback dynamics. We address this challenge by exploiting the well-documented phenomenon that artists, who systematically sort into low-rent, low-income neighborhoods but do not themselves raise local income levels, serve as pioneers whose presence increases the area’s cultural capital and attracts high-status in-movers over time. Using fine-grained georeferenced administrative panel data (500m² grids) covering nearly half a million neighborhood-year observations across 80 German cities (2001–2017), we construct a dynamic measure of gentrification based on city-specific changes in income and education. We leverage lagged inflows of artists as an instrument for the inflow of high-status residents and estimate a two-stage least squares model with rich controls and city-by-year fixed effects. Our results show that a one percent increase in the inflowing status of residents raises the probability of gentrification within three years by up to 8.16 percentage points in Germany’s top eight cities (top seven plus Leipzig). Effects for other cities with more than 100.000 inhabitants are positive but smaller, consistent with weaker amenity-driven sorting patterns outside the top cities. Complementary analyses use georeferenced rent-level data (RWI-GEO-RED) to further show that artist-induced inflows of high-status individuals explain rising median rent levels only for the top eight cities. By isolating the causal role of early high-status in-movers, our study advances understanding of the mechanisms accelerating early neighborhood gentrification and contributes to broader debates on how urban residential dynamics reproduce social inequality.
with Alexander Patzina (University Bamberg) and Katy Morris (SOFI)
This study examines the local and social factors influencing apprenticeship dropout decisions and their long-term labor market consequences in Germany. Using individual-level administrative data from the Federal Employment Agency (N=476,605), we investigate how local labor market conditions impact dropout probabilities and accumulated income ten years after termination of education and evaluate whether these associations are socially stratified. We first replicate existing literature by showing that apprenticeship dropout is more common in areas with low unemployment and many vacancies regardless of social background. While individuals from disadvantaged backgrounds drop out more often, the difference to more advantaged individuas remains stable for varying local economic conditions. Conversely, the findings on the consequences of dropping out of vocational training in initially good local labor markets suggest a high-risk, high-reward trade-off: dropouts earn less than graduates when local economies deteriorate, but may earn more than graduates f the local economy continues to improve. Furthermore, income trajectories of individuals from disadvantaged backgrounds appear more sensitive to local conditions. These findings highlight the complex interaction between local labor market conditions and social origin, with individuals from advantaged backgrounds being better able to mitigate the financial consequences when dropouts experience bad luck.
with Kinga Makovi (NYU) and Malte Reichelt (FAU, IAB)
Social networks influence job information flows, but there is ambiguity regarding their significance, timing, and uniformity across social groups. We propose a framework distinguishing the roles of (1) network composition, (2) tie-activation, and (3) employer treatment of referrals, theorizing why networks might contribute to social group inequality at different stages. To empirically test how and when networks matter, we analyze the coworker networks of men and women using comprehensive administrative data from the Munich labor market from 2000 to 2014. In a first step, we track coworkers over time to see if network composition and usefulness differ for men and women. Our analysis considers coworker network characteristics such as gender composition, the number of firms employing former coworkers, and the number of former coworkers in leadership roles. We conduct stepwise matching of men and women, gradually incorporating human capital, occupation, industry, and firm characteristics to determine how worker attributes and choices explain network evolution differences. Our findings reveal that men’s and women’s coworker networks develop differently. Women have fewer former male coworkers at other establishments in the same region, fewer connections to diverse establishments, and are linked to smaller firms. These disparities may stem from women’s longer career gaps and their selection into smaller firms, affecting the size, composition, and resourcefulness of their networks.