Beyond Proximity: INvestigating Crime with Organic Neighborhoods and a Two-Stage Unsupervised Learning Approach 

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 

Stratified Scars: Social inequality in the labor market consequences of upper secondary dropout 

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 

The Girl Next Door: Childhood Cross-Group Exposure and Ethnic Intermarriage

with Leonard Wendering and Nan Zhang (both University of Mannheim)

Childhood exposure to ethnic outgroups may have a lasting positive impact on intergroup relations. To date, however, few studies can track the consequences of childhood experiences across multiple decades. 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 boys who are similar on sociodemographic and neighborhood characteristics, but 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.

Empowering through proximity: How female neighbors serve as network ties and role models for refugee women

with Sebastian Bähr (IAB)

Under Review at American Sociological Review

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 

Labeled Unemployed: How neighborhood unemployment affects the individuals stigma-consciousness

with Sebastian Lang (LifBi)

Literature shows that feeling stigmatized has negative consequences for various areas of life, including mental health and employment probabilities. In contrast, there is less research on the spatial and social context in which individuals become aware of stigmatization, i.e., in which contexts stigma-consciousness emerges and intensifies. In this article, we investigate the neighborhood as a mechanism to explain stigma-consciousness among the unemployed. We rely on the labeling approach and social contagion models to derive hypotheses about the effect of informal societal control and the scope of the employment norm. We test these hypotheses by combining rich survey data (PASS) with highly reliable georeferenced administrative 1x1km grid cell data on neighborhood unemployment. Linear multi-level models reveal a tipping point: The neighborhood’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%. Further analyses show that the neighborhood is the level of norm enforcement, but norm setting is happening on larger spatial levels. Especially the unemployed in neighborhoods with pronounced income inequality suffer the most from higher neighborhood unemployment quotas in terms of stigma-consciousness. Beyond highlighting the importance of local social norms and how they shape individuals’ perceptions in general, the study sheds light on how social norms operate differently on different spatial levels.

The Art of Gentrification: Estimating the Effect of High-Status Inflows on Neighborhood Change using Bohemians as Instrument

with Nina Gläser (IAB, University Bamberg)

When explaining the evolution of gentrification, measured as the aggregate status rise of a neighborhood, a key role is the influx of high-status, i.e., well-earning and highly educated, individuals. However, self-selection and reversed causality in residential choices of high-status individuals complicate causal analyses on gentrification. This paper isolates the effect of inflowing high-status individuals on neighborhood gentrification using highly reliable administrative data on half a million 500x500m neighborhoods in Germany. Recognizing sorting patterns, we instrument the inflow of high-status individuals with prior inflowing bohemians for a causal estimation approach. These creative workers follow a different pattern in sorting themselves into rather poor neighborhoods but attract high-status individuals in the medium to long run. Because selection biases are likely to differ between cities with high cultural capital and other major cities, we focus in our sample on the German top-eight cities. First findings show that an increase in the status of inflowing individuals leads to a neighborhood's probability to gentrify by up to 11.8 percentage points in these cities.

When Dropouts Experience Bad Luck: The Consequences of Dropping Out of Vocational Training Differ by Local Economy

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. We first replicate existing literature by showing that apprenticeship dropout is more common in areas with low unemployment and many vacancies and then show that dropout is also less consequential in strong local labor markets. Ten years after termination of education, apprenticeship dropouts in strong local labor markets have similar income levels to graduates, while those in places of weak labor demand face substantial income penalties. Preliminary results indicate that social origin further moderates the magnitude of the income penalty in places of weak demand, with the dropout penalty among those from more advantaged backgrounds being half the size of the penalty experienced by dropouts from more disadvantaged backgrounds. 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 educational failure meets bad luck. Further analysis will examine how the evolution of local labor market conditions over time impacts the income trajectories of dropouts, particularly when conditions were initially favorable but then deteriorated.

The various roles of coworker networks for gender inequality

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.