As living in a certain neighborhood substantially affects individuals’ opportunities and available resources, the investigation of neighborhood effects has a long history in sociology. However, processes like gentrification constantly change neighborhoods' appearance making time-constant one-size-fits-all neighborhood measures unlikely to capture important local dynamics. Similarly, previously applied modern network approaches are often considered as “aspatial”, i.e., they do not account for the genuine spatial dimension of neighborhood networks. This paper presents a data-driven approach for estimating neighborhood network effects with 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, while the second stage clusters homogeneous groups by spatial proximity to “organic neighborhoods”. The approach prioritizes spatial proximity for neighborhood construction and accounts for homophily being an important facet shaping the accessibility and distribution of resources. An application to city crime based on administrative data from 86 million person-year observations from 76 German cities exemplifies the algorithm. From a social inequality perspective, crime can either be considered as rights-denied access to local resources or as a sign of social disorganization. In theoretical terms, the paper complements previous research by showing that a larger spatial expansion of affluent neighborhoods and neighborhood stability negatively correlates with crime even if the level of resources stays constant. Higher 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 for explaining cross- but also inner-city inequality.

Economic inequality and social mobility are linked. Modern intragenerational careers are often portrayed as "boundaryless," meaning individuals freely transcend organizational and occupational boundaries (Arthur and Rousseau 1996) whereas studies of balkanized, segmented labor markets (Kerr 1977, Reich et al 1973) and insider-outsider divides (Emmenegger et al 2012) posit the opposite. Thus, it is crucial to determine the observable boundaries to intragenerational mobility and relate them to economic outcomes. Which boundaries to social mobility exist in different national labor markets? How do these boundaries influence individual wage trajectories? Is there cross-national variation in career patterns? Are boundaryless systems better at absorbing economic shocks?

In this contribution, I study macrological patterns of occupational mobility in several European countries. Utilizing a graph-based approach that conceptualizes the mobility table as a network of occupational nodes (Block 2023, Lin and Hung 2022, Cheng and Park 2020, Villareal 2020) and EU-SILC data, I uncover hidden trends and variation in European occupational labor mobility. The study aims to shed light on developments in the fragmentation of occupational communities by uncovering latent boundaries to inter-occupational mobility via network community detection. I further relate the connectivity of mobility systems to their capability to absorb economic shocks and to occupational wage inequality.

This project extends recent US-based research and shows how network analysis can produce novel discoveries in the study of occupational mobility. It further embeds the study of labor flow networks in the rich theoretical tradition of research on comparative political economy and welfare state research.

The rapid adoption of hybrid work by firms has led to a debate between managers and workers on the relative value of remote and in-person communication. Colocation between workers may be helpful for communication, aid with coordination, and affect the intensity of monitoring of workers by managers. Exploiting a hybrid work field experiment involving HR workers and using unique data related to the text of electronic communication between employees, this paper provides causal evidence of how colocation between employees affects internal communication within firms. A machine learning analysis of email content reveals that colocation is a substitute for horizontal, coordination-related communication, but---somewhat surprisingly---a complement to vertical, monitoring-related communication.

With economic inequality rising in most countries, studying the mechanisms of its reproduction is a pressing matter. Research shows that friendships among children of different socio-economic backgrounds affect their future education achievement and income, inter-SES (socio-economic status) friendships especially improving the outcomes of children from lower-SES families.

Few studies explored the mechanisms of friendship preferences and choices of school children so far; the results of the studies that did are conflicting. The main mechanism assumed to guide friendship choices is homophily, and it is not found consistently in the data. I will test it once again with one of the cutting-edge tools of network analysis, Stochastic Actor-Oriented Modeling (SAOM). I will also test a different mechanism of friendship choice that has never been tested before - friendship hypergamy: children of higher SES are more desirable as friends. This mechanism might account for the cases in existing research where homophily wasn’t found.

Existing research suggests that homophily, or assortativity, is not the only mechanism behind friendship selection: research shows that higher-SES students overall have more friends, while lower-SES children have less friends and are more likely to be isolated. Moreover, since SES is clearly hierarchical, homophily does not make sense as the only mechanism of friendship selection with regards to SES. Thus, exploring homophily and potential alternative mechanisms of friendship selection will broaden our understanding of friendship choice by SES as a mechanism of inequality reproduction, potentially outlining a need for more rigorous theory building on the topic.

Racial inequality is affected by space, as neighborhood conditions shape access to resources, opportunities, and services. Research has shown that many low-income and minority neighborhoods lack traditional banks, which undermines access to financial services, savings and checking accounts, loans, and credit. In contrast, such neighborhoods often have a disproportionate number of alternative financial institutions (AFI), such as payday lenders and check cashers – known for charging higher fees and interest rates. We ask why. The distribution of conventional banks vs AFIs reflect managerial decisions, and we use data on 50 interviews with managers of financial institutions to understand how such firms make location decisions. We conceive the spatial distribution of firms as a network in which firms are nodes and co-occurrence in neighborhoods are edges. Our study thus examines, using qualitative data, the decisions underlying network formation at the organizational level. This approach provides analytical leverage to understand neighborhood clustering in structural terms. We find that major factors in the decision process are the locations of other businesses, including competitors, in addition to traffic patterns and perception of neighborhood activity. We also found an unequal use of data. While AFIs rely heavily on observation and experience, banks rarely make decisions without extensive data analysis. We discuss implications for the understanding of the ecological distribution of resources and network process.

Parents interact with other parents throughout the childcare process, forming parental networks in a variety of scenarios and places. Classrooms are the appropriate contexts to explore the interplay of social categories (e.g., gender, ethnicity, socioeconomic status), parental ties and other multiplex classroom relationships (e.g., student friendships) fostered within the social systems of family, class, school and neighbourhood. As suggested by social capital theories, social networks are loaded with valuable social capitals from which parents can gain advantages for themselves and their children. The relation-based factors, including nodal network positions and the features of connected ties, can influence parents’ access to educational resources and opportunities and further determine their children’s academic performance. There is an essential concern on whether parental networks might amplify the advantages that their children derive from family socioeconomic status, thereby multiplying the existing inequalities in children’s education. This study is based on the 2010-2011 Children of Immigrants Longitudinal Survey in Four European Countries (CILS4EU). The tie-level data are given attention with the use of multilevel models involving random ego, classroom and alter effects. The effects of tie characteristics, ego attributes, alter attributes, egocentric network structural properties, and school (institutional) characteristics on students’ average grades of math, English and the survey country language are tested. The findings will shed light on the parental relational determinators of students’ academic achievements, contributing to the discussion on the relationship between social networks and educational inequalities.