arXiv subjects

Tobias Rüttenauer

Publications and source records attributed to Tobias Rüttenauer.

3 recordsLinked to original sources

arXiv2025

Immigrant Residential Segregation in Europe: A Comparative Study of Spatial Segregation Patterns in Urban Areas across 30 Countries

Immigrant residential segregation can profoundly shape access to opportunities, immigrant integration, and inter-group relations. Yet we lack systematic evidence on how segregation varies across Europe, and what structural factors are associated with these patterns. This study addresses the gap by focusing on two questions: (i) how does immigrant-native segregation vary across urban areas in Europe, and (ii) which urban area- and country-level characteristics are consistently linked to segregation? Using harmonised 1x1 km grid-level data from the 2021/22 census, we calculate spatially weighted Dissimilarity Indices for all 717 Functional Urban Areas (FUAs) across 30 European countries. We combine these measures with rich data on demographics, the economy, housing, immigrant populations, and policy. To identify robust correlates of segregation, we apply a Specification Curve Analysis across 16,164 regression models. Segregation is higher in Western and Northern Europe compared to most of Eastern and Southern Europe. Moreover, we show that segregation is heavily driven by macro-spatial dynamics between diverse urban cores and relatively homogeneous suburban areas. At the urban area level, segregation is systematically linked to the demographic composition and spatial distribution of the local population, economic conditions, housing market characteristics, as well as the composition of the immigrant population. At the national level, established immigrant destinations are more segregated, while migration and integration policies are not consistently linked to segregation. These findings offer the most comprehensive comparative assessment of immigrant segregation across Europe to date, revealing how structural conditions relate to spatial integration.

econ.GN↗

arXiv2025

When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators

The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for how and when to use TWFE and the new DiD estimators in applied research.

econ.EM↗

arXiv2025

Spatial Data Analysis

This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where spatial relationships among data points can be of substantive interest or can significantly impact statistical analyses. The chapter begins by exploring the fundamental concepts of spatial dependence and spatial autocorrelation, and highlighting their implications for traditional econometric models. It then introduces a range of spatial econometric models, particularly spatial lag, spatial error, spatial lag of X, and spatial Durbin models, illustrating how these models accommodate spatial relationships and yield accurate and insightful results about the underlying spatial processes. The chapter provides an intuitive guide on how to interpret those different models. A practical example on London house prices demonstrates the application of spatial econometrics, emphasising its relevance in uncovering hidden spatial patterns, addressing endogeneity, and providing robust estimates in the presence of spatial dependence.

econ.EM↗