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

TL;DR

This paper addresses the European heterogeneity of immigrant-native residential segregation by constructing harmonised, high-resolution measures across 717 FUAs in 30 countries using a spatial dissimilarity index $\widetilde{D}$ at a $1\,\text{km}$ radius. It employs Specification Curve Analysis over $16{,}164$ plausible models to identify robust urban- and country-level correlates, and provides a public dataset of segregation measures and macro indicators. The findings reveal a clear North/West (higher segregation) versus South/East (lower segregation) pattern, driven by macro-spatial dynamics between diverse urban cores and homogeneous suburbs, with capitals often more segregated than national averages. At urban scales, segregation correlates with population dispersion, housing markets, and immigrant inflows, while at the national level, established destinations show higher segregation; migration and integration policies do not show robust links. Overall, the study delivers a comprehensive, data-rich framework for understanding how structural conditions shape spatial integration in Europe and offers a valuable resource for policymakers and researchers.

Abstract

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.

Figures (18)

• Figure 1: Segregation across 717 FUAs across 30 countries measured by the spatial Dissimilarity Index. A) Density distribution of segregation across regions; B) Comparison with spatial Isolation Index; C) Residential Segregation of FUAs and country boxplots with capitals highlighted. Note: The box of each boxplot represents the interquartile range (IQR) across FUAs and the whiskers extend to the smallest and largest values within 1.5xIQR. Countries are ordered by the mean of the segregation index across their FUAs.
• Figure 2: Example analysis of the association of levels of segregation with FUA population dispersion: (A) Bivariate association; (B) Histogram of coefficients from Specification Curve Analysis; (C) Counterfactual coefficient distribution under null association; (D) Ranked observed vs. counterfactual estimates. Note: In panel D, the dashed lines correspond to the 95% quantile band of the ranked counterfactual bootstrapped estimates. The grey line corresponds to the original estimates.
• Figure 3: Specification Curve Analysis of associations of levels of segregation with (A) FUA population characteristics, (B) FUA economic conditions, (C) FUA housing market conditions, (D) FUA immigration flow and composition, (E) country-level characteristics. Note: The universe of 'reasonable' model specifications excludes specifications including multiple covariates from the same covariate subgroup. For example, we do not estimate models that include both GDP per capita and unemployment rate, two indicators in the economic performance covariate subgroup.
• Figure S1: Additional examples of FUAs. The colours indicate the share of immigrants in each 1x1km grid cell.
• Figure S2: A) Scatterplot of the dissimilarity index (1km) for EU immigrants and non-EU immigrants with 45 degree line; B) Scatterplot of the dissimilarity index (1km) based on the full set of observations and based on a subset without outliers (549 grid cells) with 45 degree line. Outliers were defined as having at least 50% foreign residents and either a) at least 5-fold share of foreign residents as compared to the 4 nearest grids cell, or b) twice as many male than female residents.