Ethnic residential segregation: A matter of ethnic minority household characteristics?

Abstract

Despite the importance of household characteristics for residential mobility, these characteristics are seldom considered in research on ethnic residential segregation. This study seeks to fill this gap when focusing on the residential behaviour of ethnic minorities and the constraints they face on the housing market. We use conditional logit modelling to analyse the Belgian Census 2011 in order to investigate the relationship between, on the one hand, the presence of children and marital status and, on the other hand, living in neighbourhoods with many coethnics in Brussels and Antwerp, focusing on households formed by young adults of Moroccan descent. We find no association for the presence of children but find clear differences based on marital status. Unmarried couples are less likely than married couples to live in neighbourhoods with many coethnics. Moreover, there appear to be gender differences: Female‐headed single‐person households are less likely than married couples to live in neighbourhoods with many coethnics, whereas male‐headed single‐person households are more likely to do so. On the basis of these results, we conclude that the existing ethnic residential segregation theories that focus on ethnic minorities should take household characteristics into account.

Random Forest Variable Importance Measures for Spatial Dynamics: Case Studies from Urban Demography

“…In studies across northern European countries, findings based on different types of regression models are consistent with spatial assimilation theory [50][51][52][53]. While these studies highlight the importance of ethnic composition in shaping migrants' residential decisions, they primarily use discrete choice models to measure the impact of different factors on choice probabilities.…”

“…We aimed to assess the relationships between migrants from different CoOs/RoOs and the distribution of one group as a determinant of the residential distribution of others, together with population and topographic characteristics. The RF algorithm was applied because it is more suitable for capturing complex relationships between dependent variables, such as different population groups, than the linear and logit models used in previous studies [50,52,53]. These models are oversimplified and unsuitable for detecting such patterns, especially when a much wider range of factors is examined; they are prone to underfitting, sensitive to outliers and assume that the data are independent-which is not the case for the data at hand.…”

Disaggregation of population estimates at high spatial resolution with machine learning

“…Kraków's recent transformation into a destination city, attracting mainly third-country nationals to the EU, makes it an interesting case study for examining patterns of migrant distribution in the early stages of their formation. As its ethnic diversity continues to grow, Kraków is expected to face similar challenges to Western European countries in terms of integrating migrants and promoting social cohesion [25]. These challenges include issues related to residential segregation and the concentration of migrants in certain areas.…”