Roland Rathelot

Local Ethnic Composition and Natives’ and Immigrants’ Geographic Mobility in France, 1982–1999:

This article provides empirical results on patterns of native and immigrant geographic mobility in France. Using longitudinal data, we measure mobility from one French municipality (commune) to another over time and estimate the effect of the initial municipality’s ethnic composition on the probability of moving out. These data allow us to use panel techniques to correct for biases related to selection based on geographic and individual unobservables. Our findings tend to discredit the hypothesis of a “white flight” pattern in residential mobility dynamics in France. Some evidence does show ethnic avoidance mechanisms in natives’ relocating. We also find a strong negative and highly robust effect of co-ethnics’ presence on immigrants’ geographic mobility.

Measuring Segregation when Units are Small: A Parametric Approach

This article considers the issue of measuring segregation in a population of units that contain few individuals (e.g., establishments, classrooms). When units are small, the usual segregation indices, which are based on sample proportions, are biased. We propose a parametric solution: the probability that an individual within a given unit belongs to the minority is assumed to be distributed as a mixture of Beta distributions. The model can be estimated and indices deduced. Simulations show that this new method performs well compared to existing ones, even in the case of misspecification. An application to residential segregation in France according to parents’ nationalities is then undertaken. This article has online supplementary materials.

Measuring Segregation on Small Units: A Partial Identification Analysis

We consider the issue of measuring segregation in a population of small units, considering establishments in our application. Each establishment may have a different probability to hire an individual from the minority group. We define segregation indices as inequality indices on these unobserved, random probabilities. Because these probabilities are measured with error by proportions, standard estimators are inconsistent. We model this problem as a nonparametric binomial mixture. Under this testable assumption and conditions satisfied by standard segregation indices, such indices are partially identified and sharp bounds can be easily obtained by an optimization over a low dimensional space. We also develop bootstrap confidence intervals and a test of the binomial mixture model. Finally, we apply our method to measure the segregation of foreigners in small French firms.