We use data from Airbnb to identify the mechanisms underlying discrimination against ethnic minority hosts. Within the same neighborhood, hosts from minority groups charge 3.2 percent less for comparable listings. Since ratings provide guests with increasingly rich information about a listing’s quality, we can measure the contribution of statistical discrimination, building upon Altonji and Pierret (2001). We find that statistical discrimination can account for the whole ethnic price gap: ethnic gaps would disappear if all unobservables were revealed. Also, three-quarters (2.5 points) of the initial ethnic gap can be attributed to inaccurate beliefs of potential guests about hosts’ average group quality.

This paper measures the job-search responses to the COVID-19 pandemic using real-time data on vacancy postings and job ad views on Sweden’s largest online job board. First, new vacancy postings drop by 40%, similar to the US. Second, job seekers respond by searching less intensively, to the extent that effective labour market tightness increases during the first three months after the COVID outbreak. Third, they redirect their search towards less severely hit occupations, beyond what changes in vacancies would predict. Overall, these job-search responses have the potential to amplify the labour demand shock.

Suppose that a population, composed of a minority and a majority group, is allocated into units, which can be neighborhoods, firms, classrooms, etc. Qualitatively, there is some segregation whenever allocation leads to the concentration of minority individuals in some units more than in others. Quantitative measures of segregation have struggled with the small-unit bias. When units contain few individuals, indices based on the minority shares in units are upward biased. For instance, they would point to a positive amount of segregation even when allocation is strictly random. The command segregsmall implements three recent methods correcting for such bias: the nonparametric, partial identification approach of D’Haultfœuille and Rathelot (2017, Quantitative Economics 8: 39–73); the parametric model of Rathelot (2012, Journal of Business & Economic Statistics 30: 546–553); and the linear correction of Carrington and Troske (1997, Journal of Business & Economic Statistics 15: 402–409). The package also allows for conditional analyses, namely, measures of segregation accounting for characteristics of the individuals or the units.

Although the reservation wage plays a central role in job search models, empirical evidence on the determinants of reservation wages, including key policy variables such as unemployment insurance (UI), is scarce. In France, unemployed people must declare their reservation wage to the Public Employment Service when they register to claim UI benefits. We take advantage of these rich French administrative data and of a reform of UI rules to estimate the effect of the Potential Benefit Duration (PBD) on reservation wages and on other dimensions of job selectivity, using a difference-in-difference strategy. We cannot reject that the elasticity of the reservation wage with respect to PBD is zero. Our results are precise and we can rule out elasticities larger than 0.006. Furthermore, we do not find any significant effects of PBD on the desired number of hours, duration of labor contract and commuting time/distance. The estimated elasticity of actual benefit duration with respect to PBD of 0.3 is in line with the consensus in the literature. Exploiting a Regression Discontinuity Design as an alternative identification strategy, we find similar results.

Could we significantly reduce US unemployment by helping job seekers move closer to jobs? Using data from the leading employment board CareerBuilder.com, we show that, indeed, workers dislike applying to distant jobs: job seekers are 35 percent less likely to apply to a job 10 miles (mi.) away from their zip code of residence. However, because job seekers are close enough to vacancies on average, this distaste for distance is fairly inconsequential: our search and matching model predicts that relocating job seekers to minimize unemployment would decrease unemployment by only 5.3 percent. Geographic mismatch is thus a minor driver of aggregate unemployment.

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 of hiring 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.

This paper investigates the heterogeneity of ethnic employment gaps using a new single-index based approach. Instead of stratifying our sample by age or education, we study ethnic employment gaps along a continuous measure of employability, the employment probability minority workers would have if their characteristics were priced as in the majority group. We apply this method to French males, comparing those whose parents are North African immigrants and those with native parents. We find that both the raw and the unexplained ethnic employment differentials are larger for low-employability workers than for high-employability ones. We show in a theoretical framework that this heterogeneity can be accounted for by homogeneous underlying mechanisms and is not evidence for, say, heterogeneous discrimination. Finally, we discuss our main empirical findings in the light of simple taste-based vs. statistical discrimination models.

How many workers are confined indefinitely in a labor cycle, moving back and forth from temporary jobs to nonemployment periods without the potential to secure a steady job? To answer this question, we develop a mixture of four Markov chains, each of them modeling a specific type of labor market dynamics: stayers in steady jobs, stayers out of the labor market, unconfined movers who transition between states and eventually enter a permanent job, and confined movers who do not. This model is estimated using data from the French Labor Force Surveys from 2005 to 2009 for eight groups defined by gender and age to account for age-related life cycle periods. Education is included as an explanatory variable for the dynamics type. Confined movers represent 5% of the 20- to 59-year-old individuals who have finished their studies but 12% of 20- to 29-year-olds, and more than 25% of 20- to 29-year-olds with less education — up to junior high school completion. Confined movers experience much more frequent periods of unemployment than movers who can access stable jobs. Furthermore, confined movers are more likely to have less favored social backgrounds, have immigrant parents, and live in deprived areas (“Zones Urbaines Sensibles”). These empirical findings may be related to the economic mechanisms responsible for the duality of the French labor market.

This paper aims to isolate the ethnic gap on the labor market that can be attributed to ethnicity and not to differences in individual characteristics or residential location. Controlling for residential location is important as ethnic minorities often live in distressed neighborhoods. It is also challenging because spatial sorting is likely to differ across ethnicities because of labor- or housing-market discrimination. This paper shows that controlling for neighborhoods and observed individual characteristics fails to provide a consistent estimate for the component of the gap accountable to ethnicity only. However, under some assumptions, the quantity of interest is set identified even when heterogeneous sorting patterns across ethnicities are allowed for and the set estimate can still be informative. A two-step estimation method is presented and applied to explain the ethnic employment differential in France, between French individuals of North African ancestry and those with non-immigrant parents. Most of the gap is not due to differences in residential location or individual characteristics, but rather to ethnicity itself.

This article reports the results from a randomized experiment designed to evaluate the direct and indirect (displacement) impacts of job placement assistance on the labor market outcomes of young, educated job seekers in France. We use a two-step design. In the first step, the proportions of job seekers to be assigned to treatment (0%, 25%, 50%, 75%, or 100%) were randomly drawn for each of the 235 labor markets (e.g., cities) participating in the experiment. Then, in each labor market, eligible job seekers were randomly assigned to the treatment, following this proportion. After eight months, eligible, unemployed youths who were assigned to the program were significantly more likely to have found a stable job than those who were not. But these gains are transitory, and they appear to have come partly at the expense of eligible workers who did not benefit from the program, particularly in labor markets where they compete mainly with other educated workers, and in weak labor markets. Overall, the program seems to have had very little net benefits.

In this study, we evaluate the impact of the French Zones Franches Urbaines on economic activity. This public-funded place-based program, comparable to US enterprise zones, exempts businesses from taxes for a period of at least five years. For the purpose of this evaluation, we merged several administrative datasets at the company level. This allows us to exhaustively observe business creations and stocks, as well as employment and financial outcomes for companies at specific geographical locations. We focus on the second round of the program, during which treated territories were selected among a pool of deprived areas according to a known set of covariates. The way treatment was assigned makes the conditional independence assumption credible in our case. Overall, we find significant effects on both business creation and employment while the impact on companies that were located in the treated areas before the program is not significant, regardless of the outcome. We also provide evidence of significant negative spillovers of the program on neighboring areas.

The purpose of this study is to examine the empirical links between social housing policy and location choices of immigrants in France. The study characterizes the main individual and contextual determinants of the probability of immigrants to live in a HLM. In general, migrants live more frequently in social housing than French natives, other observables being equal. In particular, this frequency is higher for migrants from Turkey, Morocco, Southeast Asia, Algeria, Tunisia and Sub-Saharan Africa (in decreasing order). Moreover, migrants of all origins live less often in a HLM when the city has plenty of social housing and when the fraction of natives is high. Research limitations/implications. The dataset can only measure statistical association between location choices of immigrants and the supply of social housing units at the local level, in the absence of panel data and instrumental variables. Interpretation in terms of causality is thus not permitted. The dataset used is especially valuable for studying location choices of immigrants, since it allows significant samples of immigrants, according to their country of origin, these groups being generally too small in (French) surveys.

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.

Our study focuses on the differences in wages and employment between French workers with French parents and French workers with at least one African parent, using the Formation Qualification Professionnelle survey (Insee, Paris, 2003). We introduce econometric decompositions, which allow us to reach conclusions when the potentially discriminated group is small. Then, we clarify the impact of discrimination at the hiring level in this context. We find that unexplained parts in the employment decompositions are much larger than in the wage decompositions. This suggests that, in France, labor market discrimination is more frequent at the hiring level than in the compensation process.

Determinants of businesses locations are known to be multiple. Locations of partners and competitors are crucial, as well as the territory’s local characteristics. To consider the importance of local taxes we build a Poisson model to explain the number of business creations observed in a given municipality. First results suggest that there are unobserved factors driving firm creation that are positively correlated with the level of local taxes. To deal with this potential source of endogeneity, we present an approach close to Regression Discontinuity Design. We find that, everything else being equal, higher local taxes actually tend to deter firms from setting up in a given zone but the effect is weak.