Characteristics, causes, and consequences of ethnic discrimination in online recruiting

Joint with Dominik Hangartner and Michael Siegenthaler. Draft available at request. This project is financed by the Swiss National Science Foundation

Abstract: We track recruiters’ clicks on an online recruitment platform to estimate ethnic penalties for detailed ethnic and occupational groups. We link jobseekers to their entries in the Swiss unemployment register to observe search outcomes and skills unobserved by recruiters. Based on 3.4 million decisions, we estimate large differences in ethnic penalties between groups of immigrants. Many marginalized groups face large discrimination even if they are proficient of the local language and are culturally well integrated. The differences in penalties are hard to explain with statistical discrimination because the penalties are largely unrelated to observed and unobserved skills. We find mixed evidence for attention discrimination, and the economic effect is small. Discrimination against non-Europeans is smaller if recruiters have few candidates to choose from. In percentage points terms, discrimination is constant over the skill distribution, suggesting a similar rate of return to

observable skills for natives and minorities. Finally, we provide direct evidence that discrimination prolongs unemployment. First, contact attempts missed out due to discrimination on the platform prolong unemployment. Second, ethnic penalties are highly predictive of between-group differences in unemployment duration beyond a rich set of other predictors. Third, estimating the full distribution of discrimination coefficients, we find that almost a third of recruiters discriminates against non-Europeans. It is thus likely that jobseekers meet discriminatory recruiters. 

Host country citizenship reduces hiring discrimination against immigrant minorities

Joint with Dominik Hangartner and Michael Siegenthaler. Draft available at request. This project is financed by the Swiss National Science Foundation

Abstract: Persistent labor market discrimination diminishes the capacity of immigrants to achieve economic success and to contribute to the host economy. Despite the prevalence of ethnic discrimination in many immigrant-receiving countries, we lack a reliable understanding of policies that can alleviate it. This study provides evidence that host country citizenship substantially reduces hiring discrimination. To generate valid comparisons of immigrants and natives and overcome the self-selection into naturalization, we leverage novel data from the online recruitment platform of the Swiss government. Our data enable us to monitor recruiter search behavior and statistically adjust for all jobseeker characteristics that are visible on the platform. This approach allows us to compare recruiter contact rates for otherwise similar naturalized immigrants, non-naturalized immigrants, and natives. We find that nonnaturalized immigrants are 5.9% less likely to be contacted compared to observably similar natives, and that ethnic penalties are larger for more marginalized immigrant groups. Host country citizenship reduces the extent of hiring discrimination by 76%. Additional analysis suggest that citizenship acquisition is a credible signal of successful integration that enables immigrants to overcome statistical discrimination

Adapting to Scarcity: Job Search and Recruiting Across Occupational Boundaries 

Joint with Jeremias Kläui, Rafael Lalive, and Michael Siegenthaler.  This project is financed by the National Research Program on Digital Transformation NRP-77

The Role of Wages and Fringe Benefits in Job Search: Evidence from a Large-Scale Field Experiment

Joint with Andreas Beerli, Stefano Fiorin, Andreas Gulyas, Masha Khoshnama, and Michael Siegenthaler. This project is financed by the National Research Program on Digital Transformation NRP-77

Can Job Seekers Circumvent Discriminatory Recruiters?

Joint with Dominik Hangartner, Kristina Schüpbach, and Michael Siegenthaler. This project is financed by the NCCR on the move