Dissertations

Tuesday, 26 March, 2024

Daviti Jibuti: Essays in Experimental Economics: Labor Market Discrimination

Dissertation Committee:

prof. Randall K. Filer, Ph.D.  (the City University of New York, chair)

Andreas Menzel, Ph.D. (CERGE-EI, local chair)

doc. PhDr. Michal Bauer, Ph.D. (CERGE-EI)

Defense Committee:

prof. RNDr. Filip Matějka, Ph.D. (CERGE-EI, chair)

doc. Nikolas Mittag, Ph.D. (CERGE-EI)

doc. Ing. Inna Čábelková (Czech University of Life Sciences Prague)

Referees:

Daniel S. Hamermesh, Ph.D. (University of Texas at Austin)

Partha Deb, Ph.D. (Hunter College)

Please find the link and the password for the online connection below:

https://call.lifesizecloud.com/20819921

Passcode: 6182

Abstract:

The three chapters in this dissertation use field experiments to examine discrimination in various settings. Chapters I and II use a correspondence testing approach to study discrimination against applicants with visible tattoos in the German labor market. Previous empirical research has documented unfavorable treatment of tattooed applicants on the labor market. This may be because negative stereotypes are still associated with tattooed people, despite the increasing popularity of tattoos. However, the impact of tattoos on employment chances may be different across different occupations. Fictitious applications are sent to online job postings in the banking and IT sector. Otherwise identical applications differ only in the picture attached: in the treatment group the applicants have a visible tattoo. The extent of discrimination is measured by the difference in callback rates. The data indicates that the candidates without visible tattoos have, on average, a 13-percentage point higher callback rate in the banking sector, and in the IT sector applicants with visible tattoos are about 25% less likely to get a callback.

In the third chapter we conduct a study of hiring bias on an online platform where we ask participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that providing a candidate’s information at the individual level and reducing the number of choices can reduce discrimination. On the other hand, provision of a candidate’s information at the subgroup level further increases discrimination. The results have practical implications for designing better online freelance marketplaces.

Full Text: "Essays in Experimental Economics: Labor Market Discrimination"