Field and Lab Experiments on Discrimination
Paper Session
Friday, Jan. 6, 2023 10:15 AM - 12:15 PM (CST)
- Chair: Patrick Button, Tulane University and NBER
Do Inclusive Education Policies Improve Employment Opportunities? Evidence from a Field Experiment
Abstract
We study the employment opportunity of a college scholarship for high-achieving, low-income students in a labor market where disadvantaged groups are discriminated against. Using a correspondence audit-study we find that including information of being a scholarship recipient in a resume increases the likelihood of getting a callback for a job interview by 20%. However, the effects are much smaller in jobs and careers where the poor are under-represented. We show that this is consistent with the scholarship also sending a negative signal to employers and helps explain why actual beneficiaries almost never mention the scholarship in their resumes.Discrimination on the Child Care Market: A Nationwide Correspondence Study
Abstract
Migrant children are underrepresented in early child care. One potential explanation for the native-migrant enrollment gap is discriminatory behavior of child care center managers that impedes access for migrant children. We investigate this possibility by sending emails from fictitious parents to > 18'000 child care centers across Germany. The inquiry asks if there is a slot in the center available, and how to apply. We randomly varied parents' names to signal migration or native background. Emails from migrant parents receive 4.4 pp fewer answers than those from native parents. We continue to find lower response rates to migrant senders when we fix parents' education level, suggesting that effects are unlikely to be driven by child care center managers' beliefs about parental education. Email-content analysis also reveals large native-migrant gaps in a variety of dimensions, particularly in the likelihood to receive slot offers. Linking detailed regional data, we explore various mechanisms explaining our results. We find that discrimination is stronger when child care centers are located in regions with less migrants, higher right-wing vote shares, and fewer financial incentives for enrolling children with a migration background. Our paper provides first evidence that the socioeconomic gap in child care enrollment is partially caused by discrimination against migrant families.Identifying and Overcoming Gender Barriers in Tech: A Field Experiment on Inaccurate Statistical Discrimination
Abstract
Women are significantly underrepresented in the technology sector. We design a field experiment to identify statistical discrimination in job applicant assessments and test treatments to help improve hiring of the best applicants. In our experiment, we measure the programming skills of professional job applicants for a programming job. Then, we recruit a sample of employers consisting of human resource and tech professionals and incentivize them to assess the performance of these applicants based on their resumes. We find evidence consistent with inaccurate statistical discrimination: while there are no significant gender differences in performance, employers believe that female programmers perform worse than male programmers. This belief is strongest among female employers, who are more prone to selection neglect than male employers. We also find experimental evidence that statistical discrimination can be mitigated. In two treatments, in which we provide assessors with additional information on the applicants’ aptitude or personality, we find no gender differences in the perceived applicant performance. Together, these findings show the malleability of statistical discrimination and provide levers to improve hiring and reduce gender imbalance.Preferences for and Perceptions of Gender Diversity in Outcomes
Abstract
This paper describes a randomized controlled field experiment designed to measure how individuals respond to racial and gender diversity in representations of certain archetypical occupations. We ask participants in two pools – at tech conferences and online – to evaluate their user experience with an image search engine tool and randomize them to see either diverse or non-diverse images. Subjects in the less diverse treatment view images that are predominantly white men for the high-status occupations (boss and professor) and predominantly women for the low-status occupations (nurse and clerk). In the more diverse treatment, subjects view image sets that contain a more equitable distribution of gender and race. We observe that diverse images result in significantly higher ratings across all participants and find no evidence of in-group bias in this context. However, women are disproportionately more dissatisfied with the lack of diversity in high-status occupations (boss and professor) than men are. For the low-status words (clerk and nurse), we find weaker treatment effects and no heterogeneity in the satisfaction ratings by gender or race. A follow-up survey on perceptions and free response qualitative data provide a potential explanation for the findings: while the extreme underrepresentation of women and minorities in the high-status, low-pay professions is salient and goes against prior beliefs of the subjects, the equivalent underrepresentation of white men in the low-status, low-pay professions is less salient and conforms to subjects’ expectations. Correcting the asymmetry in the way we promote diversity in high-status and low-status domains has important policy implicationsJEL Classifications
- C9 - Design of Experiments
- J1 - Demographic Economics