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Discrimination in Labor Markets and Educational Settings: Experimental Evidence

Paper Session

Friday, Jan. 3, 2020 8:00 AM - 10:00 AM (PDT)

Marriott Marquis, Mission Hills
Hosted By: National Economic Association
  • Chair: Timothy Diette, Washington and Lee University

Do workers discriminate against their out-group employers? Evidence from the Gig Economy

Sher Afghan Asad
Iowa State University


We study possible worker-to-employer discrimination manifested via social preferences in an online labor market. Specifically, we ask, do workers exhibit positive social preferences for an out-race employer relative to an otherwise-identical, own-race one? We run a well-powered, model-based experiment wherein we recruit 6,000 workers from Amazon’s M-Turk platform for a real-effort task and randomly (and unobtrusively) reveal to them the racial identity of their non-fictitious employer. Somewhat surprisingly, we find strong evidence of race-based altruism – white workers, even when they do not benefit personally, work relatively “harder” to generate more income for black employers. Self-declared white Republicans and Independents exhibit significantly more altruism relative to Democrats. Notably, the altruism is not driven by race-specific beliefs about the income status of the employers. Our results suggest the possibility that pro-social behavior of whites toward blacks, atypical in traditional labor markets, may emerge in the gig economy where associative (dis)taste is naturally muted due to limited social contact.

Older Workers Need Not Apply? Ageist Language in Job Ads and Age Discrimination in Hiring

Patrick Button
Tulane University


We study the relationships between ageist stereotypes – as reflected in the language used in job ads – and age discrimination in hiring, exploiting the text of job ads and differences in callbacks to older and younger job applicants from a previous resume (correspondence study) field experiment (Neumark, Burn, and Button, 2019). Our analysis uses methods from computational linguistics and machine learning to directly identify, in a field-experiment setting, ageist stereotypes that underlie age discrimination in hiring. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, our evidence points most strongly to age stereotypes about physical ability, communication skills, and technology predicting age discrimination, and for women, age stereotypes about communication skills and technology. The method we develop provides a framework for applied researchers analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research.

Statistical Discrimination Versus Implicit Bias: Disentangling the Sources of Gender and Racial Bias in an Educational Setting

Dania V. Francis
University of Massachusetts-Amherst


In this paper, we seek to understand minority and female underrepresentation in advanced STEM courses in high school by investigating whether school counselors exhibit racial or gender bias during the course assignment process. We extend the analysis by attempting to disentangle whether any observed bias can be attributed to taste-based discrimination, statistical discrimination, or implicit bias. Using an adapted audit study, we asked a nationally-recruited sample of school counselors to evaluate student transcripts that were identical except for the names on the transcripts, which were varied randomly to suggestively represent a chosen race and gender combination.

In order to identify the sources of bias we included three additional experimental conditions. First, we asked every participant to take an Implicit Associations Test (IAT) at the end of the experiment. This test provides a measure of a participants’ implicit bias. Second, we randomly included additional academic information in the form of a high math PSAT score for a subsample of survey participants. If statistical discrimination is a source of bias, the introduction of additional positive academic information should decrease any evaluation gaps by race or gender on average. Finally, we included a series of questions vetted in the psychology literature to measure taste-based discrimination. Understanding the underlying sources of racial and gender bias can help stakeholders and policymakers design better solutions to address the bias.

Race, Religion, & Immigration: Experimental Evidence from the Labor Market

Deborah Rho
University of St. Thomas
Marina Mileo Gorsuch
St. Catherine University


IIn this project, we examine employers’ response to black immigrants compared to native-born black Americans. Between July 2017 and December 2018, we applied to publicly advertised positions using fictional resumes that are manipulated on perceived race and ethnicity (Somali American, African American, and white American) and examine the proportion of resumes that are contacted by employers. We find that male African American applicants are 5 percentage points less likely to be contacted than equivalent white American applicants. Somali American applicants are 11 percentage points less likely to be contacted by employers than equivalent white American applicants and 6 percentage points less likely to be contacted than equivalent African American applicants. For female applicants, the effects followed a similar pattern, but were muted. Signals of language ability, education, and religiosity showed little impact on the proportion contacted by an employer.

Constructing Capital in the Twentieth Century: The Price of Prisons and the Lasting Effects of Incarceration

Belinda Archibong
Barnard College


Institutions of justice, like prisons, can be used to serve economic and other extrajudicial interests, with lasting deleterious effects. We study the effects on incarceration when prisoners are used primarily as a source of labor using evidence from British colonial Nigeria. We digitized forty-two years of archival records on prisons from 1920 to 1938 and 1971 to 1995 and examine the impacts of labor demand shocks on the use of prison labor. We find that prison labor made up a significant share of colonial public works expenditure and infrastructure construction, and that positive economic shocks increased incarceration rates. This result is reversed in the postcolonial period, where prison labor is not a notable feature of the labor market. We document a significant reduction in contemporary trust in legal institutions in areas with high historic exposure to colonial imprisonment. The resulting reduction in trust is specific to legal institutions today.

Do Language Restrictions on Obtaining Drivers’ License Influence Immigrant Labor Market Outcomes?

Colin Cannonier
Belmont University


With about 20 percent of the US population now comprising of foreign language speakers, an increasing number of residents may be confronted with issues that may affect their labor market outcomes. For example, there are variations in requirements for obtaining a driver’s license across states, some of which offer English-only driver’s license examinations. This paper attempts to investigate the plausible causal link between language restrictions for obtaining a driver’s license and unemployment, wages and hours worked among the immigrant population that speak neither English nor Spanish at home. This study finds that an immigrants residing in states that offer driver’s license testing in two or fewer languages (including English) work less hours and earn lower wages than their counterparts in other states. There is no evidence of any effects on their likelihood of being unemployed. These results are consistent after controlling for endogeneity when propensity score matching techniques are employed.
Mackenzie Alston
Florida State University
Robynn Cox
University of Southern California
Marcus Casey
University of Illinois-Chicago
Daniel Silverman
Arizona State University
Duha Tore Altindag
Auburn University
JEL Classifications
  • J7 - Labor Discrimination
  • I2 - Education and Research Institutions