Discrimination at the Intersection of Age, Race, and Gender: Evidence from an Eye‐Tracking Experiment

Abstract

This paper uses a laboratory experiment with eye‐tracking and about 6,000 unique randomized resumes to explore the effects of race on employment discrimination over the life cycle for experienced applicants in their mid 30s to 70s. We find striking evidence of race discrimination against prime‐age Black job applicants that diminishes into middle age before re‐emerging for older applicants. Screeners in professional programs mechanically process Black and White resumes similarly, but spend less time on resumes for the younger Black applicants. We find evidence of levels‐based statistical discrimination, suggesting that screeners believe younger Black applicants to have worse computer skills and more gaps in their job histories. We also find evidence for variance‐based statistical discrimination against Black applicants of all ages, suggesting that screeners perceive the job history signal to be stronger for White applicants than for Black, disproportionately affecting older Black applicants. Evidence from a smaller companion study using Human Resources (HR) managers and from the Current Population Survey (CPS) supports the external validity of our experiment, particularly for female job applicants. Results are robust to a number of different controls and specification choices.

Do book consumers discriminate against Black, female, or young authors?

“…For example, while Albert et al found discrimination against job candidates older than 38 [ 28 ], Richardson et al [ 29 ] found employers considered candidates aged 42–48 more competent and more likely to be hired compared to both younger and older candidates, with the strongest discrimination against candidates over age 54. In a similar vein, at the intersection of age and race, Lahey and Oxley [ 14 ] found that job screeners discriminated against Black job candidates, but this discrimination was less against middle aged candidates than younger or older ones.…”

“…Correspondence studies have been used to investigate discrimination, with a preponderance of studies examining discrimination in labor and housing markets [ 11 , 12 ]. The classic study design sends resumes in response to job ads or real estate listings from equally qualified candidates who differ most commonly by race [ 11 ], although studies have also examined gender and age as well as the intersection of these attributes [ 12 – 14 ]. In a similar tradition, recent studies use experimental techniques to study discrimination in product markets: for example, Airbnb rentals [ 15 ], iPod sales [ 16 ], baseball card sales [ 17 ], and online car sales [ 18 ] (see [ 11 , 19 ] for reviews).…”

“…Statistical discrimination explanations seek to provide a rational actor justification for discriminatory decision-making, wherein decisions are not based on taste or preference for one group over another but instead use group membership as a proxy for the attributes of true interest. The idea behind statistical discrimination is that critical but unobserved attributes have different distributions among social groups—usually gender, age, or racial or ethnic groups [ 14 , 34 , 35 ] —– and decision-makers use this observable group identification to enhance their statistical odds of selecting candidates with the unobserved but desired attributes.…”

A field study of age discrimination in the workplace: the importance of gender and race‒pay the gap

“…The present study explores the extent to which chronological age intersects with other characteristics, such as gender and race, and has an impact on the recruitment process (Neumark et al, 2019;Drydakis et al, 2018a). Although there are studies that examine the role of age or the role of gender and race on employment outcomes, there are only few that try to understand how the three characteristicsage, gender and raceintersect and shape recruitment decisions (Lahey and Oxley, 2021). The present research builds upon previous work by Drydakis et al (2018a) that compared outcomes for young and old Black and White applicants in low skilled positions.…”

“…, 2018a). Although there are studies that examine the role of age or the role of gender and race on employment outcomes, there are only few that try to understand how the three characteristics – age, gender and race – intersect and shape recruitment decisions (Lahey and Oxley, 2021). The present research builds upon previous work by Drydakis et al.…”