Working Paper: NBER ID: w26861
Authors: Patrick M. Kline; Christopher R. Walters
Abstract: This paper develops methods for detecting discrimination by individual employers using correspondence experiments that send fictitious resumes to real job openings. We establish identification of higher moments of the distribution of job-level callback rates as a function of the number of resumes sent to each job and propose shape-constrained estimators of these moments. Applying our methods to three experimental datasets, we find striking job-level heterogeneity in the extent to which callback probabilities differ by race or sex. Estimates of higher moments reveal that while most jobs barely discriminate, a few discriminate heavily. These moment estimates are then used to bound the share of jobs that discriminate and the posterior probability that each individual job is engaged in discrimination. In a recent experiment manipulating racially distinctive names, we find that at least 85% of jobs that contact both of two white applications and neither of two black applications are engaged in discrimination. To assess the potential value of our methods for regulators, we consider the accuracy of decision rules for investigating suspicious callback behavior in various experimental designs under a simple two-type model that rationalizes the experimental data. Though we estimate that only 17% of employers discriminate on the basis of race, we find that an experiment sending 10 applications to each job would enable detection of 7-10% of discriminatory jobs while yielding Type I error rates below 0.2%. A minimax decision rule acknowledging partial identification of the distribution of callback rates yields only slightly fewer investigations than a Bayes decision rule based on the two-type model. These findings suggest illegal labor market discrimination can be reliably monitored with relatively small modifications to existing correspondence designs.
Keywords: employment discrimination; correspondence experiments; callback rates; Bayesian analysis
JEL Codes: C14; C44; C9; J7; J71; K31; K42
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| number of applications sent (C00) | job-level callback probabilities (J29) |
| race or sex of applicants (J79) | job-level callback probabilities (J29) |
| callback rates (E52) | probability of discrimination (J71) |
| discriminatory practices (J71) | callback rates (E52) |
| applications sent (J68) | detection of discriminatory jobs (J71) |
| employers discriminate based on race (J71) | callback rates (E52) |