Bias-Motivated Updating in the Labor Market

30 Pages Posted: 11 Dec 2022

Date Written: December 8, 2022

Abstract

In the canonical economics literature on discrimination, it is assumed that statistical discrimination based on inaccurate beliefs will not persist since agents have clear incentives to update as Bayesians based on accurate information. However, if beliefs about group productivity are driven by bias rather than by an agnostic lack of information, agents may be resistant to updating in the face of accurate information that contradicts stereotypes. In this experiment, I ask Prolific workers to report their beliefs about and make incentive-compatible wage offers to other workers based on anonymized resumes both before and after providing noisily accurate signals about performance by various groups. I find that these employers’ response to information about the labor market productivity of Black and White workers is a function of their implicit biases. Employers with stronger implicit biases against Black workers update their beliefs more in response to signals that are consistent with their biases (i.e. that imply the racial gap in productivity is higher than it really is) than they do in response to signals that are inconsistent (i.e. that imply the racial gap in productivity is smaller or even reversed). The existence of such bias-motivated asymmetric updating suggests that providing information about the labor market productivity of historically stigmatized groups may not be sufficient on its own to correct inaccurate beliefs or end inaccurate statistical discrimination.

Keywords: discrimination, bias, labor, experiment, beliefs, race

JEL Classification: C91, D83, J15, J71

Suggested Citation

Rackstraw, Emma, Bias-Motivated Updating in the Labor Market (December 8, 2022). Available at SSRN: https://ssrn.com/abstract=4278076 or http://dx.doi.org/10.2139/ssrn.4278076

Emma Rackstraw (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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