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Unfair Rules for Unequal Pay: Wage Discrimination and Procedural Justice

Published online by Cambridge University Press:  14 May 2019

Scott E. Bokemper ,
Peter Descioli  and
Reuben Kline Open the ORCID record for Reuben Kline [Opens in a new window]
Scott E. Bokemper
Affiliation:
Institution for Social and Policy Studies, Yale University, 77 Prospect St., New Haven, CT 06492, USA, e-mail: sbokemp1@gmail.com
Peter Descioli
Affiliation:
Department of Political Science, Stony Brook University, 100 Nicolls Rd., Stony Brook, NY 11794, USA, e-mail: pdescioli@gmail.com
Reuben Kline*
Affiliation:
Department of Political Science, Stony Brook University, 100 Nicolls Rd., Stony Brook, NY 11794, USA, e-mail: pdescioli@gmail.com
*
*Corresponding author. Email: reuben.kline@stonybrook.edu
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Abstract

Do people judge some forms of wage discrimination to be more unfair than others? We report an experiment in an online labor market in which participants were paid based on discriminatory rules. We test hypotheses about fairness based on procedural justice, divisiveness, and affective polarization between partisans. Workers transcribed text and then learned that they earned more or less money than other workers for doing the same job. We manipulated whether the unequal pay was based on their political party, eye color, or an arbitrary choice between two doors. Consistent with the divisiveness hypothesis, participants judged discriminatory pay to be less fair when it was based on a stable characteristic, political party, or eye color, compared to a transient choice (between doors). We find mixed evidence about how affective polarization exacerbates the unfairness of partisan discrimination. We discuss implications for the procedural justice of wage discrimination.

Keywords

Procedural justiceinequalityfairnessequal paydiscriminationJ71 Labor Discrimination
Type
Research Article
Information
Journal of Experimental Political Science , Volume 6 , Issue 3 , Winter 2019 , pp. 180 - 191
Copyright
© The Experimental Research Section of the American Political Science Association 2019 

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Footnotes

We thank John Hibbing, Vittorio Merola, and Alex Shaw for helpful comments. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi: https://doi.org/10.7910/DVN/GPRPZL. The authors declare no financial conflicts of interest with respect to this study.

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