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In this article, we ask whether macro-level changes during the first year of the COVID-19 pandemic relate to changes in the levels of discrimination against women and Black job-seekers at the point of hire. We develop three main hypotheses: that discrimination against women and Black job-seekers increases due to a reduction in labor demand; that discrimination against women decreases due to the reduced supply of women employees and applicants; and that discrimination against Black job-seekers decreases due to increased attention toward racial inequities associated with the Black Lives Matter protests during the summer of 2020. We test these hypotheses using a correspondence audit study collected over two periods, before and during the early COVID-19 pandemic, for one professional occupation: accountants. We find that White women experience a positive change in callbacks during the pandemic, being preferred over White men, and this change is concentrated in geographic areas that experienced relatively larger decreases in women's labor supply. Black women experience discrimination pre-pandemic but receive similar callbacks to White men during the pandemic. In contrast to both White and Black women, discrimination against Black men is persistent before and during the pandemic. Our findings are consistent with the prediction of gender-specific changes in labor supply being associated with gender-specific changes in hiring discrimination during the COVID-19 pandemic. More broadly, our study shows how hiring decision-making is related to macro-level labor market processes.
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Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments
Extensive literature probes labor market discrimination through correspondence studies in which researchers send pairs of resumes to employers, which are closely matched except for social signals such as gender or ethnicity. Upon perceiving these signals, individuals quickly activate associated stereotypes. The Stereotype Content Model (SCM; Fiske 2002) categorizes these stereotypes into two dimensions: warmth and competence. Our research integrates findings from correspondence studies with theories of social psychology, asking: Can discrimination between social groups, measured through employer callback disparities, be predicted by warmth and competence perceptions of social signals? We collect callback rates from 21 published correspondence studies, varying for 592 social signals. On those social signals, we collected warmth and competence perceptions from an independent group of online raters. We found that social perception predicts callback disparities for studies varying race and gender, which are indirectly signaled by names on these resumes. Yet, for studies adjusting other categories like sexuality and disability, the influence of social perception on callbacks is inconsistent. For instance, a more favorable perception of signals like parenthood does not consistently lead to increased callbacks, underscoring the necessity for further research. Our research offers pivotal strategies to address labor market discrimination in practice. Leveraging the warmth and competence framework allows for the predictive identification of bias against specific groups without extensive correspondence studies. By distilling hiring discrimination into these two dimensions, we not only facilitate the development of decision support systems for hiring managers but also equip computer scientists with a foundational framework for debiasing Large Language Models and other methods that are increasingly employed in hiring processes.
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- Award ID(s):
- 1851902
- PAR ID:
- 10637509
- Editor(s):
- Otterbring, Tobias
- Publisher / Repository:
- PLOS One
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 7
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0304723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0304723
