Total number of studies, categories, and cues included in our meta-analysis, along with our statistical estimation strategy. Numbers indicate the total count of studies, categories, and cues. Data sources are shown on the right-hand side: hiring managers made callback decisions based on resumes (in blue). Separately, we collected warmth and competence ratings in prolific, where participants (in red) only saw the respective cue (indicated in yellow). Our estimation strategy is visualized in the gray box in the bottom right corner: we used the averages of warmth and competence ratings to predict callback percentage. Credit: Gallo et al., 2024, PLOS ONE, CC-BY 4.0 (creativecommons.org/licenses/by/4.0/)
Perceived warmth and competence predict the influence of race, gender, and age on hiring decisions, suggesting that social perceptions might underlie such hiring bias. The meta-analysis of matching studies in North America is published on July 10, 2024 in the open-access journal PLOS ONE by Carina Hausladen of the California Institute of Technology and ETH Zürich, Marcos Gallo of the California Institute of Technology, and colleagues.
In the labor market, job applicants from marginalized groups continue to face unequal treatment. To examine hiring bias, researchers use experimental studies known as correspondence studies, in which they present employers with artificial sets of resumes, identical except for one detail that may indicate identity. Researchers can then use callback decisions to identify patterns of discrimination.
Hausladen and colleagues examined the possible link between social perceptions of candidates and decisions to recall candidates, asking whether different social perceptions associated with stereotypes of particular identities might explain the observed differences in rates of recalling candidates. They draw on the dimensions of social perception—warmth (good intentions) and competence (ability to carry out one’s intentions)—to measure the main ways in which people categorize one another.
Researchers analyzed callback rates from 21 matching studies from the United States and Canada that varied applicants’ names or attributes to indicate identity (for example, “Sarah Davis” might be perceived as a white woman, while volunteering at a church might indicate religious affiliation). Participants, selected to demographically resemble North American hiring managers, rated applicants’ characteristics in terms of perceived warmth and competence.
The analysis revealed that in studies where names were varied to indicate race, gender, and age, ratings of warmth and competence predicted differences in callbacks across identities. Characteristics of applicants who experienced lower callback rates tended to be rated as less warm and competent, and the reverse was also true.
One explanation could be that identity cues trigger stereotype-specific perceptions of warmth and competence, which then influence callback decisions. However, in studies varying attributes to signal other categories (e.g., sexuality and disability), the influence of social perception on callbacks was inconsistent.
Social perceptions can vary across cultures, and the sample sizes of some correspondence studies were also quite small, making it difficult to draw general conclusions from the study. The researchers encourage future research to include intersectional studies that expand and diversify the attributes measured.
Researchers aim to leverage this link between perceived warmth and competence and callback rates to better understand hiring discrimination and even correct biases in the large language models increasingly used to evaluate resumes.
More information:
Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments, PLoS ONE (2024). DOI: 10.1371/journal.pone.0304723
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Citation:Perceived warmth and competence predict callback decisions in a meta-analysis of recruitment experiments (July 10, 2024) retrieved July 19, 2024 from https://phys.org/news/2024-07-warmth-callback-decisions-meta-analysis.html
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