Description: A 2023 study by Michael Schaerer and colleagues has collected and analysed data (publicly available here) including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. The published paper has produced detailed statistical analyses to pull together information from the various studies. The project aims at replicating the results and, possibly, improving the modelling, criticising some of the original assumptions made. Requires R and familiarity with non-linear regression models. Students are encouraged to work with Rmarkdown or quarto to develop their dissertation.
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The project can be split into different sub-projects (easy difficulty: replication of the published meta-analysis for evidence of gender bias in hiring decisions; medium for newer modelling). Requires skills in R and will require some learning on Bayesian modelling.
StatisticsHealthEconomics/gender-bias-in-hiring
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The project can be split into different sub-projects (easy difficulty: replication of the published meta-analysis for evidence of gender bias in hiring decisions; medium for newer modelling). Requires skills in R and will require some learning on Bayesian modelling.
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