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Original Article
- Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
- Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung, Jinsu Park, Glorian Sorensen, Seung-Sup Kim
- Epidemiol Health. 2021;43:e2021099. Published online November 17, 2021
- DOI: https://doi.org/10.4178/epih.e2021099
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AbstractOBJECTIVES
This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded “not applicable (NA)” to a question about hiring discrimination despite being eligible to answer.METHODSUsing data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using “yes” or “no” responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered “NA.” Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the “yes” or “no” group and the “NA” group.RESULTSBased on the predictions from the random forest model, we found that 58.8% of the “NA” group were predicted to have experienced hiring discrimination, while 19.7% of the “yes” or “no” group reported hiring discrimination. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.CONCLUSIONSThis study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms. -
SummaryKorean summary
본 연구는 한국노동패널조사(7차년도)에 포함된 3576명의 임금근로자의 자료를 이용해 성별에 따른 구직 과정 경험한 차별에 대한 과소보고의 규모를 확인하고자 하였다. 질문에 “예” 또는 “아니요”라고 응답한 임금근로자 3479명 데이터를 이용하여 고용 시 차별경험을 예측하는 머신러닝 모형을 구축하였고, 이를 활용해 이미 직장에서 일하고 있는 상태임에도 “해당사항 없음”이라고 응답한 임금근로자 97명이 차별을 경험했는지 여부를 예측하였다. 분석결과, “해당사항 없음”이라고 답한남성 근로자 64명 중 29명(45.3%), 여성 근로자 33명 중 28명(84.8%)가 실제로 차별을 경험한 것으로 추정되었다.Key Message
We examined gender differences in under-reporting hiring discrimination for wage workers who responded “not applicable(NA)” to a question about hiring discrimination despite being eligible to answer “yes” or “no.” Using data from 3,576 wage workers of the Korea Labor and Income Panel Study, we estimated the prevalence of hiring discrimination among those who answered “NA,” based on the best-performing machine learning prediction model for “yes” or “no” group. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.
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