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Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
Yoon Jaehong,Kim Ji-Hwan,정연승,Park Jinsu,Sorensen Glorian,김승섭 한국역학회 2021 Epidemiology and Health Vol.43 No.-
OBJECTIVES: 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. METHODS: Using 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. RESULTS: Based 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. Furthermore, we found that women workers were more likely to under-report hiring discrimination than men workers. CONCLUSIONS: This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.