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Epidemiology and Health 2021;e2021099.
DOI: https://doi.org/10.4178/epih.e2021099    [Accepted] Published online Nov 17, 2021.
Gender difference in under-reporting hiring discrimination in South Korea: a machine learning approach
Jaehong Yoon2  , Ji-Hwan Kim3  , Yeonseung Chung4  , Jinsu Park5  , Glorian Sorensen6  , Seung-Sup Kim1,7 
1Hana Science Hall B 368, Associate Professor of Epidemiology, Korea University., Seoul, Korea
2Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea, Seoul, Korea
3Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea, Seoul, Korea
4Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, Daejeon, Korea
5Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, Daejeon, Korea
6Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA, Boston, United States
7Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea, Seoul, Korea
Correspondence  Seung-Sup Kim ,Tel: 010-3152-8334, Email: ssk3@korea.ac.kr
Received: Jul 6, 2021  Accepted after revision: Nov 17, 2021
Abstract
Objectives:
To examine the gender difference in under-reporting hiring discrimination by building prediction models for the workers who responded ‘Not applicable (NA)’ to a question about hiring discrimination, although they were eligible to answer.
Method:
Using the data from 3,576 waged workers in the 7th wave (2004) of the Korea Labor and Income Panel Study, we trained and tested nine machine learning algorithms using ‘Yes’ or ‘No’ responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate prevalence of experiencing hiring discrimination among those who answered NA. Under-reporting hiring discrimination was calculated by comparing the prevalence of hiring discrimination between ‘Yes’ or ‘No’ group and ‘NA’ group.
Results:
Based on the prediction from the random forest model, we found that 58.8% of the ‘NA’ group were predicted to experience hiring discrimination, while 19.7% of the ‘Yes’ or ‘No’ group reported hiring discrimination. Furthermore, we found that female workers were more likely to under-report hiring discrimination than male workers.
Conclusions:
This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.
Keywords: Social Discrimination; Social Epidemiology; Machine Learning
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