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Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
Jaehong Yoon1, Ji-Hwan Kim1, Yeonseung Chung2
1Department of Public Health Sciences, Graduate School of Korea University, Seoul, Korea.
Epidemiology and Health
|November 23, 2021
Summary
Machine learning models reveal significant gender disparities in under-reporting hiring discrimination. Women were predicted to experience discrimination at much higher rates than men among those who initially did not report it.
Area of Science:
- Epidemiology
- Sociology
- Computer Science
Background:
- Under-reporting of hiring discrimination is a significant issue in labor studies.
- Existing methods struggle to accurately capture the true prevalence of discrimination.
Purpose of the Study:
- To investigate gender differences in the under-reporting of hiring discrimination.
- To develop a predictive model for identifying individuals who experienced discrimination but did not report it.
Main Methods:
- Utilized data from the Korea Labor and Income Panel Study (N=3,576).
- Trained and evaluated nine machine learning algorithms to predict discrimination experiences.
- Applied the best-performing model (random forest) to estimate under-reporting in the 'not applicable' group.
Main Results:
- The random forest model predicted that 58.8% of individuals who answered 'not applicable' (NA) had experienced hiring discrimination, compared to 19.7% who reported it.
- Within the NA group, predicted discrimination prevalence was 45.3% for men and 84.8% for women.
- This indicates substantial under-reporting, particularly among women.
Conclusions:
- Machine learning offers a novel methodological approach to address under-reporting in discrimination studies.
- Findings highlight significant gender disparities in the experience and reporting of hiring discrimination.
- The study provides a framework for improving the accuracy of epidemiological research on social inequalities.
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