|
KMID : 1038520210430010099
|
|
Epidemiology and Health
2021 Volume.43 No. 1 p.99 ~ p.99
|
|
|
Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
|
|
Yoon Jae-Hong
Kim Ji-Hwan
Chung Yeon-Seung
Park Jin-Su
Sorensen Glorian
Kim Seung-Sup
|
|
|
Abstract
|
|
|
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. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.
CONCLUSIONS: This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.
|
|
|
KEYWORD
|
|
|
Social discrimination, Social epidemiology, Machine learning
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
   
|