Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
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,...
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| Veröffentlicht in: | Epidemiology and health 2021, 43(0), , pp.1-10 |
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| container_end_page | e2021099 |
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| container_start_page | e2021099 |
| container_title | Epidemiology and health |
| container_volume | 43 |
| creator | Yoon, Jaehong Kim, Ji-Hwan Chung, Yeonseung Park, Jinsu Sorensen, Glorian Kim, Seung-Sup |
| description | 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. |
| doi_str_mv | 10.4178/epih.e2021099 |
| format | Article |
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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.</description><identifier>ISSN: 2092-7193</identifier><identifier>EISSN: 2092-7193</identifier><identifier>DOI: 10.4178/epih.e2021099</identifier><identifier>PMID: 34809416</identifier><language>eng</language><publisher>SUWON: Korean Soc Epidemiology</publisher><subject>Female ; Humans ; Life Sciences & Biomedicine ; Machine Learning ; Male ; Original ; Public, Environmental & Occupational Health ; Republic of Korea - epidemiology ; Science & Technology ; Sex Factors ; social discrimination ; social epidemiology ; 예방의학</subject><ispartof>Epidemiology and Health, 2021, 43(0), , pp.1-10</ispartof><rights>2021, Korean Society of Epidemiology 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>1</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000887876300014</woscitedreferencesoriginalsourcerecordid><cites>FETCH-LOGICAL-c443t-1f0de8837287f3dfd69aa9a69adf60f77faba225142f1275d340fccb150e6b173</cites><orcidid>0000-0002-1501-2747 ; 0000-0003-1830-0282 ; 0000-0001-9424-5962 ; 0000-0001-6625-7931 ; 0000-0003-4147-4758 ; 0000-0002-5987-8913</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920741/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920741/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2115,27929,27930,39263,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34809416$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002787637$$DAccess content in National Research Foundation of Korea (NRF)$$Hfree_for_read</backlink></links><search><creatorcontrib>Yoon, Jaehong</creatorcontrib><creatorcontrib>Kim, Ji-Hwan</creatorcontrib><creatorcontrib>Chung, Yeonseung</creatorcontrib><creatorcontrib>Park, Jinsu</creatorcontrib><creatorcontrib>Sorensen, Glorian</creatorcontrib><creatorcontrib>Kim, Seung-Sup</creatorcontrib><title>Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach</title><title>Epidemiology and health</title><addtitle>EPIDEMIOL HEALTH</addtitle><addtitle>Epidemiol Health</addtitle><description>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.</description><subject>Female</subject><subject>Humans</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Original</subject><subject>Public, Environmental & Occupational Health</subject><subject>Republic of Korea - epidemiology</subject><subject>Science & Technology</subject><subject>Sex Factors</subject><subject>social discrimination</subject><subject>social epidemiology</subject><subject>예방의학</subject><issn>2092-7193</issn><issn>2092-7193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqNkkFv1DAQhSMEolXpkSvKEYRSbMexHQ5I1QrKikpIqJytiT3edZu1g5MF8e9xNu2qveFDxhp_8-LxvKJ4TckFp1J9wMFvL5ARRknbPitOGWlZJWlbP3-0PynOx_GW5MW5JIK-LE5qrkjLqTgt4AqDxVRa7xwmDAbH0odyPyerhENMkw-bcuvTHKwfTfI7H2DyMczgt5gQPpZQ7sBsfcCyR0hhZmEYUszJV8ULB_2I5_fxrPj55fPN6mt1_f1qvbq8rgzn9VRRRywqVUumpKuts6IFaCF_rRPESemgA8YaypmjTDa25sQZ09GGoOiorM-Kd4tuSE7fGa8j-EPcRH2X9OWPm7VuVSsFbzO7Xlgb4VYPuSVIfw8Fh0RMGw25cdOjbgiFhpFGyvxiTiCQDhpppZJMWCEwa31atIZ9t0NrMEwJ-ieiT0-C3-Y7_daqZURymgXe3guk-GuP46R3-Zmx7yFg3I-aCULzvJSa0WpBTYrjmNAdf0OJnh2hZ0foB0dk_s3jux3ph_lnQC3AH-yiG42fHXDEsmWUyp2KOu8oX_npMPdV3Icpl77__9L6H7Xe04Y</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Yoon, Jaehong</creator><creator>Kim, Ji-Hwan</creator><creator>Chung, Yeonseung</creator><creator>Park, Jinsu</creator><creator>Sorensen, Glorian</creator><creator>Kim, Seung-Sup</creator><general>Korean Soc Epidemiology</general><general>Korean Society of Epidemiology</general><general>한국역학회</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><scope>ACYCR</scope><orcidid>https://orcid.org/0000-0002-1501-2747</orcidid><orcidid>https://orcid.org/0000-0003-1830-0282</orcidid><orcidid>https://orcid.org/0000-0001-9424-5962</orcidid><orcidid>https://orcid.org/0000-0001-6625-7931</orcidid><orcidid>https://orcid.org/0000-0003-4147-4758</orcidid><orcidid>https://orcid.org/0000-0002-5987-8913</orcidid></search><sort><creationdate>20210101</creationdate><title>Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach</title><author>Yoon, Jaehong ; Kim, Ji-Hwan ; Chung, Yeonseung ; Park, Jinsu ; Sorensen, Glorian ; Kim, Seung-Sup</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c443t-1f0de8837287f3dfd69aa9a69adf60f77faba225142f1275d340fccb150e6b173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Female</topic><topic>Humans</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Original</topic><topic>Public, Environmental & Occupational Health</topic><topic>Republic of Korea - epidemiology</topic><topic>Science & Technology</topic><topic>Sex Factors</topic><topic>social discrimination</topic><topic>social epidemiology</topic><topic>예방의학</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yoon, Jaehong</creatorcontrib><creatorcontrib>Kim, Ji-Hwan</creatorcontrib><creatorcontrib>Chung, Yeonseung</creatorcontrib><creatorcontrib>Park, Jinsu</creatorcontrib><creatorcontrib>Sorensen, Glorian</creatorcontrib><creatorcontrib>Kim, Seung-Sup</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>Korean Citation Index</collection><jtitle>Epidemiology and health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yoon, Jaehong</au><au>Kim, Ji-Hwan</au><au>Chung, Yeonseung</au><au>Park, Jinsu</au><au>Sorensen, Glorian</au><au>Kim, Seung-Sup</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach</atitle><jtitle>Epidemiology and health</jtitle><stitle>EPIDEMIOL HEALTH</stitle><addtitle>Epidemiol Health</addtitle><date>2021-01-01</date><risdate>2021</risdate><volume>43</volume><spage>e2021099</spage><epage>e2021099</epage><pages>e2021099-e2021099</pages><artnum>2021099</artnum><issn>2092-7193</issn><eissn>2092-7193</eissn><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.</abstract><cop>SUWON</cop><pub>Korean Soc Epidemiology</pub><pmid>34809416</pmid><doi>10.4178/epih.e2021099</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-1501-2747</orcidid><orcidid>https://orcid.org/0000-0003-1830-0282</orcidid><orcidid>https://orcid.org/0000-0001-9424-5962</orcidid><orcidid>https://orcid.org/0000-0001-6625-7931</orcidid><orcidid>https://orcid.org/0000-0003-4147-4758</orcidid><orcidid>https://orcid.org/0000-0002-5987-8913</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; KoreaMed Open Access; Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; PubMed Central |
| subjects | Female Humans Life Sciences & Biomedicine Machine Learning Male Original Public, Environmental & Occupational Health Republic of Korea - epidemiology Science & Technology Sex Factors social discrimination social epidemiology 예방의학 |
| title | Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach |
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