Gender stereotypes in job advertisements: What do they imply for the gender salary gap?
Gender stereotypes, the assumptions concerning appropriate social roles for men and women, permeate the labor market. Analyzing information from over 2.5 million job advertisements on three different employment search websites in Mexico, exploiting approximately 235,00 that are explicitly gender-tar...
| Publicado en: | Journal of Labor Research Vol. 43; no. 1; pp. 65 - 103 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=156619954&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 156619954 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01953613 JLR jtl: Journal of Labor Research issn: 01953613 maglogo: N pubinfo: dt: Mar2022 vid: 43 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 156619954 10.1007/s12122-022-09331-4 ppf: 65 ppct: 38 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.4MB tig: atl: Gender stereotypes in job advertisements: What do they imply for the gender salary gap? aug: au: Arceo-Gomez, Eva O. Campos-Vazquez, Raymundo M. Badillo, Raquel Y. Lopez-Araiza, Sergio affil: Universidad Iberoamericana, Departamento de Economía, Prolongación Paseo de la Reforma 880, Lomas de Santa Fe, 01219, Mexico City, Mexico Centro de Estudios Económicos, El Colegio de México, Camino al Ajusco 20, Col. Pedregal de Santa Teresa, 10740, Mexico City, Mexico Banco de México, Cinco de Mayo 2, 06000, Mexico City, Mexico Instituto Tecnológico Autónomo de México, Mexico City, Mexico su: Mexico Wage differentials Gender stereotypes Gender inequality Social role Random forest algorithms Gender wage gap sug: subj: Wage differentials Gender stereotypes Gender inequality Social role Mexico Random forest algorithms Gender wage gap keyword: Big data Discrimination Machine learning Salary gap Big data Discrimination Machine learning Salary gap ab: Gender stereotypes, the assumptions concerning appropriate social roles for men and women, permeate the labor market. Analyzing information from over 2.5 million job advertisements on three different employment search websites in Mexico, exploiting approximately 235,00 that are explicitly gender-targeted, we find evidence that advertisements seeking "communal" characteristics, stereotypically associated with women, specify lower salaries than those seeking "agentic" characteristics, stereotypically associated with men. Given the use of gender-targeted advertisements in Mexico, we use a random forest algorithm to predict whether non-targeted ads are in fact directed toward men or women, based on the language they use. We find that the non-targeted ads for which we predict gender show larger salary gaps (8–35 percent) than explicitly gender-targeted ads (0–13 percent). If women are segregated into occupations deemed appropriate for their gender, this pay gap between jobs requiring communal versus agentic characteristics translates into a gender pay gap in the labor market. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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