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Gender stereotypes in job advertisements: What do they imply for the gender salary gap?

Author

Listed:
  • Eva O. Arceo-Gomez

    (Universidad Iberoamericana, Departamento de Economía)

  • Raymundo M. Campos-Vazquez

    (El Colegio de México)

  • Raquel Y. Badillo

    (Banco de México)

  • Sergio Lopez-Araiza

    (Instituto Tecnológico Autónomo de México)

Abstract

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.

Suggested Citation

  • Eva O. Arceo-Gomez & Raymundo M. Campos-Vazquez & Raquel Y. Badillo & Sergio Lopez-Araiza, 2022. "Gender stereotypes in job advertisements: What do they imply for the gender salary gap?," Journal of Labor Research, Springer, vol. 43(1), pages 65-102, March.
    • Handle: RePEc:spr:jlabre:v:43:y:2022:i:1:d:10.1007_s12122-022-09331-4
    DOI: 10.1007/s12122-022-09331-4
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    References listed on IDEAS

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    Cited by:

    1. Suguru Otani, 2024. "Nonparametric Estimation of Matching Efficiency and Elasticity on a Private On-the-Job Search Platform: Evidence from Japan, 2014-2024," Papers 2410.17011, arXiv.org, revised Dec 2024.
    2. Kelvin Leong & Anna Sung, 2024. "Gender stereotypes in artificial intelligence within the accounting profession using large language models," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    3. Araujo, María Caridad & Bastias-Butler Elizabeth & Bauhoff, Sebastian & Berlinski, Samuel & Bosch, Mariano & Busso, Matías & Cristia, Julian P. & Cruz-Aguayo, Yyannu & Duryea, Suzanne & Etcheverry, Lu, 2024. "Expanding Opportunities: Policies for Gender Equality and Inclusion," IDB Publications (Books), Inter-American Development Bank, number 13841, December.
    4. Kimberly Scharf & Oleksandr Talavera & Linh Vi, 2023. "Gender Differences in Returns to Beauty," Discussion Papers 23-08, Department of Economics, University of Birmingham.

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    More about this item

    Keywords

    Gender stereotypes; Salary gap; Discrimination; Big data; Machine learning; Mexico;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • O54 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Latin America; Caribbean

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