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Artificial Intelligence and the Gender Pay Gap in Labour Markets: Cross-Country Evidence and the Role of Gender-Sensitive Reforms

Author

Listed:
  • Aida Yzeiri Baftiari
  • Artina Kamberi
  • Agon Memeti

Abstract

This paper investigates whether the diffusion of artificial intelligence (AI) is associated with gendered labour market outcomes at the macro level and whether gender-sensitive institutional reforms mitigate gender inequality. We constructed a cross-country dataset covering up to 50 countries over the period 2012–2022. Given the rapid acceleration of AI adoption after 2022, the estimates should be interpreted as evidence for the 2012–2022 period and may not reflect the most recent wave of AI diffusion. We then estimate three complementary econometric models. First, using 500 promotion-related observations pooled across countries, we estimate a logistic regression to test whether AI adoption and an AI bias proxy are associated with women’s promotion probabilities (RQ1; H1a–H1b). Second, using a balanced panel of 10 countries observed over 10 years (N = 100), we estimate country fixed-effects models for female labour force participation and the gender pay gap (RQ2; H2a–H2c). Third, we employ a difference-in-differences specification on the same panel to assess the impact of gender-sensitive institutional reforms on the gender pay gap (RQ3; H3). Across the logit and fixed-effects models, coefficients on AI adoption and the AI bias indicator are small, statistically insignificant, and imprecisely estimated, providing no support for the hypotheses that AI diffusion widens gender gaps in promotions, participation, or wages. By contrast, the reform indicator in the difference-in-differences model is negative, sizable, and highly significant, indicating that treated countries experience an average reduction of about 6.4 percentage points in the gender pay gap relative to non-treated countries. The findings suggest that AI adoption, as currently measured, is not a detectable structural driver of gender inequality in labour markets at the country level, whereas gender-sensitive institutional reforms are empirically associated with improved wage outcomes for women.

Suggested Citation

  • Aida Yzeiri Baftiari & Artina Kamberi & Agon Memeti, 2026. "Artificial Intelligence and the Gender Pay Gap in Labour Markets: Cross-Country Evidence and the Role of Gender-Sensitive Reforms," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 90-113.
    • Handle: RePEc:bas:econst:y:2026:i:4:p:90-113
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    References listed on IDEAS

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    1. Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020. "The Managerial Effects of Algorithmic Fairness Activism," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 85-90, May.
    2. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
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    More about this item

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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