Can AI Bridge the Gender Gap in Competitiveness? - Evangelos Mourelatos; Panagiotis Zervas; Dimitris Lagios; Giannis Tzimas (Global Labor Organization (GLO), 2024)
This paper employs an online real-effort experiment to investigate gender disparities in the selection of individuals into competitive working environments when assisted by artificial intelligence (AI). In contrast to previous research suggesting greater competitiveness among men, our findings reveal that both genders are equally likely to compete in the presence of AI assistance. Surprisingly, the introduction of AI eliminates an 11-percentage-point gender gap, between men and women in our competitive scenario. We also discuss how the gender gap in tournament entry appears to be contingent on ChatGPT selection rather than being omnipresent. Notably, 47% of female participants independently chose to utilize ChatGPT, while 55% of males did the same. However, when ChatGPT was offered by the experimenter-employer, more than 53% of female participants opted for AI assistance, compared to 57% of males, in a gender-neutral online task. This shift prompts a reevaluation of gender gap trends in competition entry rates, particularly as women increasingly embrace generative AI tools, resulting in a boost in their confidence. We rule out differences in risk aversion. The discussion suggests that these behavioral patterns may have significant policy implications, as the introduction of generative AI tools in the workplace can be leveraged to rectify gender disparities.
Keywords: C90; J16; J71; 330; Gender differences; ChatGPT; Competition; Economic experiments