Arbeitspapier

Can AI Bridge the Gender Gap in Competitiveness?

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.

Sprache
Englisch

Erschienen in
Series: GLO Discussion Paper ; No. 1404

Klassifikation
Wirtschaft
Design of Experiments: General
Economics of Gender; Non-labor Discrimination
Labor Discrimination
Thema
Gender differences
ChatGPT
Competition
Economic experiments

Ereignis
Geistige Schöpfung
(wer)
Mourelatos, Evangelos
Zervas, Panagiotis
Lagios, Dimitris
Tzimas, Giannis
Ereignis
Veröffentlichung
(wer)
Global Labor Organization (GLO)
(wo)
Essen
(wann)
2024

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Mourelatos, Evangelos
  • Zervas, Panagiotis
  • Lagios, Dimitris
  • Tzimas, Giannis
  • Global Labor Organization (GLO)

Entstanden

  • 2024

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