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Gender Gaps in Economics: Trends, Perceptions, and Paths Forward

Paper Session

Saturday, Jan. 3, 2026 2:30 PM - 4:30 PM (EST)

Philadelphia Convention Center, 204-A
Hosted By: American Economic Association & Committee on the Status of Women in the Economics Profession
  • Chair: Donna Ginther, University of Kansas

Information Bias and Selection of Female Professors

Stephanie El Khoury
,
Arizona State University

Abstract

Individuals often rely on crowd-generated ratings to form beliefs and guide decisions, yet these signals can embed bias. In higher education, students frequently consult online teaching evaluations when selecting college instructors. However, these ratings systematically disadvantage female faculty, reflecting the existence of gender biases. Using original surveys administered at one of the largest U.S. public universities, I document the existence of this bias, showing that female professors are rated 4.5% lower than their male counterparts. I then examine how this bias shapes student beliefs and course selection. While students consistently value higher-rated professors, they are largely unaware of the underlying bias and thus fail to adjust their beliefs and choices accordingly. To mitigate these effects, I implement an informational intervention that corrects students’ beliefs. This intervention leads to a 10% increase in students’ valuation of female instructors (relative to course cost), shifts enrollment decisions in favor of women, and eliminates the tendency to leave biased ratings on evaluation platforms. To deepen understanding of student decision-making, I develop and estimate a Bayesian updating model that captures how students form expectations of professor quality from biased signals. The analysis reveals that unawareness of bias drives statistical discrimination, leading students to expect lower quality from female compared to male professors for the same ratings. Finally, I document tangible losses in U.S. dollars arising from suboptimal enrollment decisions under biased beliefs. Together, these findings underscore the potential for informational interventions to reduce statistical discrimination and improve decision quality.

Demographic Differences in Letters of Recommendation for Economics Ph.D. Students

Anna Kovner
,
Federal Reserve Bank of Richmond
Beverly Hirtle
,
Federal Reserve Bank of New York

Abstract

We analyze 6,400 letters of recommendation for more than 2,200 economics and finance Ph.D. graduates from 2018 to 2021. Letter text varies significantly by field of interest, with significantly less positive and shorter letters for Macroeconomics and Finance candidates. Letters for female and Black or Hispanic job candidates are weaker in some dimensions, while letters for Asian candidates are notably less positive overall. We introduce a new measure of letter quality capturing candidates that are recommended to “top” departments. Female and Asian candidates are less likely to be recommended to top academic departments, even after controlling for other letter characteristics. Finally, we examine early career outcomes and find that letter characteristics, especially a “top” recommendation, have meaningful effects on initial job placements and journal publications.

Gender Biased Resistance to Harsh Feedback

Garrison Pollard
,
University of Florida
Thomas Knight
,
University of Florida
Mark Rush
,
University of Florida
Perihan Saygin
,
Universitat Autonoma de Barcelona

Abstract

Responses to performance feedback play a critical role in shaping future outcomes in educational and professional contexts. This paper examines whether evaluator gender influences the likelihood that individuals contest feedback. Using an experiment conducted in large introductory economics courses, we exploit the random assignment of evaluators with randomly assigned male- or female-sounding names to identify a systematic gender bias: individuals are significantly more likely to contest feedback when it is delivered by an evaluator with a female-sounding name than when similar feedback comes from a male-sounding evaluator. This gender disparity is most pronounced when evaluations fall below students’ performance expectations, are more ambiguous, yet are scored neutrally relative to a "fair" assessment. These findings suggest that women in evaluative positions face disproportionate resistance when delivering negative assessments and have implications for their authority, credibility, and career advancement in both educational and workplace settings.

Pipelines or Pyramids: Unequal Progress and Barriers for Women in Economics

Ondine Berland
,
London School of Economics
Oliver Harman
,
University of Oxford
Ninon Moreau Kastler
,
Paris School of Economics

Abstract

This paper reviews the literature on gender inequalities in the academic field of economics, highlighting both the unequal progress women have experienced and the persistent barriers they continue to face. Our analysis of recent literature and available data highlights that the reduction in women's absence in the field has been heterogeneous. While women now constitute 26% of published economists, their presence sharply declines at higher ranks, with only 5% representation among economists in the top percentile of research influence. Notably, recent progress has paradoxically concentrated at this highest level, indicating shifting institutional norms and evolving perceptions about women's contributions. We further explore how understanding women's behaviours and career choices, gender biases in evaluation, and workplace environments contribute to women's underrepresentation in the field. Our discussion extends to the labour market for economists, drawing parallels with broader labour trends, and examining how gender-biased treatment and hiring processes impact women's advancement. Finally, we turn to policy interventions, which are key in promoting gender balance, focusing on networks, mentorship, role models, and representation.

Discussant(s)
Sarah Reber
,
Brookings Institution
Evelyn Skoy
,
Hamilton College
Pallab Ghosh
,
University of Oklahoma
Donna Ginther
,
University of Kansas
JEL Classifications
  • J1 - Demographic Economics
  • J1 - Demographic Economics