Gender pay gaps in economics: A deeper look at institutional factors

Abstract

Using rich data on graduate tenure-track faculty, we explore the gender pay gap in academic departments of economics and agricultural/applied economics and the differences between them. We find that the gender pay gaps in economics and agricultural/applied economics are 8.3% and 4.1%, respectively, controlling for faculty rank, experience, and university affiliation. The gender pay gap increases with rank and varies across institutions. Productivity is an important determinant of wages but it explains little of the gender pay gap.While the lower unexplained gap in agricultural/applied economics is laudable, a greater share of women who are assistant and associate professors is part of the explanation. Given institutional differences, we explore the extent to which institutional factors-differences in the returns to observed characteristics, such as rank; unobserved characteristics; and institutional differences in pay levels-contribute to the gender pay gap.

The Gender Pay Gap in Academia: Evidence from the Beedie School of Business

“…To ensure that our results held for the whole data panel, we also removed the rank controls in column 6 and found that the estimated coefficient for female academics was even larger and more significant (0.04, with a t-statistic of 2.91). 2 These findings are consistent with the aggregate evidence on the unexplained variation in the gender pay gap (Grove et al 2011;Maitra et al 2021;Kim et al 2023). We contribute to this literature by suggesting that the wage differential may not result from taste-based discrimination in an academic setting, but rather that the salary gap can be explained by factors related to teaching and research productivity.…”

“…The dependent variable is the log of the annual salary, for an individual i in year t. The primary coefficient of interest is β 1 , which represents the estimated wage gap for female (Female) relative to male academics. We then followed prior literature (i.e., Ward 2001;Sayre et al 2000;Chen and Crown 2019;Smith-Carrier et al 2021;Kim et al 2023) and added a set of control variables P it as proxies for productivity for an individual i in year t (obtained from Google Scholar, ResearchGate, and the most recent individual CVs), which included the following (up to year t): the log of citations (Cite), the number of books (#Books), the number of chapters/cases (#Ch/Ca), the number of publications in peer-reviewed journals (#PR Pubs), and the number of publications in journals on the Financial Times list (#FT Pubs); a set of control variables F it as proxies for full-time equivalency, which included (for year t) an indicator variable for part-time (PT), the number of semesters of unpaid leave (UL), the number of semesters of study leave (SL), the number of courses taught as an overload or bought out and not taught (OL), and the number of semesters of parental leave (PL); and a set of control variables X it , which included ethnicity controls (indicators for race/ethnicity-African American, Indian, Asian/Pacific Islander, Native American, and Other, including multiple and undisclosed ethnicities, with Caucasian as the omitted category), prior experience controls (indicators for prior academic experience--Prior AE, and prior work experience-Prior WE, and the number of years employed by SFU-#SFU), education controls (indicators for PhD degree-PhD, professional designation and certificate-Des/Cert), and rank and promotion controls (indicators for faculty rank: Assistant-Assist Prof, Associate-Assoc Prof, and Full-Full Prof, with Lecturer and Other-Lect/Other as the omitted category; and indicators for promotions from Lecturer, Assistant, and Associate levels). Table 1 presents the definitions for all the variables utilized in our analysis.…”

“…Examples of varied performance definitions include the number of publications and citations (Kim et al 2023), the research score (Brower and James 2020), the number of publications in the Brigham Young University (BYU) journals list (Wiedman 2020), and the "pipeline" effect (Momani et al 2019). Despite these different performance definitions, these studies only account for a small percentage of the overall gender pay gap (Kim et al 2023). Some findings indicate that the gap persists and, in certain instances, widens with rank.…”

“…Recent papers call for further research to examine more comprehensive data, particularly regarding the quality and quantity of the output. For instance, Kim et al (2023) argue that employing more detailed data and investigating whether there are higher returns on the quantity and quality of research in economics as opposed to agricultural/applied economics could shed light on the underlying factors contributing to the pronounced gender gap in economics.…”

The gender pay gap in UK universities 2004/5 to 2019/20

“…Lastly, Bandiera et al (2016) examined the gender pay gap at the LSE using internal research productivity scores, concluding that controlling for predicted REF scores had minimal effect on the pay gap, indicating lower pay for women with the same level of research productivity. Thus, as noted by Kim et. al.…”