Understanding the Gender Gap: An Economic History of American Women.

Kleinberg, S. J.; Goldin, Claudia

doi:10.2307/2233578

Cited by 183 publications

(4 citation statements)

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“…Gender gaps in pay have proven to be persistent (Goldin, 1990; Blau & Kahn, 2017), and large in academia. Moreover, the number of women in traditionally higher‐paying academic fields such as economics, science, and engineering has stagnated since 2000 (Ceci et al., 2014; Kahn & Ginther, 2017; Lundberg & Stearns, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Chen

Weinberg

2023

Agricultural Economics

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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.

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Section: Introductionmentioning

confidence: 99%

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

Kim

Chen

Weinberg

2023

Agricultural Economics

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“…Akerlor and Kranton's study [9] suggests that individuals experience a drop in utility when they deviate from societal expectations, leading to diminished female engagement in the labor field. This model also provides an explanation for occupational segregation based on gender, as supported by Goldin [22], Altonji and Blank [11], and Bertrand et al's findings [23].…”

Section: Hypothesis Developmentmentioning

confidence: 68%

CEO Gender and Firms Adoption of Artificial Intelligence Technology

Yi,

Wang,

Liu

2024

AEMPS

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In this rapidly evolving era, AI is an emerging industry with a significant presence in various industries. Factors affecting the extent to which AI is used in companies have been explored by many researchers, but the factor of CEO gender is rarely mentioned. To test the effect of CEO gender on the degree of corporate use of AI, this paper analyzes the text of corporate annual reports to measure the degree of each company's use of AI by counting the number of times the term AI appears in each annual report. The CEO gender information is manually collected to construct an indicator of the degree of corporate adoption of AI. In this paper, a theoretical hypothesis of the relationship between CEO gender and the degree of corporate use of AI based on the different attitudes of males and females towards ethical behavior and risk will be provided. Then, accorded with the panel data about companies from Chinese a-share non-financial, which was listed from 2000 - 2021, a two-way fixed-effects regression model is used to analyze the effect of CEO gender on the degree of firms' use of AI technology. The study results show that AI usage is higher in male than female CEOs. Based on the above findings, this paper not only reveals the influence of CEO gender on the extent of firms' AI use but also deepens the understanding of the differences in gender characteristics between males and females.

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“…Increasing women's labour force participation, historically and in the last decades, and across societies, has been explained mainly (but not exclusively) by economic changes such as sectoral shifts towards light manufacturing and services, where women have a comparative advantage (in comparison with labour‐intensive agriculture or heavy industry). Attitudinal change (in favour of women's paid employment and other gender equality issues) happened only later on (if at all in the short‐ and medium‐term) (Goldin, 1990; Heath & Jayachandran, 2016; Rindfuss et al., 1996). This is because traditional norms, including gender ideologies, tend be rather ‘sticky’ and durable.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Gender balance in the workforce and abortion attitudes: A cross‐national time‐series analysis

Fernández

Valiente

Castillo

2023

British Journal of Sociology

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This article explores the relationship between gender balance in the workforce and attitudes towards abortion worldwide. Studies on macro‐level conditions related to abortion attitudes overlook the role of gender balance in the workforce—specifically the degree of female representation in a country's workforce. There are strong reasons why this factor could shape abortion attitudes. We argue that such a gender balance creates necessary conditions to break with traditional, anti‐abortion ideology and facilitates dissemination and public acceptance of pro‐choice views. We test this argument with two different datasets ‐ the Integrated Values Survey and three waves of the International Social Survey Programme—along with two outcomes: general tolerance towards abortion and tolerance towards abortion for pregnant women of low income. Using three‐level random intercept models and multiple controls for individual and country‐level conditions, the results support our hypothesis: In countries with higher gender balance in the workforce, individuals display higher tolerance towards abortion.

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Understanding the Gender Gap: An Economic History of American Women.

Cited by 183 publications

References 0 publications

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

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

CEO Gender and Firms Adoption of Artificial Intelligence Technology

Gender balance in the workforce and abortion attitudes: A cross‐national time‐series analysis

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