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on Gender |
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Issue of 2024‒04‒29
four papers chosen by Jan Sauermann, Institutet för Arbetsmarknads- och Utbildningspolitisk Utvärdering |
| By: | Rachel Schuh |
| Abstract: | I analyze the value workers ascribe to the gender composition of their workplace and the consequences of these valuations for occupational segregation, tipping, and welfare. To elicit these valuations, I survey 9, 000 U.S. adults using a hypothetical job choice experiment. This reveals that on average women and men value gender diversity, but these average preferences mask substantial heterogeneity. Older female workers are more likely to value gender homophily. This suggests that gender norms and discrimination, which have declined over time, may help explain some women’s desire for homophily. Using these results, I estimate a structural model of occupation choice to assess the influence of gender composition preferences on gender sorting and welfare. I find that workers’ composition valuations are not large enough to create tipping points, but they do reduce female employment in male-dominated occupations substantially. Reducing segregation could improve welfare: making all occupations evenly gender balanced improves utility as much as a 0.4 percent wage increase for women and a 1 percent wage increase for men, on average. |
| Keywords: | gender; labor; occupational choice |
| JEL: | J16 J24 J71 |
| Date: | 2024–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:98021&r=gen |
| By: | Shibashish Mukherjee (EM - EMLyon Business School); Sorin M.S. Krammer |
| Abstract: | Gender diversity on corporate boards continues to present a significant challenge, exacerbated by significant external disruptions such as financial crises or the recent COVID-19 pandemic. These exogenous shocks pressure organizations to reconcile diversity imperatives with more immediate concerns arising from the crises at hand. Employing elements from gender role and institutional theories, we argue that major exogenous shocks will negatively affect (i.e., reduce) gender diversity in corporate boards. Moreover, we propose that female CEOs and the strength of institutional mechanisms (i.e., quotas and corporate governance codes) will moderate (i.e., weaken) the negative effect of these shocks on board gender diversity. We examine these hypotheses in the context of the last global financial crisis (GFC), employing a panel of 10, 181 unique firms across 21 countries between 2000 and 2015. We apply a two-way fixed effect difference-in-difference research design, complemented by an extensive battery of additional analyses to ensure robustness. Our results confirm a substantial decline in board gender diversity following the GFC. However, we do not find empirical support for female CEOs or institutional mechanisms in mitigating these diversity reductions. Following these findings, we propose several implications for research and policy. |
| Keywords: | Board gender diversity, Global financial crisis, Female CEO, Gender quotas, Corporate governance codes, Difference-in-difference |
| Date: | 2024–03–23 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04522722&r=gen |
| By: | Alexander Berry; Elizabeth M. Maloney; David Neumark |
| Abstract: | Stronger enforcement of discrimination laws can help to reduce disparities in economic outcomes with respect to race, ethnicity, and gender in the United States. However, the data necessary to detect possible discrimination and to act to counter it is not publicly available – in particular, data on racial, ethnic, and gender disparities within specific companies. In this paper, we explore and develop methods to use information extracted from publicly available LinkedIn data to measure the racial, ethnic, and gender composition of company workforces. We use predictive tools based on both names and pictures to identify race, ethnicity, and gender. We show that one can use LinkedIn data to obtain reasonably reliable measures of workforce demographic composition by race, ethnicity, and gender, based on validation exercises comparing estimates from scraped LinkedIn data to two sources – ACS data, and company diversity or EEO-1 reports. Next, we apply our methods to study the race, ethnic, and gender composition of workers who were hired and those who experienced mass layoffs at two large companies. Finally, we explore using LinkedIn data to measure race, ethnic, and gender differences in promotion. |
| JEL: | J15 J16 J7 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32294&r=gen |
| By: | Jiafu An; Difang Huang; Chen Lin; Mingzhu Tai |
| Abstract: | In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making. How would this impact the distributional outcomes across social groups? Here we investigate the gender and racial biases of OpenAIs GPT, a widely used LLM, in a high stakes decision making setting, specifically assessing entry level job candidates from diverse social groups. Instructing GPT to score approximately 361000 resumes with randomized social identities, we find that the LLM awards higher assessment scores for female candidates with similar work experience, education, and skills, while lower scores for black male candidates with comparable qualifications. These biases may result in a 1 or 2 percentage point difference in hiring probabilities for otherwise similar candidates at a certain threshold and are consistent across various job positions and subsamples. Meanwhile, we also find stronger pro female and weaker anti black male patterns in democratic states. Our results demonstrate that this LLM based AI system has the potential to mitigate the gender bias, but it may not necessarily cure the racial bias. Further research is needed to comprehend the root causes of these outcomes and develop strategies to minimize the remaining biases in AI systems. As AI based decision making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15281&r=gen |