|
on Human Capital and Human Resource Management |
| By: | Julián Messina (University of Alicante & IZA); Anna Sanz-de-Galdeano (University of Alicante & IZA); Anastasia Terskaya (Universitat de Barcelona & IEB) |
| Abstract: | Utilizing comprehensive administrative data from Brazil, we investigate the impact of peer effects on wages, considering both within-gender and cross-gender dynamics. Since the average productivity of both individuals and their peers is unobservable, we estimate these values using worker fixed effects while accounting for occupational and firm sorting. Our findings reveal that within-gender peer effects have approximately twice the influence of cross-gender peer effects on wages for both males and females. Furthermore, we observe a reduction in the disparity between these two types of peer effects in settings characterized by greater gender equality. |
| Keywords: | Peer effects; Gender; Matched employer-employee data; Identity; Wage determination |
| JEL: | J16 J24 J31 M12 M54 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ieb:wpaper:doc2023-10&r=hrm |
| By: | Enzo Brox; Michael Lechner |
| Abstract: | This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high-quality data on football matches to measure an important component of individual performance, shooting performance, isolated from collaborative effects. Employing causal machine learning methods, we address the assortative matching of workers and estimate both average and heterogeneous effects. There is substantial evidence for spillover effects in performance evaluations. Coworker shooting performance, meaningfully impacts both, manager decisions and third-party expert evaluations of individual performance. Our results underscore the significant role coworkers play in shaping career advancements and highlight a complementary channel, to productivity gains and learning effects, how coworkers impact career advancement. We characterize the groups of workers that are most and least affected by spillover effects and show that spillover effects are reference point dependent. While positive deviations from a reference point create positive spillover effects, negative deviations are not harmful for coworkers. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15200&r=hrm |
| By: | Mallory Avery; Andreas Leibbrandt; Joseph Vecci |
| Abstract: | The use of Artificial Intelligence (AI) in recruitment is rapidly increasing and drastically changing how people apply to jobs and how applications are reviewed. In this paper, we use two field experiments to study how AI recruitment tools can impact gender diversity in the male-dominated technology sector, both overall and separately for labor supply and demand. We find that the use of AI in recruitment changes the gender distribution of potential hires, in some cases more than doubling the fraction of top applicants that are women. This change is generated by better outcomes for women in both supply and demand. On the supply side, we observe that the use of AI reduces the gender gap in application completion rates. Complementary survey evidence suggests that anticipated bias is a driver of increased female application completion when assessed by AI instead of human evaluators. On the demand side, we find that providing evaluators with applicants’ AI scores closes the gender gap in assessments that otherwise disadvantage female applicants. Finally, we show that the AI tool would have to be substantially biased against women to result in a lower level of gender diversity than found without AI. |
| Keywords: | artificial intelligence, gender, diversity, field experiment |
| JEL: | C93 J23 J71 J78 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10996&r=hrm |
| 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=hrm |
| By: | Darova, Ornella; Duchene, Anne |
| Abstract: | We run two field experiments on team diversity in a large undergraduate economics class. Small groups with random compositions are generated and assigned team tasks. In the first experiment, tasks are creative and complex, while in the second one they are more standard. We use a multidimensional measure of diversity based on gender, race, and migration status. We estimate its impact on teamwork quality and group performance. We find a significant U-shaped effect on teamwork quality in both experiments. However, the impact on performance depends on the type of task: it is positive for creative tasks, but negative for standard ones. We interpret these results as the consequence of two conflicting forces: diversity is a source of creativity, but it can hamper communication and coordination between team members. When tasks are creative, the first (positive) force dominates; for standard tasks, instead, communication challenges do. The U-shaped impact on teamwork quality suggests that faultlines – dividing lines that split a group into subgroups based on demographic characteristics – can cause inter-subgroup cohesion to break down, while very homogeneous or very heterogeneous groups collaborate better. These results allow us to build a comprehensive framework to better understand the impact of diversity on teamwork. |
| Keywords: | Diversity, Knowledge Production, Creativity, Teamwork, Education |
| JEL: | A22 I21 J15 |
| Date: | 2024–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120519&r=hrm |
| 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=hrm |