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on Gender |
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Issue of 2023‒06‒12
five papers chosen by Jan Sauermann Institutet för Arbetsmarknads- och Utbildningspolitisk Utvärdering |
| By: | Bernd Frick (Paderborn University); Clarissa Laura Maria Spiess Bru (Paderborn University); Daniel Kaimann (Paderborn University) |
| Abstract: | This study investigates whether women are more lenient in evaluating the performance of others. We examine the gender-specific behavior of female and male critics in expert evaluations, considering their allocated level of experience by using data from high-prestige wine assessments. We demonstrate that women rate, on average, less generously than men, even in direct comparison. In addition, we show that women with advanced experience levels are less generous than the most experienced same-sex reviewer, whereas this effect is not observed for men. Finally, controlling for self-selection into a particular field (i.e., wine critics), this study confirms previous findings using data, e.g., from professional sports: unobserved heterogeneity drives results generated in lab experiments. |
| Keywords: | Gender Differences, Information Asymmetry, Competitiveness, Overconfidence, Gender Bias, Reviews and Ratings |
| JEL: | J16 C33 L66 J2 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:dispap:106&r=gen |
| By: | Barbara Downs; Lucia Foster; Rachel Nesbit; Danielle H. Sandler |
| Abstract: | Existing work has shown that the entry of a child into a household results in a large and sustained increase in the earnings gap between male and female partners in opposite-sex couples. Potential reasons for this include work-life preferences, comparative advantage over earnings, and gender norms. We expand this analysis of the child penalty to examine earnings of individuals in same sex couples in the U.S. around the time their first child enters the household. Using linked survey and administrative data and event-study methodology, we confirm earlier work finding a child penalty for women in opposite-sex couples. We find this is true even when the female partner is the primary earner pre-parenthood, lending support to the importance of gender norms in opposite-sex couples. By contrast, in both female and male same-sex couples, earnings changes associated with child entry differ by the relative pre-parenthood earnings of the partners: secondary earners see an increase in earnings, while on average the earnings of primary and equal earners remain relatively constant. While this finding seems supportive of a norm related to equality within same-sex couples, transition analysis suggests a more complicated story. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:23-25&r=gen |
| By: | Yao Yao; Zheng Li |
| Abstract: | In contrast to most prior studies of gender inequality focusing on a specific country or a specific year, this paper uses cross-nationally comparable data from the Luxembourg Income Study (LIS) to examine the impacts of wage premiums in male- and female-dominated industries and education levels on gender inequality in five developed countries- the United States, the United Kingdom, Germany, Ireland, and Belgium from 2004 to 2017. To the best of our knowledge, there are no attempts in the prior empirical literature studying the effects of wage premiums in male- and female-dominated industries on gender inequality. To guarantee continuity and stability, we run the regression year by year separately for 14 consecutive periods for each of five advanced countries. The timeline covers the before, during, and after the great recession to rule out the possible effects of historical contingency. Thus, this is the first empirical paper to investigate the causal relationship between male- and female-dominated industries and gender inequality across counties over a continuous period. We raise and answer three research questions: (1) Do the wage premiums among male- and female-dominated industries affect the gender wage gap? (2) Is there a cross-country variation in the relationship between education levels and the gender wage gap? (3) Is there an impact of education levels on the gender employment gap? As for empirical analysis, for the first two questions, we run the multivariate linear regression; for the third question, we estimate the probit model, marginal effects, and the delta method standard errors. We find that: 1) There is a significant correlation between the wage premiums in female- and male-dominated industries and gender wage gap; 2) There is a crosscountry variation in the relationship between education levels and the gender wage gap; 3) There is also a cross-country variation in the relationship between education levels and the gender employment gap. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:lis:liswps:832&r=gen |
| By: | Mallory Avery (Department of Economics, Monash Business School, Monash University); Andreas Leibbrandt (Department of Economics, Monash Business School, Monash University); Joseph Vecci (Gothenburg University, Vasagatan, Gothenburg, Sweden) |
| 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 in recruitment impacts 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 this is driven by female jobseekers believing that there is less bias in recruitment 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 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:mos:moswps:2023-09&r=gen |
| By: | David G. Blanchflower; Alex Bryson |
| Abstract: | Given recent controversies about the existence of a gender wellbeing gap we revisit the issue estimating gender differences across 55 subjective well-being metrics – 37 positive affect and 18 negative affect – contained in 8 cross-country surveys from 167 countries across the world, two US surveys covering multiple years and a survey for Canada. We find women score more highly than men on all negative affect measures and lower than men on all but three positive affect metrics, confirming a gender wellbeing gap. The gap is apparent across countries and time and is robust to the inclusion of exogenous covariates (age, age squared, time and location fixed effects). It is also robust to conditioning on a wider set of potentially endogenous variables. However, when one examines the three ‘global’ wellbeing metrics - happiness, life satisfaction and Cantril’s Ladder - women are either similar to or ‘happier’ than men. This finding is insensitive to which controls are included and varies little over time. The difference does not seem to arise from measurement or seasonality as the variables are taken from the same surveys and frequently measured in the same way. The concern here though is that this is inconsistent with objective data where men have lower life expectancy and are more likely to die from suicide, drug overdoses and other diseases. This is the true paradox – morbidity doesn’t match mortality by gender. Women say they are less cheerful and calm, more depressed, and lonely, but happier and more satisfied with their lives, than men. |
| JEL: | I30 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31212&r=gen |