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on Human Capital and Human Resource Management |
| By: | Li, Jiang (Statistics Canada); Dostie, Benoit (HEC Montreal); Simard-Duplain, Gaëlle (University of British Columbia) |
| Abstract: | Using data from the Canadian Employer-Employee Dynamics Database between 2001 and 2015, we examine the impact of firms' hiring and pay-setting policies on the gender earnings gap in Canada. Consistent with the existing literature and following Card, Cardoso, and Kline (2016), we find that firm-specific premiums explain nearly one quarter of the 26.8% average earnings gap between female and male workers. On average, firms' hiring practices – due to difference in the relative proportion of women hired at high-wage firms, or sorting – and pay-setting policies – due to differences in pay by gender within similar firms – each explain about one half of this firm effect. The compositional difference between the two channels varies substantially over the life-cycle, by parental and marital status, and across provinces. |
| Keywords: | gender wage gap, firm effects, marital status, linked employer-employee data, pay-setting, sorting |
| JEL: | J16 J31 J51 J71 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13907&r=all |
| By: | Brañas-Garza, Pablo; Mesa-Vázquez, Ernesto; Rivero-Garrido, Noelia |
| Abstract: | This paper explores gender differences in overplacement in two independent and unrelated tasks. The first measures performance via Raven’s Progressive Matrices test, the second in a video presentation assessed by external judges. While in the first task, we expected participants to have prior knowledge about their own experience in similar tasks, we did not expect them to have experience of the second task. Therefore, the latter seems an ideal environment in which to test overplacement given that participants had no ex-ante information with which to make performance predictions. In both cases, participants received monetary incentives depending on the accuracy of their predictions regarding their own performance compared to other participants. We analyzed overplacement – whether participants expect to outperform their actual performance compared to the entire sample – and in/out-group overplacement– whether the participants expect to outperform participants of the same and the opposite sex. Results show that there are no gender differences in any task except in Raven’s Progressive Matrices for out-group overplacement. |
| Keywords: | Overplacement, gender, experiments, in-group, out-group |
| JEL: | D84 D91 J16 |
| Date: | 2020–12–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104426&r=all |
| By: | Michael Thaler |
| Abstract: | Men and women systematically differ in their beliefs about their performance relative to others; in particular, men tend to be more overconfident. This paper provides support for one explanation for gender differences in overconfidence, performance-motivated reasoning, in which people distort how they process new information in ways that make them believe they outperformed others. Using a large online experiment, I find that male subjects distort information processing to favor their performance, while female subjects do not systematically distort information processing in either direction. These statistically-significant gender differences in performance-motivated reasoning mimic gender differences in overconfidence; beliefs of male subjects are systematically overconfident, while beliefs of female subjects are well-calibrated on average. The experiment also includes political questions, and finds that politically-motivated reasoning is similar for both men and women. These results suggest that, while men and women are both susceptible to motivated reasoning in general, men find it particularly attractive to believe that they outperformed others. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.01538&r=all |
| By: | Bo Cowgill; Fabrizio Dell'Acqua; Samuel Deng; Daniel Hsu; Nakul Verma; Augustin Chaintreau |
| Abstract: | Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from $\approx$400 AI engineers, each of whom developed an algorithm under a randomly assigned experimental condition. Our treatment arms modified programmers' incentives, training data, awareness, and/or technical knowledge of AI ethics. We then assess out-of-sample predictions from their algorithms using randomized audit manipulations of algorithm inputs and ground-truth math performance for 20K subjects. We find that biased predictions are mostly caused by biased training data. However, one-third of the benefit of better training data comes through a novel economic mechanism: Engineers exert greater effort and are more responsive to incentives when given better training data. We also assess how performance varies with programmers' demographic characteristics, and their performance on a psychological test of implicit bias (IAT) concerning gender and careers. We find no evidence that female, minority and low-IAT engineers exhibit lower bias or discrimination in their code. However, we do find that prediction errors are correlated within demographic groups, which creates performance improvements through cross-demographic averaging. Finally, we quantify the benefits and tradeoffs of practical managerial or policy interventions such as technical advice, simple reminders, and improved incentives for decreasing algorithmic bias. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.02394&r=all |
| By: | Ioana Marinescu; Ivan Ouss; Louis-Daniel Pape |
| Abstract: | How does employer market power affect workers? We compute the concentration of new hires by occupation and commuting zone in France using linked employer-employee data. Using instrumental variables with worker and firm fixed effects, we find that a 10% increase in labor market concentration decreases hires by 12.4% and the wages of new hires by nearly 0.9%, as hypothesized by monopsony theory. Based on a simple merger simulation, we find that a merger between the top two employers in the retail industry would be most damaging, with about 24 million euros in annual lost wages for new hires, and an 8000 decrease in annual hires. |
| JEL: | J23 J3 J42 K21 L13 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28084&r=all |
| By: | Tim Lohse; Salmai Qari |
| Abstract: | We study the role of face-to-face interaction for gender differences in deceptive behavior and perceived honesty. In the first part, we compare women to men’s deceptive behavior using data from an incentivized income-reporting experiment with three treatments. Reporting is fully computerized in a baseline treatment but occurs face-to-face in the second and third treatment. Lies can be detected in the course of an audit, which happens with a given probability in the first and second treatment whereas it depends on perceptions by others in the third treatment. In the computerized baseline treatment, men and women’s deceptive behavior is statistically indistinguishable. However, women’s truthfulness increases when face-to-face interaction is introduced in the second treatment. In contrast, males’ deceptive behavior does not change until the audit probability depends on their perceived honesty in the third treatment. Then, men’s truthfulness rises sharply and exceeds women’s level of honesty by far. We elaborate on these gender differences in the second part. We conduct an experiment to assess the honesty of videotaped income-reporting statements from a setting identical to the third treatment. Our findings confirm that men anticipate their low perceived honesty, which is consistent with the results from the first part. |
| Keywords: | Gender differences, lying, face-to-face interaction, honesty assess- ment, perception, video analysis, laboratory experiment |
| JEL: | C91 D91 J16 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1922&r=all |
| By: | C. Bram Cadsby (Department of Economics and Finance, University of Guelph, Guelph ON Canada); Fei Song (Ted Rogers School of Management, Ryerson University, Toronto ON Canada); Nick Zubanov (University of Konstanz, Konstanz, Germany) |
| Abstract: | We examine the response of labor supply to short-run wage changes with and without a reference wage (RW) that we manipulate experimentally. We find that, in the absence of RW, labor supply increases monotonically with wage. In contrast, when RW is present, people work more both when wages rise and fall relative to RW. These findings suggest a kink in the labor-supply curve, consistent with income targeting by loss-averse individuals. However, the effects of income targeting are sensitive to context: in a treatment where wages could either rise or fall relative to RW, the kink in the labor-supply curve disappears. |
| Keywords: | labor supply, short-run wage changes, reference wage, loss-aversion, experimental |
| JEL: | D91 J22 J31 M52 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:gue:guelph:2020-06&r=all |
| By: | Bach, Maximilian; Fischer, Mira |
| Abstract: | This paper studies responses to high-stakes incentives arising from early ability tracking. We use three complementary research designs exploiting differences in school track admission rules at the end of primary school in Germany's early ability tracking system. Our results show that the need to perform well to qualify for a better track raises students' math, reading, listening, and orthography skills in grade 4, the final grade before students are sorted into tracks. Evidence from self-reported behavior suggests that these effects are driven by greater study effort but not parental responses. However, we also observe that stronger incentives decrease student well-being and intrinsic motivation to study. |
| Keywords: | Student Effort,Tracking,Incentives |
| JEL: | I20 I28 I29 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbmbh:spii2020202&r=all |