nep-hrm New Economics Papers
on Human Capital and Human Resource Management
Issue of 2025–11–03
seven papers chosen by
Patrick Kampkötter, Eberhard Karls Universität Tübingen


  1. How AI-Augmented Training Improves Worker Productivity By Fouarge, Didier; Fregin, Marie-Christine; Janssen, Simon; Levels, Mark; Montizaan, Raymond; Özgül, Pelin; Rounding, Nicholas; Stops, Michael
  2. Freed from the Boys: How Single-Sex Schooling Shapes Girls’ Effort and Performance in High-Stakes Exams By Calsamiglia, Caterina; Fawaz, Yarine; Fernández-Kranz, Daniel; Lee, Junhee
  3. Sustainable Human Capital Management, ESG, and Firm Performance: Moderating Role of ESG Disclosure By Stela Jorgji; Jonida Teta; Saeed Mousa; Vadim Ponkratov; Izabella Elyakova; Larisa Vatutina; Andrey Pozdnyaev; Tatiana Chernysheva; Elena Romanenko; Mikhail Kosov
  4. Beliefs about Bots: How Employers Plan for AI in White-Collar Work By Brüll, Eduard; Mäurer, Samuel; Rostam-Afschar, Davud
  5. Unemployment Insurance and Worker Reallocation By Grindaker, Morten; Simmons, Michael
  6. Subtle discrimination By Pikulina, Elena S.; Ferreira, Daniel
  7. Career Concerns in Collective Decision-Making: The Federal Open Market Committee By Matias Iaryczower; Gabriel Lopez-Moctezuma; Paola Moscariello

  1. By: Fouarge, Didier (ROA, Maastricht University); Fregin, Marie-Christine (Maastricht University); Janssen, Simon (Institute for Employment Research (IAB), Nuremberg); Levels, Mark (Maastricht University); Montizaan, Raymond (ROA, Maastricht University); Özgül, Pelin (Maastricht University); Rounding, Nicholas (Maastricht University); Stops, Michael (Institute for Employment Research (IAB), Nuremberg)
    Abstract: We analyze the impact of AI-augmented training on worker productivity in a financial services company. The company introduced an AI tool that provides performance feedback on call center agents to guide their training. To estimate causal effects, we exploit the staggered roll out of the AI-tool. The AI-augmented training reduces call handling time by 10 percent. We find larger effects for short-tenured workers because they spend less time putting clients on hold. But the AI-augmented training also improves communication style with relatively stronger effects for long-tenured agents, and we find slightly positive effects on customer satisfaction.
    Keywords: performance feedback, training, artificial intelligence, employee productivity
    JEL: J24 O31 O33
    Date: 2025–10
    URL: https://d.repec.org/n?u=RePEc:iza:izadps:dp18224
  2. By: Calsamiglia, Caterina (IPEG); Fawaz, Yarine (CEMFI, Madrid); Fernández-Kranz, Daniel (IE University); Lee, Junhee
    Abstract: Prior research has found that boys often outperform girls in high-stakes math exams, raising the question of whether these gender differences under pressure stem from nature or nurture. This relative female disadvantage can influence access to selective university programs and subsequent career paths. Using administrative and survey data linked to a lottery-based school assignment system, we show that this disadvantage is reversed in single-sex schools: girls randomly assigned to SS schools devote more effort, outperform boys in high-stakes math exams, and have a higher likelihood of enrolling in university STEM degrees (excluding biology). These positive effects come at a cost to well-being in terms of higher stress and worse mental health. These effects are not driven by differences in teacher gender or school resources due to public versus private management. Our findings are consistent with theories emphasizing the social costs of norm violation: in single-sex schools, girls are freed from peer norms that may otherwise discourage overt academic ambition, allowing them to sustain higher effort in competitive and male-dominated domains.
    Keywords: Korea, high-stakes exams, education, nurture, single-sex schooling, random assignment, gender, gender gap, natural experiment
    JEL: I21 J16 I24 D91 J24 I28
    Date: 2025–10
    URL: https://d.repec.org/n?u=RePEc:iza:izadps:dp18208
  3. By: Stela Jorgji (University of Tirana); Jonida Teta (University of Tirana); Saeed Mousa (ESC [Rennes] - ESC Rennes School of Business); Vadim Ponkratov; Izabella Elyakova; Larisa Vatutina; Andrey Pozdnyaev; Tatiana Chernysheva (MSU - Lomonosov Moscow State University = Université d'État Lomonossov de Moscou [Moscou]); Elena Romanenko; Mikhail Kosov (PRUE - Plekhanov Russian University of Economics [Moscow])
    Abstract: This study investigates the relationships between sustainable human capital management practices, ESG performance, ESG disclosure, and firm financial performance. Using a sample of 387 S&P 500 firms from 2013 to 2023 and a panel data regression approach, we examine the impact of training expenditure, workforce diversity and inclusion, pay equity, and employee benefits on ESG performance. We also explore the association between ESG performance and ESG disclosure, the effect of ESG performance on financial performance, and the moderating role of ESG disclosure in the ESG-financial performance relationship. Our findings reveal that sustainable human capital management practices have a positive and significant impact on ESG performance, which in turn positively influences firm financial performance. We also find a positive relationship between ESG performance and ESG disclosure, and that ESG disclosure moderates the ESG-financial performance link, with the positive association being stronger for firms with higher levels of ESG disclosure. This study contributes to the literature by offering an integrated approach to examine the relationships between sustainable human capital management, ESG performance, ESG disclosure, and financial performance, providing novel insights into the drivers and outcomes of corporate sustainability in the context of human capital management.
    Keywords: Sustainability, Effects of Globalization, Firm Financial Performance, ESG Disclosure, ESG Performance, Sustainable Human Capital Management
    Date: 2024–06–01
    URL: https://d.repec.org/n?u=RePEc:hal:journl:hal-05271946
  4. By: Brüll, Eduard; Mäurer, Samuel; Rostam-Afschar, Davud
    Abstract: We provide experimental evidence on how employers adjust expectations to automation risk in high-skill, white-collar work. Using a randomized information intervention among tax advisors in Germany, we show that firms systematically underestimate automatability. Information provision raises risk perceptions, especially for routine-intensive roles. Yet, it leaves short-run hiring plans unchanged. Instead, updated beliefs increase productivity and financial expectations with minor wage adjustments, implying within-firm inequality like limited rent-sharing. Employers also anticipate new tasks in legal tech, compliance, and AI interaction, and report higher training and adoption intentions.
    Keywords: Artificial Intelligence, Automation, Technological Change, Innovation, Technology Adoption, Firm Expectations, Belief Updating, Expertise, Labor Demand, White Collar Jobs, Training
    JEL: J23 J24 D22 D84 O33 C93
    Date: 2025
    URL: https://d.repec.org/n?u=RePEc:zbw:glodps:1683
  5. By: Grindaker, Morten (University of Chicago); Simmons, Michael (Department of Economics, Umeå University)
    Abstract: Does Unemployment Insurance affect how employed workers search for new jobs? We provide novel evidence by combining administrative data on the universe of Norwegian workers and firms with a regression kink design. A marginal increase in benefits lowers job-to-job transitions, increases unemployment incidence, and lowers future earnings. These effects are stronger for workers with higher predicted unemployment risk and align with job search models where workers systematically move towards safer jobs. In an equilibrium job search model calibrated to match these empirical effects, employed workers’ responses account for 45 percent of the net fiscal costs of a marginal benefit expansion.
    Keywords: On the job search; Unemployment Insurance; Regression kink design; Unemployment Risk
    JEL: G33 G52 H31 H55 J31 J65
    Date: 2025–10–24
    URL: https://d.repec.org/n?u=RePEc:hhs:umnees:1039
  6. By: Pikulina, Elena S.; Ferreira, Daniel
    Abstract: We introduce the concept of subtle discrimination—biased acts that cannot be objectively ascertained as discriminatory. When candidates compete for promotions by investing in skills, firms' subtle biases induce discriminated candidates to overinvest when promotions are low-stakes (to distinguish themselves from favored candidates) but underinvest in high-stakes settings (anticipating low promotion probabilities). This asymmetry implies that subtle discrimination raises profits in low-productivity firms but lowers them in high-productivity firms. Although subtle biases are small, they generate large gaps in skills and promotion outcomes. We derive further predictions in contexts such as equity analysis, lending, fund flows, banking careers, and entrepreneurial finance.
    Keywords: promotions; firm performance; bias; human capital
    JEL: M51 J71 J31
    Date: 2025–10–06
    URL: https://d.repec.org/n?u=RePEc:ehl:lserod:125960
  7. By: Matias Iaryczower; Gabriel Lopez-Moctezuma; Paola Moscariello
    Abstract: In this paper, we quantify the distortions induced by career concerns within the Federal Open Market Committee (FOMC). We combine a structural approach with an unanticipated change in the information available to the public about internal committee deliberations. We show that—given the policy preferences of Fed Presidents and Board Governors serving in the FOMC—agents' incentives to appear competent and unbiased outweigh the distortions induced by anti-pandering and conformity. Relative to a counterfactual with no reputational considerations, career concerns improve the welfare of an unbiased principal. Given our estimates of career concerns, Transparency improves welfare relative to an Opaque regime in which internal deliberations are not made public. In a counterfactual exercise, we show that greater heterogeneity in regional shocks reduces conformity but increases policy errors under Transparency.
    JEL: C57 D78 E58
    Date: 2025–10
    URL: https://d.repec.org/n?u=RePEc:nbr:nberwo:34394

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