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on Human Capital and Human Resource Management |
| By: | Virginia Minni; Kieu-Trang Nguyen; Heather Sarsons; Carla Srebot |
| Abstract: | This paper studies how managers’ gender attitudes shape workplace culture and gender inequality. Using data from a multinational firm operating in over 100 countries, we leverage cross-country manager rotations to identify the effects of male managers' gender attitudes on gender pay gaps within a team. Managers from countries with one standard deviation more progressive gender attitudes reduce the pay gap by 5 percentage points (18%), largely through higher promotion rates for women. These effects persist after managers rotate out and are strongest in more conservative countries. Managers with progressive attitudes also influence the local office culture, as local managers who interact with but are not under the purview of the foreign manager begin to have smaller pay gaps in their teams. Our evidence points to individual managers as critical in shaping corporate culture. |
| JEL: | F23 J16 M14 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34782 |
| By: | Natalia Emanuel; Emma Harrington |
| Abstract: | What are the returns to firms of paying more? We study a Fortune 500 firm’s voluntary firm-wide $15/hour minimum wage, which affected some warehouses more than others. Using a continuous difference-in-differences design, we find that a $1/hour pay increase (5.5 percent) halves worker departures, reduces absenteeism by 18.6 percent, and increases productivity (boxes moved per hour) by 5.7 percent. These productivity gains fully defrayed increased labor costs, offsetting the firm’s incentive to mark down wages. We develop a simple model that connects efficiency-wage incentives and monopsony power, showing how these forces can counterbalance each other to keep wages closer to workers’ marginal revenues. |
| Keywords: | voluntary firm minimum wage; Efficiency wages; monopsony; labor market frictions |
| JEL: | M52 J31 J42 |
| Date: | 2026–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:102436 |
| By: | Naeem, Arslan (Revivo Technology, Lahore Pakistan) |
| Abstract: | The growing use of algorithm based systems to manage employees has transformed how work is organized and controlled in contemporary organizations. Commonly referred to as algorithmic management, this approach relies on automated decision making to allocate tasks, monitor performance, and evaluate employee outcomes. While such systems are often adopted to enhance efficiency, their implications for human work remain insufficiently understood. This study examined the relationship between algorithmic management and key employee outcomes, with a particular focus on employee autonomy, job satisfaction, and performance. Using a quantitative research design, data were collected through an online survey from employees working in algorithmically managed environments. Established measurement scales were employed to assess the study variables, and statistical analyses were conducted to test the proposed hypotheses. The findings indicate that algorithmic management is negatively associated with employee autonomy and job satisfaction, while demonstrating a significant relationship with employee performance. These results suggest that algorithmic management may improve performance related outcomes but can also constrain employees’ sense of control and satisfaction at work. The study contributes to the growing literature on digital and technology-mediated management by highlighting the dual effects of algorithmic management on human work. From a practical perspective, the findings underscore the importance of designing algorithmic systems that balance organizational efficiency with employee well-being. The study also offers directions for future research on the evolving role of algorithms in shaping the future of work. |
| Date: | 2026–01–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:zdm2p_v2 |
| By: | Sona Badalyan |
| Abstract: | I study how delayed retirements reshape firms’ internal labor markets, leveraging a German reform that raised women’s early retirement age by at least three years. The reform increased retention of older women and reduced both internal promotions and external hiring of younger coworkers, with the greatest losses among middle-aged workers who were near to older workers on the career ladder. Spillovers are structured: promotion crowd-outs arise in thick internal labor markets with intense competition, while hiring declines are largest in thin external markets with high turnover costs. Crowd-out effects concentrate within jobcells, whereas coworkers in different jobcells can benefit when retained older workers possess specific human capital. Taken together, the evidence supports slot-constraint theories—augmented by firm-specific human-capital mechanisms |
| Keywords: | aging, internal labor markets, human capital, worker substitutability |
| JEL: | H55 J21 J23 J24 J26 J31 J63 M51 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp810 |
| By: | Esther Bailey; Daniel Fehder; Eric Floyd; Yael Hochberg; Daniel J. Lee |
| Abstract: | We use a randomized experiment with 553 science- and technology-based startups in 12 co-working spaces across the US to evaluate the effects of intensive, short-term entrepreneurial training programs on survival and performance for innovation-driven startups. Treated startups are more likely to shut down their businesses and do so sooner than control startups. Conditional on survival, however, treated startups are more likely to raise external funding for their ventures, raise funding faster, and raise more funding than the control group; they also exhibit higher employment and revenue. Treated founders are less likely to found a new startup after shutdown. Our findings are consistent with practitioner arguments that early entrepreneurship training interventions can help entrepreneurs with less viable ventures “rationally quit” (“fail fast”). We use machine learning techniques (causal random forest) to provide exploratory insights on the most impacted subgroups. |
| JEL: | C93 D22 M13 M53 O32 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34755 |
| By: | Valentin Favre-Bulle; Sylvain Weber |
| Abstract: | We examine the impact of monetary and non-monetary incentives, individually and combined, on residential electricity consumption. A field experiment in Switzerland provided all participants with access to a custom-developed app offering feedback on electricity use and energy-saving tips. In addition to the control group, one treatment group received social comparisons based on savings relative to similar households, while a second group additionally received financial rewards linked to their electricity savings. We find no strong evidence of treatment effects. We do not observe crowding-out effect from combining monetary and non-monetary incentives, as the difference between treatments is not significant. Treatment effects appear to differ between PV and non-PV owners, with some indication of greater effectiveness for the latter, though further research is needed. Compared to a non-participant group, participation in the experiment and use of the application marginally reduced electricity consumption. |
| Keywords: | Household electricity usage; Demand-side management; Smart metering; Randomised control trial; Field experiment; Difference-in-differences; Crowding-out effect; Social incentives; Financial incentives. |
| JEL: | C93 D12 L94 Q41 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:irn:wpaper:26-04 |
| By: | Ajay K. Agrawal; John McHale; Alexander Oettl |
| Abstract: | The task-based approach has become the dominant framework for studying the labor-market effects of artificial intelligence (AI), typically emphasizing the replacement of human workers by machines. Motivated by growing empirical evidence that contemporary AI is more often used as a tool that augments workers, this paper develops two related task-based models in which AI enhances worker productivity without automating tasks. Abstracting from capital, we develop a pair of related task-based models that examine how technological progress in AI that provides new tools to augment workers affects aggregate productivity and wage inequality. Both models emphasize the role of human capital in intermediating the effects of AI-related technological shocks. In the first model, AI use requires specialized expertise, and technological progress expands the set of tasks for which such expertise is effective. We show that a larger supply of AI expertise amplifies the productivity gains from improvements in AI technology while attenuating its adverse effects on wage inequality. The second model focuses on non-AI skills, allowing AI tools to alter the set of tasks that workers can perform given their skills. In equilibrium, workers allocate across tasks in response to wages, generating an endogenous distribution of skills across the task space. A central result is that aggregate productivity and wage inequality depend on different global properties of this equilibrium distribution: productivity is particularly sensitive to thinly staffed tasks that create bottlenecks, while wage inequality is driven by the concentration of workers in a narrow set of tasks. As a result, improvements in AI tools can induce non-monotonic co-movement between productivity and inequality. By linking these mechanisms to multidimensional human capital---including AI expertise and higher-order non-AI skills---the paper highlights the role of education and training policies in shaping the economic consequences of AI-driven technological change. |
| JEL: | J24 O33 O41 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34781 |
| By: | Giorgio Brunello (University of Padova); Désirée Rückert (European Investment Bank); Christoph T. Weiss (European Investment Bank); Patricia Wruuck (German Federal Ministry of Economic Affairs and Climate Action) |
| Abstract: | Using firm-level data covering 25 EU countries, the UK and the US and a difference-in-differences approach, we show that employers adopting advanced digital technologies reduce their investment in training per employee. Compared to non-adapting firms, this reduction is negligible on impact but increases to -11.3 and -13.8 percent of the pre-treatment mean two and three years after adoption. It can be decomposed into two contrasting effects: the increase in the probability of investing in training and the reduction in investment by firms with positive training. We argue that a candidate reason for the decline in investment in training per employee is that the use of advanced digital technologies and employee training are substitutes in production, implying that an increase in the former negatively affects the marginal productivity of the latter. Our findings point to challenges in realizing high levels of firm-sponsored training for employees in increasingly digital economies. |
| Keywords: | Digital Technologies, Investment in Employee Training. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:pad:wpaper:0315 |