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on Human Capital and Human Resource Management |
| By: | Ales Marsal (National Bank of Slovakia); Patryk Perkowski (Yeshiva University) |
| Abstract: | We examine how generative AI impacts productivity across the task-based framework using a field experiment at the National Bank of Slovakia. In our experiment, we randomly assign generative AI access to central bank employees completing workplace tasks that mirror the theoretical task-based framework. Our results indicate that generative AI access leads to large improvements in both quality and efficiency for the majority of participants. We find a strong complementarity between generative AI and non-routine work, both on average and for most participants. We also find some support for generative AI as both cognitive-biased and specialist-biased, though smaller in magnitude than our tests of routine-biased. While workers in routine jobs experience larger individual performance gains, generative AI is less effective for the routine task content of their work. The mismatch between generative AI’s task- versus worker-level impacts is economically large, and results from a simulation exercise suggest the organization can increase output by 7.3% by changing how workers are assigned to tasks in the presence of generative AI. Additionally, we find differences in how the benefits of generative AI relate to worker skills: low-skill workers benefitmost in terms of quality while high-skill workers benefit in terms of efficiency. Our findings provide empirical support on generative AI and task-level complementarities, with important implications for how generative AI will impact workers, organizations, and labor markets more broadly. |
| JEL: | J24 M15 E58 C93 O33 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:svk:wpaper:1128 |
| By: | Frederiksen, Anders (Aarhus University); Junker-Jensen, Louis (Aarhus University) |
| Abstract: | We study the part-time penalty. Using Danish register data, the Danish Labor Force Survey, and hospital personnel records, we show that the pay gap between part-time and full-time workers is sizable and increases over the career because the two groups accumulate different levels of human capital over time. Our best estimates of the part-time penalty are for nurses. The penalty is 14 percent at the beginning of the career and increases by 0.5 percent each year. This pay gap is closely related to the development of nurses' competence level, highlighting the persistent effects that part-time work has on lifetime earnings. |
| Keywords: | part-time penalty, career, gender, pay gap |
| JEL: | M5 J3 J16 J22 J24 J31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18401 |
| By: | El Haj, Morien (Ghent University); Moens, Eline (Ghent University); Verhofstadt, Elsy (Ghent University); Van Ootegem, Luc (Ghent University); Baert, Stijn (Ghent University) |
| Abstract: | In tight labour markets, where employers compete not only on wages but also on amenities such as job family friendliness, employer-provided childcare arrangements serve as a powerful tool to attract and retain working parents. Yet little causal evidence exists on how employees evaluate such benefits. Therefore, this study uses a scenario experiment among working parents of young children to examine how job attractiveness is shaped by variations in employer-provided childcare arrangements – in terms of location, opening hours, and price – along with the possibility of teleworking. Our results show that all forms of employer-provided childcare increase job attractiveness, with childcare facilities operating on schedules explicitly aligned with employees’ working hours having the strongest effects. Working parents are willing to forego a 20% wage increase in a new job to obtain this latter amenity. They expect such amenity to improve their job satisfaction, performance, stress management, and work–family balance. Our results imply that the policy offers mutual gains for both employees and employers. |
| Keywords: | childcare, telework, job attractiveness, willingness to pay, factorial survey experiment |
| JEL: | C91 J13 J16 J24 J81 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18430 |
| By: | Alessandra Fenizia; Christos A. Makridis |
| Abstract: | This paper evaluates the causal impact of the 2025 U.S. federal personnel reforms on employment, employee engagement, job satisfaction, burnout, job search behavior, and perceptions about their workplace. Using administrative and survey data, we implement a differencein-differences design comparing federal employees to observationally similar state-and local government employees before and after the reforms. Event-study estimates show near-parallel pre-trends (2022-2024) and a sharp post-reform divergence in 2025. Baseline estimates indicate a meaningful decline in federal employment and employee engagement relative to the state control group, accompanied by reductions in job satisfaction and corresponding increases in reported burnout and job search activity. Heterogeneity analyses show that these effects are driven almost entirely by federal workers who identify as Democrats, and to some extent Independents, with little to no impact on their Republican colleagues. We also find that perceptions of workplace practices, like trust in leadership, play a moderating role. |
| Keywords: | federal workforce, civil service reform, employee engagement, job satisfaction, public sector, Gallup Workforce Panel |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12517 |
| By: | Faberman, Jason (Federal Reserve Bank of Chicago); Mueller, Andreas (University of Zurich); Sahin, Aysegül (Princeton University) |
| Abstract: | This paper studies gender gaps in labor-market outcomes, with a focus on job ladder dynamics. We show that women experience substantially lower wage growth conditional on prior wages despite nearly identical job-to-job transition rates for men and women. To reconcile these observations, we document gender differences in the valuation of nonwage job amenities and in job search behavior, and develop a multi-dimensional job-ladder model with endogenous search effort where workers value both wages and amenities. The model allows for gender heterogeneity in separation rates, search effort, the value of nonemployment, amenity valuations, and bargaining power, enabling a joint analysis of gender wage and employment gaps. A quantitative decomposition shows that differences in preferences for nonwage amenities account for nearly 40 percent of the gender pay gap. Differences in the value of nonemployment and bargaining power explain most of the remainder, with only a limited role for differences in separation rates and search behavior. Finally, we show that increases in job amenities — such as the expansion of remote work — raise the gender wage gap while reducing gender differences in employment. |
| Keywords: | gender wage gap, job search, job amenities, on-the-job search |
| JEL: | J16 J60 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18418 |
| By: | David Almog; Lucas Lippman; Daniel Martin |
| Abstract: | We use an online experiment with a real work task to study whether workers change their behavior when they know AI will be used to judge their work instead of humans. We find that individuals produce a higher quantity of output when they are assigned an AI evaluator. However, controlling for quantity, the quality of their output is lower, regardless of whether quality is measured using humans or LLM grades. We also find that workers are more likely to use external tools, including LLMs, when they know AI is used to judge their work instead of humans. However, the increase in external tool use does not appear to explain the differences in quantity or quality across treatments. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.02076 |
| By: | Giménez-Nadal, José Ignacio (University of Zaragoza); Molina, José Alberto (University of Zaragoza); Velilla, Jorge (University of Zaragoza) |
| Abstract: | Worker productivity depends not only on hours worked, but also on how work time is actually used, and time-use evidence shows that non-work at work is non-trivial. This paper provides a data-driven characterization of shirking, and studies which observable characteristics best predict shirking behavior using American Time Use Survey data over 2003–2024. We implement a machine-learning forward selection procedure based on out-of-sample predictive performance. Our results suggest that shirking strongly depends on stochastic or unobserved factors, and that the determinants of the extensive and intensive margins are different. Moreover, the most informative predictors are predominantly job-related and time-allocation variables, whereas macro and labor-market indicators seem less relevant. This suggests that policies or managerial approaches to improve worker efficiency relying on observables face important limitations. |
| Keywords: | shirking, non-work at work, ATUS data, prediction |
| JEL: | J22 C53 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18432 |