|
on Human Capital and Human Resource Management |
| By: | Wei Cai; Andrea Prat; Jiehang Yu |
| Abstract: | Incomplete contract theory, supported by anecdotal evidence, suggests that when a firm is acquired, workers may be adversely affected in non-contractible aspects of their work experience. This paper empirically investigates this prediction by combining M\&A events from the Refinitiv database and web-scraped Glassdoor review data. We find that: (a) Controlling for pre-trends, mergers lead to lower satisfaction, especially on non-contractible dimensions of the employee experience (about 6% of a standard deviation); (b) The effect is stronger in the target firm than in the acquiring firm; (c) Text analysis of employee comments indicates that the decline in satisfaction is primarily associated with perceived breaches of implicit contracts. Our findings indicate that mergers may reduce workers' job utility through non-monetary channels. |
| JEL: | D23 G34 J31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34920 |
| By: | Khaliliaraghi, Negar (IFAU - Institute for Evaluation of Labour Market and Education Policy); Lundborg, Petter (Lund University); Vikström, johan (IFAU and Uppsala University) |
| Abstract: | Gender gaps in earnings persist even among high-skilled workers, in part because men and women often perform different tasks within and across jobs. We study a rare setting in which high-skilled men and women perform the same tasks under comparable conditions, allowing us to assess gender differences in productivity and pay without confounding from task or client allocation. Using administrative data from the Swedish Public Employment Service between 2003 and 2014, we exploit a rotation scheme that quasirandomly assigns job seekers to employment caseworkers. We find that productivity differences are small: job seekers assigned to female and male caseworkers exit unemployment at similar rates, and hourly wages—conditional on productivity—are nearly identical across genders, leaving little scope for wage differences driven by discrimination or bargaining in this setting. Despite this, female caseworkers earn about 8 percent less per year, entirely due to differences in contracted and actual hours worked. We also find suggestive evidence that male caseworkers are more likely to be promoted than equally productive female colleagues. Taken together, the results show that when tasks are standardized and performance is measured objectively, gender differences in productivity and hourly pay are minimal, while gaps in annual earnings and career progression persist. |
| Keywords: | Gender Gaps; Productivity; Wages; Task Allocation |
| JEL: | D84 I12 J12 J21 |
| Date: | 2026–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ifauwp:2026_007 |
| By: | Nuriye Melisa Bilgin; Ester Faia; Gianmarco Ottaviano |
| Abstract: | We examine how upstream firms' technology adoption affects the performance and adoption decisions of downstream partners. Using business-to-business data with administrative records on advanced technology adoption, we find gains in productivity, performance, adoption probabilities of firms connected to the adopter, relatively to those that are not. Identification combines staggered event studies, balanced panels of pre-existing relationships, and recentering methods to address expected exposure within the network. Gains vary along firm size, centrality, technology quality, but do not systematically increase with input exposure, suggesting that knowledge spillovers may induce organizational adjustments. Adoption by competitors is associated with short-run negative effects. |
| Keywords: | technology diffusion, adoption and propagation, firm networks, firm productivity, imported inputs |
| Date: | 2026–03–11 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2157 |
| By: | Gschwendt, Christian (University of Bern); Viarengo, Martina (Graduate Institute of International and Development Studies, Geneva); Zollner, Thea S. (University of Bern) |
| Abstract: | The economic impact of technological change will critically depend on how future workers invest in their human capital. Yet, little is known about how future workers themselves evaluate and choose their educational and occupational paths in light of emerging technologies. This paper examines how adolescents currently at the school-to-work transition stage value working with generative artificial intelligence (GenAI) in their future occupations, and how automation risk and opportunities for continuing education shape these preferences. We field a discrete-choice experiment among a nationally representative sample of over 7, 000 Swiss adolescents aged around 15. We find that adolescents generally exhibit an aversion to collaborating with GenAI at work, with females consistently more averse than males. However, preferences are nuanced: adolescents welcome greater GenAI collaboration, provided that GenAI usage levels remain moderate and that it is not accompanied by increases in job automation risk. Finally, our findings suggest that AI-related educational opportunities in occupations improve attitudes towards working with GenAI across genders. |
| Keywords: | occupational choice, gender gaps, GenAI, choice experiment, continuing education, automation risk |
| JEL: | I24 J24 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18456 |
| By: | Lodefalk, Magnus (Örebro University School of Business); Löthman, Lydia (Örebro University School of Business); Koch, Michael (Aarhus University); Engberg, Erik (Örebro University School of Business) |
| Abstract: | We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden’s largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden’s seven-month gap between the Riksbank’s first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer– employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers. |
| Keywords: | Generative artificial intelligence; Job postings; Labour demand; Employment composition; Monetary policy |
| JEL: | J23 J24 O33 |
| Date: | 2026–03–16 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2026_002 |
| By: | Cowgill, Bo (Columbia Business School); Hernandez-Lagos, Pablo (Sy Syms School of Business at Yeshiva University); Wright, Nataliya (Columbia Business School) |
| Abstract: | Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants' access to GAI can increase---but also decrease---an evaluator's screening mistakes. This result depends on how GAI affects experts' signals compared to non-experts'. Using experiments in hiring and startup investing, we estimate that senders' access to GAI (ChatGPT) lowers screening accuracy by 4-9% for employers and startup investors. Consistent with our model, senders' access to GAI also improves screening accuracy in some settings---in our case, among senders from non-English-speaking countries. These results show that GAI can profoundly shape screening accuracy. |
| Keywords: | screening, Artificial Intelligence, entrepreneurship, human capital |
| JEL: | D82 M51 L26 D83 O33 M13 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18442 |
| By: | Christian Gschwendt; Claudio Schilter |
| Abstract: | Generative AI (GenAI) adoption is spreading rapidly and reshaping work, yet its implications for firms' training decisions remain largely unexplored. This paper examines how automation in the post-GenAI era affects firms' entry-level training positions using a vignette experiment with recruiters at over 2, 800 Swiss firms, covering more than 100 distinct occupations. Firms plan to reduce training positions in response to automation prospects, with larger reductions the greater the expected automated task share and the earlier the expected implementation. Effects are markedly stronger in routine-intensive and AI-exposed occupations, as well as among large firms. Our experiment allows us to disentangle an "erosion of the training pipeline, " where firms reduce training even though demand for trained specialists remains, from an overall decline in occupational labor demand. We find that pipeline erosion accounts for less than one third of the average reduction in training, but substantially more when automation is particularly intensive - measured by a high share of tasks being automated - and in routine-intensive and AI-exposed occupations. Overall, the results suggest that GenAI adoption is likely to reallocate firms' human capital investment with potential downstream implications for early career formation, and to reinforce labor market de-routinization trends. |
| Keywords: | Automation, firm training, technological change, generative AI, artificial intelligence, entry-level employment |
| JEL: | J24 M53 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0252 |