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on Human Capital and Human Resource Management |
| By: | Hellsten, Mark (University of Tübingen); Khanna, Shantanu (Northeastern University); Lodefalk, Magnus (Örebro University); Yakymovych, Yaroslav (Uppsala University) |
| Abstract: | Artificial intelligence (AI) is expected to reshape labor markets, yet causal evidence remains scarce. We exploit a novel Swedish subsidy program that encouraged small and mid-sized firms to adopt AI. Using a synthetic difference-in-differences design comparing awarded and non-awarded firms, we find that AI subsidies led to a sustained increase in job postings over five years, but with no statistically detectable change in employment. This pattern reflects hiring signals concentrated in AI occupations and white-collar roles. Our findings align with task-based models of automation, in which AI adoption reconfigures work and spurs demand for new skills, but hiring frictions and the need for complementary investments delay workforce expansion. |
| Keywords: | hiring, labor markets, Artificial Intelligence, task content, technological change |
| JEL: | J23 J24 O33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18267 |
| By: | Chikhale, Nisha (University of Delaware); Duncombe, Natalie; Larsen, Birthe (Department of Economics, Copenhagen Business School) |
| Abstract: | We provide new causal evidence on the labor market consequences of workplace sex-ual harassment using matched survey and administrative data from Denmark. Both women and men experience persistent earnings losses of around 6 percent, with losses doubling among those who change employers. These effects are not driven by non-employment or occupational downgrading but by moves to lower-paying firms. A sub-stantial share of harassment comes from clients—particularly for women—highlighting the need for broader anti-harassment policies. Our findings reveal the long-term eco-nomic scars of harassment and gendered patterns in firm mobility, sorting, and pro-ductivity that persist beyond job transitions. |
| Keywords: | Workplace sexual harassment; Anti-harassment policies; Gendered firm mobility patterns |
| JEL: | J16 J32 J81 |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:cbsnow:2025_007 |
| By: | David Arnold; Simon Quach; Bledi Taska |
| Abstract: | This paper studies the labor market effects of recent state-level policies that require employers to disclose salary information in job postings. Leveraging a difference-in-differences design, we show that employers increased the fraction of postings with salary information by 30 percentage points. Across three datasets, we find consistent evidence of an increase in wages of 1.3-3.6%. We find no impacts on pay dispersion, employment, the number of postings, or skill and education requirements. Our evidence is consistent with pay transparency increasing competition in the labor market, leading to positive spillovers on incumbent workers and always-posting firms. |
| JEL: | J30 J31 J38 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34480 |
| By: | Leonardo Bursztyn; Alex Imas; Rafael Jiménez-Durán; Aaron Leonard; Christopher Roth |
| Abstract: | Anxiety about falling behind can drive people to embrace emerging technologies with uncertain consequences. We study how social forces shape demand for AI-based learning tools early in the education pipeline. In incentivized experiments with parents—key gatekeepers for children’s AI adoption—we elicit their demand for unrestricted AI tools for teenagers’ education. Parental demand rises with the share of other teenagers using the technology, with social forces increasing willingness to pay for AI by more than 60%. Providing information about potentially adverse effects of unstructured AI use negatively shifts beliefs about the merits of AI, but does not change individual demand. Instead, this information increases parents’ preference for banning AI in schools. Follow-up experiments show that social information has little effect on beliefs about AI quality, perceived skill priorities, or support for bans, suggesting that effects operate through social pressure rather than social learning. Our evidence highlights social pressure driving individual technology adoption despite widespread support for restricting its use. |
| JEL: | D83 D91 I20 O33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34488 |