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on Human Capital and Human Resource Management |
| By: | Tomasz Sulka (HU Berlin) |
| Abstract: | This paper develops a dynamic search model in which certain ``hidden attributes" are revealed only after acceptance of an offer and may trigger continued search in the following period. The model is applied to study how workers' imperfect information about pecuniary workplace benefits (such as employer-sponsored pension and health insurance plans) during job search, and the subsequent realization of these benefits on the job, affect the multidimensional compensation packages offered in equilibrium by profit-maximizing firms. I find that unobservability of benefits prior to acceptance distorts firms' incentives toward providing inefficiently low benefits, despite the fact that lower benefits induce higher worker turnover. Furthermore, when workers differ in strategic sophistication, and therefore hold different beliefs about unobservable benefits, there exist equilibria with spurious differentiation in compensation packages. In these equilibria, the wage differential is bounded from above by the benefit differential. The model demonstrates how imperfect information about workplace benefits can explain several empirical puzzles, including inefficiently low benefit provision and large between-firm dispersion in benefits. |
| Keywords: | exploitative contracting; hidden attributes; job search; workplace benefits; compensating differentials; |
| JEL: | D83 D91 J31 J32 J33 |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:566 |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, and AIAS, Aarhus University); Schroede, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Örebro University School of Business) |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures. |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2026_003 |
| By: | Alexander Bick; Adam Blandin; David J. Deming; Nicola Fuchs-Schündeln; Jonas Jessen |
| Abstract: | This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI. |
| JEL: | J2 O3 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34995 |
| By: | David Hummels; Jakob Munch; Huilin Zhang |
| Abstract: | We build a model of CEO compensation that unites principal-agent and assignment models in the face of trade shocks that interact with CEO effort. The model predicts that trade shocks change CEO compensation through scale, volatility, and ability-magnification channels. Using Danish matched worker-firm data, we find empirical support for these channels: (1) Exogenous shocks to trade increase the size and value of the firm and CEO compensation; (2) the share of firm value paid to the CEO is increasing in the size and value of the firm and increasing in the volatility induced by global shocks; (3) Higher-ability CEOs generate increases in firm value that are more than 100 times greater than their compensation, through a combination of mitigating losses and maximizing the return to positive shocks. |
| JEL: | F16 G30 J30 J31 M52 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35004 |
| By: | Bernd Irlenbusch (University of Cologne & London School of Economics and Political Science); Holger A. Rau (University of Duisburg-Essen & University of Gottingen); Rainer Michael Rilke (WHU – Otto Beisheim School of Management) |
| Abstract: | LLMs are rapidly entering the hiring process, but their most pronounced effects may occur before any screening by changing who chooses to apply. We study how human versus LLM-based evaluation and gender transparency shape entry into competitive jobs. In a preregistered online experiment, participants first complete a Niederle and Vesterlund (2007) tournament task to measure competitive preferences, then prepare text-based job applications and decide whether to apply under each of four evaluation regimes—human only, LLM only, and two hybrid human-in-the-loop configurations—while gender disclosure is randomized between subjects. LLM involvement reduces application rates, with stronger effects for women than men, including under hybrid designs. Effects are driven by non-competitive candidates; non-competitive women, the group most exposed to AI-induced deterrence, receive the strongest objective evaluations under pure AI assessment across all subgroups, yet are systematically underconfident and apply least often. Competitive men persistently apply and exhibit overconfidence-driven adverse selection, whereas competitive women show resilience to AI-induced deterrence while remaining well-calibrated under AI evaluation and exhibiting positive self-selection across regimes. We find no effects of gender transparency. |
| Keywords: | AI hiring, LLMs, algorithm aversion, gender differences |
| JEL: | C92 J71 J24 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:398 |