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on Human Capital and Human Resource Management |
| By: | Pulito, Giuseppe; Pytlikova, Mariola; Schroeder, Sarah; Lodefalk, Magnus |
| 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 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1732 |
| By: | David A. Matsa; Amalia R. Miller |
| Abstract: | We study Germany’s landmark quota requiring major public companies to include at least one woman on their top executive teams. The quota increased female representation among top executives by about two-thirds. Firms largely recruited women from outside their networks and without prior public-company top-executive experience, choosing them over male candidates with similar profiles. Most were appointed to HR or niche roles, and there was no increase in female CEOs. We find no significant effects on the female share of managers in lower ranks, policies promoting gender equality, firm value, or performance. Overall, the quota boosted diversity without causing much disruption. |
| JEL: | G34 G38 J44 J71 J78 K22 K31 M51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35030 |
| By: | Wei Cai; Andrea Prat; Jiehang Yu |
| Abstract: | Prior research has pointed to differences in organizational capital as a reason for the persistent performance discrepancies among otherwise similar firms. In this paper, we develop and validate a new measure of organizational capital. Based on over a million crowd-sourced employee reviews scraped from Glassdoor, we construct the measure of organizational capital at the firm-year level using the word embedding model and ChatGPT-generated synthetic reviews. Our measure varies over time in accordance with macro trends, and differs both across and within firms, reflecting firm heterogeneity and major internal changes. We validate our measure by testing empirical predictions of the properties of organizational capital discussed in prior literature. Our findings suggest that this measure captures a slowly evolving intangible asset that is significantly associated with firm performance and top management’s influence, aligning with the conceptualization of organizational capital by Dessein and Prat (2022). We further showcase applications of our measure in accounting, economics, finance, and management literature. Taken together, the paper provides implications for various stakeholders who are interested in assessing and managing firms’ organizational capital. |
| JEL: | D22 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35039 |
| By: | Manuel Arellano; Orazio Attanasio; Margherita Borella; Mariacristina De Nardi; Gonzalo Paz-Pardo |
| Abstract: | We develop a new approach to estimating earnings, job, and employment dynamics using subjective expectations data from the NY Fed Survey of Consumer Expectations. These data provide beliefs about future earnings offers and acceptance probabilities, offering direct information on counterfactual outcomes and enabling identification under weaker assumptions. Our framework avoids biases from selection and unobserved heterogeneity that affect models using realized outcomes. First-step fixed-effects regressions identify risk, persistence, and transition effects; second-step GMM recovers the covariance structure of unobserved heterogeneities such as ability, mobility, and match quality. We find lower risk and persistence of the individual productivity component than in prior work, but greater heterogeneity in ability and match quality. Simulations show that reduced-form estimates overstate persistence and volatility on individual-level productivity due to job transitions and sorting. After accounting for heterogeneity, volatility declines and becomes flat across the earnings distribution. These results underscore the value of expectations data. |
| JEL: | C23 C8 D15 J01 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35027 |
| By: | Autor, David (MIT); Chin, Caroline (MIT); Salomons, Anna (Tilburg University and Utrecht University); Seegmiller, Bryan (Northwestern University) |
| Abstract: | We study the role of expertise in new work—novel occupational roles that emerge as technological and economic conditions evolve—using newly available 1940 and 1950 Census Complete Count files and confidential American Community Survey data from 2011–2023. We show that new work is systematically distinct from simply more work in existing occupations in four respects. First, it attracts workers with distinct characteristics: new work is disproportionately performed by younger and more educated workers, even within detailed occupation-industry cells. Second, new work commands wage premiums that persist beyond workers’ initial entry into new work, consistent with returns to scarce, specialized expertise rather than temporary market disequilibrium. Third, these premiums decline across vintages as expertise diffuses, with ‘newer’ new work commanding larger premiums. Fourth, the emergence of new work can be traced to regional demand shocks, suggesting that expertise formation responds to economic opportunities. These findings suggest that new work is a countervailing force to automation-driven job displacement not merely by creating additional employment, butby generating new domains of human expertise that command market premiums. |
| Keywords: | new work, technological change, occupations, tasks |
| JEL: | E24 J11 J23 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18504 |