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on Human Capital and Human Resource Management |
| By: | Joshua S. Gans; Peter Landry |
| Abstract: | Firms ask workers to train AI systems that will replace them. We model this with a worker who can shirk, cooperate with training, or obstruct training while maintaining productivity. Because training ends the job but only cooperation completes training, dismissal becomes a weaker penalty for shirking. The firm must raise the worker’s rent, and the worker gains from their replacement. AI can create jobs paying more than they produce and eliminate jobs it never automates. Firms paying only wages may slow training. A credible completion bonus equal to the worker’s rent without AI removes both the gain and the slowdown. |
| JEL: | D86 J24 J41 M52 O33 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35950 |
| By: | Vahagn Jerbashian; Montserrat Vilalta-Bufi |
| Abstract: | We provide evidence from a 2025 survey of individuals in Spain on workplace use of artificial intelligence (AI) and other modern digital technologies. AI use is already economically meaningful but far from pervasive, with about 16% of workers reporting AI use. Its adoption is highly uneven across workers and jobs, with strong gradients by education, age, earnings, and firm size and scope, as well as substantial within-occupation heterogeneity. AI use is strongly correlated with the use of other modern technologies, which accounts for a larger share of variation than any worker or job characteristic considered separately, including occupational exposure measures. The purposes of AI use also vary markedly across demographic groups and jobs. Users report predominantly positive productivity effects in abstract cognitive tasks, together with limited but systematic time reallocation across tasks. |
| Keywords: | AI at work, complementary technologies, task reallocation, productivity |
| JEL: | J24 O33 L23 C83 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13094 |
| By: | Norris Keiller, Agnes; Obermeier, Tim; Teichgraeber, Andreas; Van Reenen, John |
| Abstract: | To what extent do identical workers in an industry benefit from productivity growth in the same industry? We study this question in the context of the UK auto industry which experienced a thirteen-fold increase in output per worker between 1980 and 2018. Over the same period, auto workers saw substantially stronger hourly wage growth than workers in other manufacturing industries. Controlling for unobserved worker heterogeneity, the auto wage premium over other manufacturing workers doubled from 8% in the 1980s to 17% in the 2010s, while the manufacturing premium over the rest of the economy remained roughly stable at 5%. Interpreted through the lens of a rent sharing model, we estimate that 63% of the increase in the raw firm-level auto wage premium can be accounted for by strong productivity growth. In contrast, differential bargaining power between auto workers cannot account for these changes. |
| Keywords: | wages;productivity;auto industry |
| JEL: | J30 L10 L60 |
| Date: | 2026–12–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:141474 |
| By: | Julien Senn; Thomas F. Epper; Ernst Fehr |
| Abstract: | Workers care not only about pay and career opportunities but also about meaningful work and socially responsible employers. This heterogeneity matters for worker-firm matching, recruitment, and compensation, yet its preference-based origins remain poorly understood. In a two-wave study of a sample broadly representative of the Swiss adult population (N=1, 031), we study whether social preferences provide a common account of life goals, job preferences, and occupational sorting. We structurally estimate two facets of social preferences: altruism in the advantageous and in the disadvantageous domain. We show that these two dimensions are negatively correlated, and that failing to account for both can substantially bias — in our applications, underestimate — the effects of social preferences. Social preferences predict respondents’ life goals, their preferred job attributes, and their willingness to work in the tobacco industry. In particular, higher altruism is associated with lower emphasis on wealth, financial security, career status, pay, and advancement, but greater emphasis on corporate social responsibility and meaningful work. It also predicts greater willingness to avoid working in the tobacco industry. Lastly, altruism when ahead is positively associated with employment in education, health, and social services. Thus, this paper highlights the importance of social preferences for explaining worker motivation and labor-market sorting. |
| Keywords: | social preferences, meaning-at-work, career choices, sorting |
| JEL: | D01 D91 J24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13075 |
| By: | Zhong, Yang (University of Warwick) |
| Abstract: | Distractions are pervasive in modern workplaces, from frequent interruptions to noisy open-plan offices. Using an incentivized laboratory experiment, I study performance and well-being under task-switching and auditory distractions, elicit willingness to pay to avoid them, and develop a survey measure of distraction resilience that captures individuals' willingness and ability to work under distractions. Neither distraction significantly reduces performance, but both reduce well-being. Participants are willing to pay to avoid distractions, with those experiencing lower well-being under distractions willing to pay more. I find no gender differences in the effects on performance or well-being, although women are willing to pay less than men to avoid both distractions. I validate the survey measure of distraction resilience by showing that those scoring higher experience better well-being under both distractions. I then incorporate the validated measure in a representative Dutch panel and find that greater distraction resilience is related to higher income, a greater likelihood of holding a supervisory position, and higher job satisfaction. |
| Keywords: | refugee children, schools, integration, educational attainment, contraceptives JEL Classification: I24, I21, J13, J15, O15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cge:wacage:827 |
| By: | Henry E. Siu; Kelly Foley; Guido Matias Cortes |
| Abstract: | Some occupations pay higher wages than others, even conditional on worker characteristics, implying that occupations can be ranked on a ladder. We develop a simple decomposition to study the role this occupational ladder plays in average wage growth and in the fanning out of wages over the life cycle. We present three main findings. First, the distribution of employment shifts towards higher-paying occupations as workers gain experience. This contributes modestly to average life-cycle wage growth, with greatest impact in early career. Second, this upward movement in the occupational employment distribution is not accompanied by increasing dispersion; thus, the occupational ladder does not directly contribute to the fanning out of wages. Third, early-career workers who are well-positioned on the occupational ladder tend to earn wages far below the occupational average. However, by mid-career, workers’ relative wages within occupations (their positions on the job ladder) no longer depend on their occupation. This change in the covariance between individuals’ positions on the job and occupational ladders is a major contributor to the fanning out of wages over workers’ careers. |
| JEL: | E24 J24 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35929 |
| By: | Brosch, Hanna (University of Erlangen-Nuremberg); Langer, Christina (ifo Institute); Lergetporer, Philipp (Technical University of Munich); Mueller, Sandra (Federal Institute for Vocational Education and Training (BIBB)); Pfeifer, Harald (Federal Institute for Vocational Education and Training (BIBB)); Tschoepe, Nele (Federal Institute for Vocational Education and Training (BIBB)) |
| Abstract: | Does generative artificial intelligence (AI) expand the set of tasks that middle-skilled, early-career workers can successfully perform? In a field experiment with 673 final-year IT apprentices at twelve German vocational schools, we randomly assign access to AI for completing occupation-specific tasks. To identify task expansion, we study tasks both within the apprenticeship curriculum and deliberately beyond it. AI raises productivity substantially across all task types, with gains tending to be larger on tasks where performance without AI is lowest. Crucially, productivity increases by 26–31 percentage points on tasks beyond apprentices’ formal training, providing direct evidence of AI-enabled task expansion. These productivity gains do not come at the cost of reduced immediate task comprehension. Treatment effects are generally larger among apprentices with above-median AI literacy. |
| Keywords: | generative artificial intelligence, field experiment, productivity, task expansion, middle-skilled workers, apprenticeships |
| JEL: | J24 O33 C93 I21 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18999 |