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on Human Capital and Human Resource Management |
| By: | Gorny, Paul M.; Schäfer, Louis |
| Abstract: | We study human-robot collaboration in a controlled experiment run in a realistic production environment. Participants completed a sequential task in pairs, where one worker (Worker 1) decided whether to pass intermediate components to a coworker or not. Depending on the treatment, the coworker was either another human participant or a physical industrial robot. The coworker-setup was either static or adaptive, with adaptive coworkers' productivity being influenced by Worker 1's performance in the task. We find strong evidence of robot aversion: workers were significantly less likely to pass intermediate products to their coworkers in the robotic as compared to the human treatments. This was despite overall productivity was identical across treatments. In a subsequent responsibility attribution task, participants also attributed greater responsibility to the robots, indicating a systematic bias in social evaluation of machine coworkers. Adaptivity only marginally affected these outcomes. Our results demonstrate that cooperation and responsibility attribution in hybrid teams depend not only on performance but also on social perceptions of artificial agents, highlighting behavioural frictions that may constrain the effective integration of robots into human work environments. |
| Keywords: | Human-robot collaboration, Responsibility attribution, Robot aversion, Adaptivity, Automation, Experimental methodology |
| JEL: | C91 J24 O33 |
| Date: | 2025–10–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126663 |
| By: | Hellsten, Mark (University of Tübingen); Khanna, Shantanu (Northeastern University); Lodefalk, Magnus (Örebro University School of Business); 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: | Artificial intelligence; Labor markets; Hiring; Task content; Technological change |
| JEL: | J23 J24 O33 |
| Date: | 2025–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_013 |
| By: | Lima de Miranda, Katharina; Detlefsen, Lena; Schmidt, Ulrich |
| Abstract: | Our study contributes to the literature on choice shifts in group decision-making by analyzing how the level of risk-taking within a group is influenced by its gender composition. In particular, we investigate experimentally whether group composition affects how preferences ‘shift’ when comparing individual and group choices. Consistent with hypotheses derived from previous literature, we show that male-dominated groups shift toward riskier decisions in a way that is not explained by any simple preference aggregation mechanism. We discuss potential channels for the observed pattern of choice shifts. |
| Keywords: | Experiment, gender group, decisions, risk-taking, risky shift |
| JEL: | D71 D81 D91 J16 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkie:330837 |
| By: | Hellsten, Mark; Khanna, Shantanu; Lodefalk, Magnus; Yakymovych, Yaroslav |
| 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: | Artificial intelligence, Labor markets, Hiring, Task content, Technological change |
| JEL: | J23 J24 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1692 |
| By: | G. Jacob Blackwood; Cindy Cunningham; Matthew Dey; Lucia Foster; Cheryl Grim; John Haltiwanger; Rachel Nesbit; Sabrina Wulff Pabilonia; Jay Stewart; Cody Tuttle; Zoltan Wolf |
| Abstract: | We present new empirical evidence suggesting that we can better understand productivity dispersion across businesses by accounting for differences in how tasks, skills, and occupations are organized. This aligns with growing attention to the task content of production. We link establishment-level data from the Bureau of Labor Statistics Occupational Employment and Wage Statistics survey with productivity data from the Census Bureau’s manufacturing surveys. Our analysis reveals strong relationships between establishment productivity and task, skill, and occupation inputs. These relationships are highly nonlinear and vary by industry. When we account for these patterns, we can explain a substantial share of productivity dispersion across establishments. |
| Keywords: | productivity dispersion, tasks, skills, occupations |
| JEL: | D24 J24 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-63 |
| By: | Anais Galdin; Jesse Silbert |
| Abstract: | Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.08785 |
| By: | Sonia Falconieri; Marcelo Ortiz; Francisco Urzua; Paolo Volpin |
| Abstract: | Since Norway’s board gender reform in 2003, many European countries have introduced gender targets for the boards of listed firms. We examine how these regulations affected the gender of newly appointed chief executive officers (CEOs) in private firms. Using crosscountry and industry-level variation in exposure to the reform, we document an 8 to 13 percent increase in the number of appointments of female CEOs in industries with listed firms subject to the reforms, and no change in industries without such exposure. The effect is stronger in countries with mandatory quotas and where board appointments are more salient. The results indicate that board gender regulations generated positive spillover effects beyond the targeted listed firms, increasing the representation of women in top executive positions in private firms; however, they did not lead to a broader country-level cultural shift. |
| Keywords: | Board quotas, female CEOs, private firms |
| JEL: | G14 G34 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:upf:upfgen:1926 |