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on Human Capital and Human Resource Management |
| By: | Cowgill, Bo (University of Toronto); Freiberg, Brandon (INSEAD); Starr, Evan (University of Maryland) |
| Abstract: | We study worker noncompete clauses in a large field experiment with two finance firms. Across ~14, 000 job offers to freelance recruiters on short-term contracts, we randomize wages and the presence, salience, and duration of noncompetes (all contracts also included a nondisclosure agreement). Removing a noncompete increases mobility between competing employers by 36--52% and raises workers' total earnings from the two firms by 12--17%. We find no evidence---rejecting even small effects---that removing noncompetes generates secret leakage. We also find no evidence that workers choose noncompete jobs for higher pay. Many workers appear unaware of noncompetes before firms' post-employment communication. The results align with a model of inattention and uncertainty about enforcement. |
| Keywords: | noncompete clauses, earnings, knowledge diffusion, mobility, contracting, inattention, enforcement risk |
| JEL: | J42 M5 J31 J41 K31 L41 C93 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18820 |
| By: | Gill, David (Purdue University); Qiao, Zhongheng (Purdue University) |
| Abstract: | Workers and organizations routinely choose the complexity of tasks that they undertake. We study how cognitive ability and overconfidence shape this choice of task complexity, both theoretically and experimentally. Consistent with our model, we find that more cognitively able individuals choose more complex tasks. Conditional on cognitive ability, we find that more overconfident individuals also choose more complex tasks. About 80% of subjects pick their payoff-maximizing task, and higher cognitive ability predicts more efficient choices of task complexity. But errors are strikingly one-sided: nearly all who fail to choose optimally pick tasks that are too complex. Our findings suggest that organizations should pair task menus and incentives for choosing more complex tasks with information and feedback that help workers assess their ability and probability of success. This preserves the benefits of self-selection while reducing costly overreach. |
| Keywords: | cognitive ability, complexity, task choice, choice efficiency, overconfidence, experiment |
| JEL: | C91 D03 D83 D91 J24 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18834 |
| By: | Caliendo, Marco (University of Potsdam); Huber, Katrin (University of Potsdam); Isphording, Ingo (Max Planck Institute for Behavioral Economics); Wegmann, Jakob (Rockwool Foundation Berlin) |
| Abstract: | We study the extent, correlates, and consequences of reporting bias in survey wages using German linked survey-administrative data (SOEP-CMI-ADIAB). Survey wages differ systematically from administrative records: mean survey wages are 7% lower, with mean-reverting discrepancies that firm context explains far better than individual characteristics. Since neither source alone is sufficient, we construct a hybrid wage combining their strengths. Measurement choice matters mainly through the treatment of administrative top-coding: when wages are outcomes, censoring at the assessment limit understates returns to education by 4-11% and the gender wage gap by up to 23%, while imputation reverses the bias for returns. When wages are regressors, wage-satisfaction gradients are 9-28% steeper with survey than administrative wages below the assessment limit, indicating non-classical, context-dependent misreporting. We provide guidance for choosing between administrative, survey, and hybrid wages, with lessons for any setting where self-reported wages are collected alongside top-coded administrative records. |
| Keywords: | reporting bias, measurement error, wage, income, administrative data, survey data, data linkage |
| JEL: | J30 C81 D31 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18794 |
| By: | Nikolova, Milena (University of Groningen) |
| Abstract: | This paper provides the first causal evidence on how Artificial Intelligence (AI)-based workplace safety systems shape perceived job quality. I conducted a preregistered vignette experiment with a nationally representative sample of 2, 172 Dutch adults who evaluated otherwise identical workplaces introducing one of three safety systems: human supervisors, AI-only monitoring, or hybrid AI-human supervision. Compared with human supervision, both AI-only and hybrid systems reduced perceived job satisfaction, work meaningfulness, and perceived social value of the job. Contrary to expectations, combining AI with human supervisors did not mitigate these negative effects. Respondents also viewed AI-based systems as less respectful of workers' privacy and dignity, despite viewing them as effective as human supervisors. Perceived fair wages changed little across conditions. These findings suggest that AI can influence work not only by improving safety but also by reducing important non-pecuniary dimensions of job quality, highlighting that the welfare consequences of workplace AI extend beyond productivity and accident prevention. |
| Keywords: | Artificial Intelligence (AI), safety systems, survey experiment, work meaningfulness, job quality |
| JEL: | I39 J01 J28 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18782 |
| By: | Josh Feng; Xavier Jaravel |
| Abstract: | What are the implications of unequal access to entrepreneurial careers for labor markets? Using data from the U.S. Census and LinkedIn profiles, we document that entrepreneurs are significantly more likely to hire workers from similar social backgrounds (gender, race, age, education, etc.). These effects are quantitatively large across several demographic dimensions. For example, female employee share at female-founded startups is 36.4pp higher after controlling for industry-by-metro area-by-cohort fixed effects, with corresponding estimates of 51.2pp for Blacks, 37.3pp for Hispanics, and 11.3pp for non-college individuals. Large effects are present in high-growth startups, across industries and occupations, and remain stable across new firm cohorts. In addition, we find that these differences persist out to at least 20 years. We use wage data and an AKM research design to untangle whether the relative differences are driven by labor demand or labor supply effects. We find that demand drives the differences: group-specific wage decompositions show that new firms pay higher relative wages to individuals from similar backgrounds to the entrepreneur. Using these estimates, we calibrate a model of entrepreneurship with heterogeneous ability and production functions, and assess the impacts on relative wage from reducing access barriers to entrepreneurship. |
| Keywords: | entrepreneurship, hiring, labor market, production functions |
| JEL: | L26 J31 D24 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-45 |
| By: | Nikolova, Milena (University of Groningen); Milanova, Vilian (University of Groningen); Wang, Feicheng (University of Groningen) |
| Abstract: | This paper provides the first causal evidence that merely attributing identical creative work to AI rather than to a human affects how much meaning people derive from a task and how much effort they are willing to contribute. We conducted a preregistered survey experiment in nationally representative samples from the United States (N = 1, 511) and the Netherlands (N = 2, 117). Participants evaluated identical public health campaign slogans that were randomly attributed either to an AI system or to a human professional, allowing us to isolate the causal effect of AI attribution while holding the creative output constant. AI attribution reduced perceived task meaning modestly and made participants 13% less likely to contribute a slogan of their own, indicating lower voluntary effort. These findings suggest that AI can influence work not only by changing productivity but also by altering the perceived value of human contribution itself. |
| Keywords: | Artificial intelligence (AI), meaning, effort, survey experiment |
| JEL: | C91 J01 I30 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18784 |