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on Human Capital and Human Resource Management |
| By: | Francis Annan; Collin B. Raymond |
| Abstract: | We study how firms choose among incentive contracts and how accurately managers predict their effects. Using a survey of managerial beliefs and a large-scale field experiment, we randomly assign agents at the market level to several widely used, expenditure-equivalent incentive schemes. In the field, the best-performing contract increases agent output and firm revenue by over 20% relative to the status quo, despite being ranked lower by managers, whereas the worst-performing contract performs as predicted. Managers correctly identify underperforming contracts but systematically underestimate top- performing ones. We document the sources of performance di!erences—labor supply responses rather than selection or pricing and the determinants of managerial predictability: contract complexity and managerial hierarchy. Our results highlight the importance of contract design for firm performance and reveal systematic limits to managerial cognition in shaping incentives. |
| JEL: | D86 D91 G23 L21 O12 O16 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35788 |
| By: | Ana Figueiredo; Agnieszka Markiewicz; Elliott Weder |
| Abstract: | Using matched employer-employee data from the Netherlands, we decompose the wage penalty of temporary work. Temporary workers earn 28 log points less per hour than permanent workers. Worker fixed effects explain half this gap, while firm pay premia account for 28%. Most of the firm contribution reflects temporary workers sorting into low-paying firms rather than within-firm pay differences. As temporary employment differs substantially across demographic groups, we extend our framework to gender and native-migrant wage gaps, separating contract from firm sorting. Differential exposure to temporary work explains part of the native-migrant firm pay-premia gap but matters little for the gender wage gap. |
| Keywords: | Temporary employment; Firm pay premia; Wage inequality |
| JEL: | J31 J41 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26240 |
| By: | Will Aarons; Asani Sarkar |
| Abstract: | There is little consensus on the effect of AI adoption on employment. Economy-wide measures of pay and headcount have moved little even as AI adoption has spread rapidly, yet narrower measures document declining hiring for workers at the start of their careers or in AI-exposed occupations. In this post, we revisit this topic for U.S. small businesses. We use data from the 2025 Small Business Credit Survey (SBCS) to examine the twelve-month-forward employment and revenue expectations of AI users. We find that firms currently using AI are significantly more likely than non-users to expect increased employment and revenues over the next year, even after accounting for firm and owner characteristics and location. Revenue expectations are most optimistic for AI users that report facing operational challenges in utilizing technology. |
| Keywords: | artificial intelligence (AI); small business; employment; revenues |
| JEL: | O3 L2 D2 |
| Date: | 2026–10–08 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103899 |
| By: | Daniel Herbst |
| Abstract: | Compared to self-employment or performance-based pay, fixed wages reduce earnings uncertainty but are prone to market distortions through moral hazard and adverse selection. Using a model of wage contracts under asymmetric information, I show that these distortions map to marginal treatment response functions in a marginal treatment effects (MTE) framework. I apply this framework to a field experiment in which data-entry workers choose between a randomized fixed wage and a piece rate. I find evidence of both moral hazard and adverse selection: Fixed wages reduce worker productivity by an estimated 8.74 percent relative to the mean. Meanwhile, a 10 percent increase in the wage offer attracts a marginal worker whose productivity is higher by 1.90 percent of the mean. Using semi-parametric MTE estimation, I calculate the welfare loss associated with asymmetric information and the marginal values of public funds (MVPFs) for policies aimed at recovering this loss. I find that a tax on piece rates can mitigate adverse selection into fixed wages, generating tax revenue at social costs as low as $0.88 per dollar. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.06827 |
| By: | Liu Zhang; Mark Esposito |
| Abstract: | Firms increasingly evaluate deployed AI agents using repeated human ratings, yet observed score changes may reflect the measurement process as much as changes in the agent itself. Firms also rarely know how long an evaluation remains informative as deployed systems evolve. We study reliability as a latent state using 2, 611 rubric-scored production interviews from a deployed voice-and-video interviewing agent, overlapping human reviewers, the agent's change log, and support tickets. Reviewer severity varied substantially: two reviewers assessing the same batches differed by 0.79 standard deviations on the composite index, and a shift in reviewer composition flattened the raw trend. After adjusting for reviewer effects and smoothing batch-level estimates, evaluated reliability increased by 0.53 standard deviations from March to August 2026. Reliability also shifted materially across logged deploys. At the estimated rate of unaccounted movement, forecast uncertainty reaches the magnitude of the largest observed deploy contrast after about five weeks, although this horizon is imprecisely estimated and based on a small number of step-like changes. An independent operational outcome moved in the same direction: interview-related support tickets fell by about 10% per month relative to technical-issue tickets. These data do not identify a causal effect of evaluation on performance. Instead, they show that longitudinal AI evaluation requires three checks: comparability across raters, evidence currency under drift, and corroboration with consequential operational outcomes. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.07003 |