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on Human Capital and Human Resource Management |
| By: | Jean-Victor Alipour |
| Abstract: | The large-scale rise of working from home (WFH) has granted workers unprecedented autonomy over how they allocate their time and effort, while weakening employers' monitoring ability. We ask whether firms use performance-based compensation to align workers' incentives when adopting WFH. To isolate the causal impact of WFH, we use individual-level panel data and exploit workers' exposure to the WFH shock in 2020 through their 2019 job's WFH feasibility. Results show that WFH significantly raises the incidence of performance pay and its share in annual earnings between 2019 and 2023, consistent with principal-agent theory. The shift is concentrated among men and workers without collectively bargained wages. WFH simultaneously reduces uncompensated overtime, particularly in the groups with stronger shifts to performance pay. The pattern is expected when implicit incentives (overtime as a worker-quality signal) are replaced by explicit performance-based contracts (bonus for output). We find evidence for selection on gains, where those more easily induced to WFH are also more likely to adopt performance pay. |
| Keywords: | work from home, performance pay, principal-agent, monitoring, signaling |
| JEL: | J33 M52 D86 D82 J22 J81 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12680 |
| By: | Christos Makridis; Christos A. Makridis |
| Abstract: | AI is transforming work, but workers’ responses to these technologies depend not only on exposure to AI, but also on how organizations, especially managers, oversee the transition. Using longitudinal data from the Gallup Workforce Panel from 2023-2026, I examine whether managers and workplace practices shape employees’ fears that AI will eliminate their jobs. Across survey waves, roughly 3-4 percent of workers say their job is very likely to be eliminated within five years because of new technology, automation, robots, or artificial intelligence, while about 14-19 percent say it is somewhat or very likely. Concern is substantially higher among frequent AI users. Stronger workplace practices are associated with lower displacement fear: a one-standard-deviation increase in workplace quality is associated with 13-24 percent lower odds of reporting greater displacement risk, and workers reporting the highest level of organizational wellbeing support are 6-6.8 percentage points less likely to say their job is somewhat or very likely to be displaced in cross-sectional specifications. Frequent AI use is positively associated with perceived displacement risk, with estimates ranging from about 3-12.9 percentage points across the main specifications and reaching 6 percentage points in the most saturated respect model. However, this association is weaker in higher-quality workplace environments: among workers reporting the highest level of organizational wellbeing support, the frequent-AI-use premium is reduced by up to 9.0 percentage points. In short, managers play a central role in shaping how workers interpret AI adoption. |
| Keywords: | artificial intelligence, job displacement, managers, workplace practices, worker expectations, technological change, organizational capital |
| JEL: | D23 J24 J28 M12 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12678 |
| By: | Antonio Cabrales; Esther Hauk |
| Abstract: | Many workers value purpose and meaning in their jobs alongside income, and firms need to align these preferences with profit goals. This paper develops a dynamic model in which firms invest in "purpose" to enhance job meaning and motivate effort. Workers, who differ in productivity, choose both productive and socialization effort, gaining utility from income and meaning. Purpose accumulates over time through firm investment and interacts with socialization to generate meaning, which boosts productivity. Firms invest in purpose only insofar as it raises profits. We characterize the unique equilibrium, including steady state and transition dynamics. Meaning and purpose rise with the importance workers place on meaning and with firm's patience, but fall with depreciation and socialization costs. The relationship with workers' share of output is non- monotonic. We also show that some intermediate level of heterogeneity in skills is best for performance. Compared to a worker-owned firm, profit-maximizing firms underinvest in purpose, highlighting a misalignment between firm incentives and worker preferences. The model provides insight into when and why firms adopt purpose-driven practices and underscores the role of diversity in fostering meaning at work. |
| Keywords: | diversity in work, investment in purpose, meaning at work, personnel motivation |
| JEL: | M50 M52 L23 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1578 |
| By: | Diya Abraham (Department of Economics, University of Reading); Ondrej Krcal (Department of Economics, Masaryk University, Brno) |
| Abstract: | We investigate how workers’ motivation is influenced by whether they feel trusted or not by managers. In a laboratory experiment, responsibility for a manager’s earnings is divided unequally between two workers. We vary whether this decision is made by the manager or a random device on the manager’s behalf. Importantly, having more/less responsibility does not affect the workers’ wages. Despite this, we find that workers provide less effort when they are deliberately, vs. randomly, assigned lower responsibility. We find a smaller, less robust positive effect of learning one is more trusted. We examine two inter-related mechanisms and show that both beliefs about expected effort as well as emotions triggered when learning about the manager’s decision help explain our results. |
| Keywords: | trust, vulnerability, motivation, social comparison |
| JEL: | C90 D23 D91 J53 |
| Date: | 2026–05–25 |
| URL: | https://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2026-03 |
| By: | Samantha Horn (University of Chicago); Peter Schwardmann (Carnegie Mellon University); Egon Tripodi (Hertie School) |
| Abstract: | Evaluative social interactions are pervasive in labor markets. Inequality in these settings can arise not only from how individuals are treated or perform when evaluated, but from whether they enter evaluation at all. We study these margins in the context of social anxiety. In a controlled online experiment (N = 922), applicants decide whether to complete a live video interview that determines a monetary hiring bonus. We find that inequities associated with social anxiety are concentrated in participation rather than in performance or treatment. Socially anxious applicants are substantially less willing to interview, hold more pessimistic beliefs about being hired, and correctly anticipate a worse experience. Yet they perform no worse and are evaluated no differently. Interview experience does not attenuate the relative pessimism of socially anxious individuals, a pattern that is inconsistent with Bayesian updating under comparable signals. We use our rich audio-visual data and open-ended reflection texts to show that, instead, socially anxious applicants interpret similar interactions more negatively. We then provide evidence on organizational interventions aimed at closing social anxiety gaps. Finally, we show that social anxiety explains a meaningful share of inequalities commonly attributed to gender and social skill differences and is associated with significant earnings gaps in national data. |
| Keywords: | social anxiety; job interviews; beliefs; mental health; discrimination; learning; |
| JEL: | D83 J71 I10 C90 |
| Date: | 2026–06–01 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:574 |
| By: | Bart Hobijn; Andre Kurmann; Tristan Potter |
| Abstract: | Non-compete agreements (NCAs) are pervasive even in low-wage labor markets, yet most evidence relies on variation in enforceability rather than NCA incidence. Using longitudinal data from the NLSY97, we study how signing an NCA affects wage trajectories and job tenure. Exploiting complete work histories and applying a clean-controls local projections difference-in-difference design, we find a striking divergence: NCAs are associated with significantly slower wage growth for low-education workers over four years, but faster wage growth for high-education workers. Effects on job tenure are imprecisely estimated for both groups. |
| Keywords: | Non-compete; low-wage labor markets; Local projections; Difference-in-difference |
| JEL: | J31 J41 J62 K31 |
| Date: | 2026–03–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:103280 |
| By: | R. Jason Faberman; Andreas I. Mueller; Aysegul Sahin |
| Abstract: | This paper studies gender gaps in labor-market outcomes, with a focus on job ladder dynamics. We show that women experience substantially lower wage growth conditional on prior wages despite nearly identical job-to-job transition rates for men and women. To reconcile these observations, we document gender differences in the valuation of nonwage job amenities and in job search behavior, and develop a multi-dimensional job-ladder model with endogenous search effort where workers value both wages and amenities. The model allows for gender heterogeneity in separation rates, search effort, the value of nonemployment, amenity valuations, and bargaining power, enabling a joint analysis of gender wage and employment gaps. A quantitative decomposition shows that differences in preferences for nonwage amenities account for nearly 40 percent of the gender pay gap. Differences in the value of nonemployment and bargaining power explain most of the remainder, with only a limited role for differences in separation rates and search behavior. Finally, we show that increases in job amenities—such as the expansion of remote work—raise the gender wage gap while reducing gender differences in employment. |
| Keywords: | Gender wage gap; Job search; job amenities; On-the-job search |
| JEL: | J16 J60 |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:103282 |
| By: | Yuki Ono; Fumio Ohtake; Nobuyuki Hanaki |
| Abstract: | We study a voluntary contribution mechanism (VCM) with intragroup competition, in which individuals’ marginal returns depend on their contribution rank within the group. By systematically varying the strength of rank-based incentives, we derive theoretical predictions and test them in a laboratory experiment. We find that intragroup competition significantly increases contributions, but the response is highly nonlinear: contributions increase sharply once incentives become sufficiently strong to support an efficient equilibrium, but further increases in incentive intensity generate only modest additional effects. These findings highlight how incentive design shapes cooperation and provide new insights into the effects of relative performance incentives in public goods environments. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1313 |
| By: | Mallory Avery; Edwin Ip; Andreas Leibbrandt; Joseph Vecci |
| Abstract: | Recent technological advancements are reshaping pathways to employment by automating the interview process. Asynchronous interviews, in which job applicants submit answers to interview questions via an online platform without interacting with an interviewer, are replacing more traditional face-to-face job interviews. At the same time, AI algorithms are now widely used to assess these interview answers. In this paper, we use a field experiment to comprehensively study how these new technologies affect applicants and employersin the recruitment process. Over 3, 000 job applicants are randomized into asynchronous audio or video interviews, live online interviews, and a control group. Their job interviews are then assessed by both professional recruiters and a commercial AI recruitment tool used by most Fortune 100 companies. We find that asynchronous interviews cause an over 50% decrease in application continuation, including among the most qualified applicants, and that this decline is largest for women. A complementary vignette experiment provides evidence that this deterrence is driven by perceptions about the competitiveness and fairness of the recruitment process. In terms of assessments, we find that the AI evaluation tool scores women and underrepresented racial minorities higher than human evaluators, while the opposite is true for men, Whites and Asians. We track our applicants’ subsequent labor market outcomes and find that the AI assessment tool predicts subsequent employment success substantially better than human recruiters, suggesting that AI captures soft skills and potential that humans overlook. In addition, we provide evidence that, unlike AI, human recruiters’ assessments suffer from multiple cognitive biases. Our findings provide some of the first key evidence on how recent technological advances are transforming the hiring process. |
| Keywords: | Technological Change, Artificial Intelligence, Gender, Field Experiment |
| JEL: | C93 J23 J71 J78 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:mos:moswps:paper_1775627424263_198 |