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on Human Capital and Human Resource Management |
| By: | Amrote Seyoum Getu (TU Berlin); Radosveta Ivanova-Stenzel (TU Berlin); Michel Tolksdorf (TU Berlin); Eva Wiese (TU Berlin) |
| Abstract: | We study how perceived teammate identity shapes effort allocation in hybrid human–artificial agent teams in collaborative environments. In a real-effort online experiment and under different incentive schemes, fixed payment and team competition, participants work in a team where each team member is responsible for their own segment but can observe and intervene in teammates’ segments. Teammates are algorithmically controlled agents, framed as either artificial or human-like, with their actual behavior held constant across treatments. Participants exert approximately one-third less compensatory effort when teammates are presented with human-like cues than when they are transparently labeled as artificial, and this difference is robust across both incentive schemes. The pattern is consistent with a model in which perceived human identity reduces the non-monetary benefit of helping. The result highlights that in hybrid teams, the labeling and presentation of artificial collaborators have economically meaningful consequences for cooperative effort and the organization of team production. |
| Keywords: | human–ai collaboration; online experiment; team incentives; teamwork; tournaments; |
| JEL: | C91 D29 D83 D89 |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:580 |
| By: | Claudio Schilter; Thea S. Zoellner |
| Abstract: | Many firms in tight labor markets recruit for highly gender-segregated occupations, leaving them, in effect, drawing from only half the potential labor pool. We study how firms can use fringe benefits to attract workers and influence occupational choice. Unlike occupation-specific recruiting strategies, fringe benefits can be added to any occupation; they are also easily tailored, implemented, and scaled. Using a personalized and incentivized discrete choice experiment, we present participants with job options that match their skills and work task preferences. Within each option, we vary salary, occupation, and fringe benefits that adolescents had classified as gender-neutral, male-typed, or female-typed. We find that fringe benefits are highly valued: their effect is around a third as large as having prior interest in the occupation. Neutral and stereotypical benefits in particular improve perceptions of the firm across multiple dimensions. Furthermore, we find substantial gender differences. Female-typed benefits deter male participants, whereas all other benefit-participant combinations have a positive effect. Female participants value benefits more when those benefits are paired with an atypical occupation, and we find suggestive evidence that this reflects benefits alleviating concerns about limited support in such roles. In addition, male-typed benefits can increase the attractiveness of gender-atypical occupations for female participants. Female participants who consider atypical occupations place relatively higher value on these benefits and tend to exhibit less traditionally feminine preferences. We also find suggestive evidence that anticipated social reactions, particularly from parents, accompany these choices. |
| Keywords: | occupational choice, fringe benefits, gender |
| JEL: | J20 J24 J28 J16 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0259 |
| By: | Nicholas Bloom; Gideon Moore; Lisa K. Simon; Caelan Wilkie-Rogers |
| Abstract: | Economists increasingly use professional profile data to reconstruct employment histories and measure skill supply. We show that these records are not fixed historical snapshots, but mutable accounts that workers revise over time. Using monthly vintages of Revelio Labs data from 2020–2026, we document that 19.7 percent of established U.S. LinkedIn users retroactively edit the title or description of a job they have already left. These “time-travel” edits are closely tied to labor market transitions: around such edits, workers are much more likely to change employers as compared to later-editing users. This mutability can bias historical measures of skills, but it also reveals workers’ beliefs about which skills are in demand. Retroactive edits show sharp post-2022 increases in AI-related language and recent reductions in work-from-home and DEI language. Finally, LLM-associated writing markers surge after ChatGPT, especially among less-educated groups and MBAs from lower-ranked programs, revealing heterogeneous AI-assisted profile editing. |
| JEL: | J0 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35546 |
| By: | Joshua S. Gans |
| Abstract: | Can automation harm workers without replacing them? We study jobs in which workers value both producing useful output and knowing that the output depends on their own contribution. A credible machine alternative can weaken that second source of meaning even when the firm retains the worker. Our model shows that this loss raises compensation when wages adjust fully; when they adjust only partly, workers bear some of the loss themselves. It can also make automation more likely. An external developer may profit by publicly demonstrating a machine before licensing it, because the demonstration lowers the value of the human alternative. This "meaning externality" can create demand for the machine and make profitable development socially harmful. Better technical quality and greater public salience have different effects: quality improves output, while salience alone weakens human work. Automation can, therefore, reduce the value of work before it eliminates jobs. |
| JEL: | D62 D91 J24 J31 J32 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35559 |
| By: | Alexander Bick; Adam Blandin; David J. Deming; Tyler R. Schumacher |
| Abstract: | We measure how workers use genAI for their jobs in a nationally representative survey linking genAI adoption to detailed occupations and tasks. Our data provide the first task-level genAI adoption indexes, which we show can inform analyses of genAI’s labor market impact. Exposure scores explain some, but far from all, of the variation in adoption across occupations and tasks. We also distinguish our indexes from measures based on genAI platform chat logs, which differ conceptually and tend to over-classify chats into generic activities spanning many occupations. Finally, we highlight that current adoption is widespread but shallow: genAI is used across many occupations and tasks, yet within most of them, fewer than half of workers adopt. This indicates substantial variation among workers doing very similar work, suggesting that understanding who adopts may matter as much as understanding which tasks genAI assists. |
| JEL: | J24 O3 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35677 |
| By: | Dalle, Axana (Ghent University); Moens, Eline (Ghent University); Van Ootegem, Luc (Ghent University); Verhofstadt, Elsy (Ghent University); Baert, Stijn (Ghent University) |
| Abstract: | This systematic literature review provides a state-of-the-art overview of the effectiveness of workplace interventions aimed at improving workers’ mental well-being. Specifically, it examines which types of interventions are effective for which aspects of mental well-being, both in the short and long term. To this end, we identify 125 randomised controlled trials comprising 147 interventions that vary in their objectives (prevention, treatment, or reintegration), targets (workers or work), methods (cognitive, meditation-based, physical, ergonomic, organisational, or multimethod) and delivery channels (offline, online or hybrid). Mental well-being outcomes are classified as biological and self-reported indicators of the three components of subjective mental well-being (evaluative, affective, and eudaimonic well-being). Overall, we find that, for preventing and treating early mental health symptoms, cognitive or meditation-based interventions targeting personal coping resources appear most promising. In contrast, successful reintegration seems to require a combination of cognitive and organisational interventions, with the latter addressing job demands and resources. |
| Keywords: | mental well-being, workplace, interventions, randomised controlled trials |
| JEL: | C93 I31 J28 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18907 |