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on Human Capital and Human Resource Management |
| By: | Aghion, Philippe; Bergeaud, Antonin; Blundell, Richard; Griffith, Rachel |
| Abstract: | We use matched employee–employer data from the UK to investigate the importance of social skills – in particular, teamwork and communication with co-workers – as a driver of wage growth for workers with lower formal education. We find that in social skill tasks, a significant fraction of less educated workers enjoy wage progression with tenure, and higher returns in firms with more educated co-workers. We rationalise these dynamics through a model in which social skills – initially opaque to both employee and employer – are learned about over time through complementarity with co-workers. |
| Keywords: | firm pay premium;social skills;teamwork;tenure-wage profiles;wage growth |
| JEL: | J31 J24 L25 |
| Date: | 2026–09–21 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:141241 |
| By: | Ellora Derenoncourt (Princeton University and NBER); Francois Gerard (University College London); Lorenzo Lagos (Brown University); Claire Montialoux (Sciences Po) |
| Abstract: | We study the role of union heterogeneity in shaping wages and inequality among unionized workers. Using linked employer-employee data from Brazil and job moves across multi-firm unions, we estimate over 4, 800 union-specific pay premia. Unions explain 3–4% of earnings variation. While unions raise wages on average, the standard deviation in union effects is large (6–7%). Validating our approach, wages fall in markets with higher vs. lower union premia following a nationwide right-to-work law. Linking premia to detailed data on union attributes, we find that unions with strike activity, collective bargaining agreements, internal competition, and skilled leaders secure higher wages. High-premium unions compress wage gaps by education while the average union exacerbates them. Post right-to-work, however, worker support for high-premium unions falls when between-group bargaining differentials are large. Our findings show that unions are not a monolith—their structure and actions shape their wage effects and, consequently, worker support. |
| Keywords: | Brazil |
| JEL: | J31 J51 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:pri:cepsud:350 |
| By: | Coyle, Diane; Nguyen, Nghi; Poquiz, John Lourenze; Riley, Rebecca |
| Abstract: | This paper provides early evidence that AI adoption by firms is linked to management practices in unique ways. Using panel data from the UK Management and Expectations Survey (MES), we find that firms with higher baseline management scores are significantly more likely to adopt AI. This relationship is specific to AI: the same management practices show no relationship with the adoption of other technologies such as robotics, specialized equipment, or specialized software. We also find that management capabilities related to performance measurement appear strongly related to AI adoption. These results suggest that organisational complementarities are specific to different technologies, with implications for policies to encourage AI adoption by firms. |
| Keywords: | AI; technology adoption; management practices; technology diffusion; organisational capital; decentralisation; ICT |
| JEL: | D24 L23 O31 O33 |
| Date: | 2026–09–28 |
| URL: | https://d.repec.org/n?u=RePEc:eoe:escoed:escoe-dp-2026-13 |
| By: | Hattori, Keisuke |
| Abstract: | Replacing a human follower with AI that supplies more input can reduce team output. With complementary effort, a leader works harder because a human follower observes this effort and responds with greater effort. AI that matches the follower's contribution removes this reason to work hard, so the leader works less and team output falls. In larger teams, the remaining human followers also work less. Profit-maximizing firms may nevertheless adopt AI that contributes more while the team produces less. Falling AI costs can trigger an output drop at adoption, followed by recovery. Replacing followers can mean discouraging leaders. |
| Keywords: | Artificial intelligence, Team production, Leadership, Strategic complementarity, Automation |
| JEL: | D23 J23 M54 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:344041 |
| By: | Benjamin T. Leyden; Daniela Scur |
| Abstract: | When a knowledge worker is given an AI assistant, they stop executing tasks and start delegating them. Thus, we propose that the use of AI is an act of management, which helps explain the heterogeneity in returns to AI use observed across firms. We model the return to AI as moderated by two managerial abilities: direction (how well the worker specifies what they delegate) and verification (how reliably they catch the tool's errors). As AI becomes more reliable, the skill with the higher marginal return shifts from verification to direction. To the extent that use builds skill, workers develop their managerial abilities in the normal course of work, but as the tool improves, direction will continue to build while verification will not. As increasingly fewer tasks are worth checking, the skill collapses. Workers will reach that point sooner if they only bear part of the cost of their errors. In equilibrium, the strongest verifiers tend to sort into the jobs where collapse is most costly. |
| Keywords: | artificial intelligence, delegation, managerial ability, skill formation |
| JEL: | M54 J24 O33 D24 D83 D86 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13030 |