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on Sociology of Economics |
| By: | Richard B. Freeman; Danxia Xie; Hanzhe Zhang; Hanzhang Zhou |
| Abstract: | We analyze the academic affiliations of nearly 6, 000 award-winning researchers in 18 major fields in the natural sciences, engineering, and social sciences from the 1820s to the 2020s, focusing on the 1960s onward. The analysis reveals a trend of declining concentration in the institutional affiliations of award-winning researchers, shifting from a few science-strong universities in high-income countries to a more diverse set of institutions across the world. The decline in concentration is observed in all fields except one: economics. The institutional affiliations of prizewinning economists have become more concentrated over time, making economics the most concentrated field. We associate the higher concentration of prizewinning work in economics with the field’s stronger sorting by institutional prestige, its lower reliance on specialized equipment and instruments, and its assessment of findings based on a synthesis of evidence rather than on decisive experiments or proofs. We discuss the benefits and costs of this high and rising institutional concentration of prizewinning economists. |
| JEL: | A11 A12 A14 I23 J44 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35875 |
| By: | Daniel P. Gross; Dror Shvadron; Hansen Zhang |
| Abstract: | Large language models are diffusing rapidly into science, raising concerns that delegating research writing to AI may undermine the development of expertise that scientific training is supposed to build. We measure AI-generated writing in the dissertations of a near-census of US STEM PhD graduates from 2019 to 2026 and link graduates to their subsequent employment. AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rapidly growing. Use is more common among students from non-English-speaking countries and at lower-ranked programs. Among graduates in the same program and cohort, those whose dissertations contain AI writing are less likely to enter academic research careers and more likely to enter non-tenure-track roles or industry, with this pattern strongest among US-origin students. At the same time as AI may increase research productivity, it thus appears to also be displacing a potentially important portion of the work through which scientific expertise is developed. |
| JEL: | I23 J24 O31 O33 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35859 |
| By: | Steve Lawford |
| Abstract: | When manuscripts become cheap to produce but expert evaluation stays scarce, submission rights acquire value. A non-transferable author quota turns submission into a revealed-priority decision: authors nominate their top projects using only ordinal information, and, with informative rankings, adding candidates cannot worsen the selected portfolio. The cost is inflexibility in allocating review capacity across authors and realised projects. In a tractable benchmark, I derive the exact equal-load condition under which the quota's robustness to heterogeneous fee sensitivity and cardinal miscalibration outweighs that cost. With bounded project values, abundance can turn an equal-load fee into an access screen, while the quota preserves within-author curation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.04688 |