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on Sociology of Economics |
| By: | Zhengyi Yu |
| Abstract: | This paper studies the impact of AI on productivity and inequality by focusing on the introduction of AlphaFold2. This AI algorithm can accurately predict protein structures, which were traditionally characterized by structural biologists through experiments. To capture the impact of AI on structural biologists at scale, I implement a difference-in-differences strategy comparing them to life scientists in other fields. While structural biologists did not change their overall number of publications with the availability of AlphaFold2, they experienced a 10% increase in citations to their new projects, a 4% rise in publications in high-impact journals, and a shift from their original research trajectory. However, the emergence of AI intensifies citation polarization between highly cited and less-cited researchers. Consistent with this growing inequality, highly cited scientists are twice as likely to incorporate AlphaFold2 successfully into their research as their less-cited peers. In addition, AI affects the next generation of researchers: the average years of experience of leading authors in protein structure papers increase after the emergence of AI. |
| Keywords: | AI, technology, labor productivity, inequality |
| JEL: | J21 J24 O33 D63 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12462 |
| By: | Dominic Rohner; Oliver Vanden Eynde; Philine Widmer |
| Abstract: | Academic freedom has come under growing strain across the world. To study whether and how academics react to political pressure, we exploit a natural experiment: the U.S. government's ``blacklist'' of undesirable words released in early 2025. We find that the release of this list leads to a sharp reduction in the use of banned words in sensitive contexts among economists working at universities that rely heavily on NSF funding. The drop is particularly marked for content related to gender, race, and environment. Our findings are consistent with scholars responding strongly to political pressure through career incentives. |
| Keywords: | censorship, science, academic freedom, science funding |
| JEL: | D73 I23 O38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12498 |
| By: | Sofie Cairo; Ria Ivandić; Anne Sophie Lassen; Valentina Tartari |
| Abstract: | Persistent gender gaps in the labor market are largely driven by the underrepresentation of women at the top of most professions. We study how parenthood shapes gender gaps in academic careers using population-wide administrative and survey data linked to productivity and promotion records. Parenthood marks a sharp divergence in academic careers: one in three women exit academia following motherhood. Men also experience a decline in academic employment after fatherhood, but the effects are substantially smaller. For mothers, childbirth leads to a persistent decline in both tenure attainment and research output, while men’s trajectories on these margins are unaffected by parenthood. The child penalty on tenure is driven primarily by women’s higher exit rates from academia. Gender differences in career aspirations do not explain these findings; instead, childcare and mobility constraints play a central role. Child penalties are exacerbated in highly competitive environments and environments without senior female role models. |
| JEL: | A11 D63 J13 J16 J44 |
| Date: | 2026–02–18 |
| URL: | https://d.repec.org/n?u=RePEc:bdp:dpaper:0092 |
| By: | David Popp; Myriam Gregoire-Zawilski; Lizhen Liang; Daniel Acuna |
| Abstract: | Does government funding influence the choice of research topics? Novel grant-making modalities such as the Advanced Research Projects Agency-Energy (ARPA-E) program aim to encourage scientists to take on difficult-to-solve, wicked societal problems such as clean energy. Yet little causal evidence exists linking funding and research direction, with most existing studies focusing on health sciences. We provide new evidence on the effect of funding on clean energy research, addressing two questions: (1) Do scientists change the focus of their research in response to targeted government funding opportunities? (2) If so, what types of calls for funding best attract new researchers? Using data on grants from the Department of Energy and National Science Foundation, we combine text and regression analyses to compare the publication trajectories of funded scientists to a set of matched controls. After funding, the research of funded scientists becomes more similar to the grant topic than that of the matched controls. The effect is largest for ARPA-E, which explicitly aims to attract new scientists to clean energy research, suggesting that agency efforts to attract new researchers to a topic area can succeed. General calls for funding such as offered by traditional NSF directorates generate less movement. |
| JEL: | O38 Q48 Q55 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34856 |
| By: | Bulla, Martin (Max Planck Institute For Ornithology); Mikula, Peter |
| Abstract: | Research funding schemes are increasingly struggling to reliably distinguish scientific merit through traditional scoring. Using the most recent evaluations of the EU Marie Skłodowska-Curie Actions postdoctoral fellowships as a case study, we show how the rapid institutional adoption of Large Language Models coincides with unprecedented score compression. With only ~5% of proposals now falling below the 70% quality threshold, down from ~20% in previous years. We argue that “excellence saturation” has reached a tipping point that exposes the structural limits of fine-grained peer review and alters reviewer decision-making dynamics where funding decisions resemble a lottery. This shift to AI-assisted grant writing effectively decouples a proposal’s form from its scientific substance, necessitating a transition from fine-grained ranking toward managing an abundance of excellence through alternative allocation mechanisms, such as funding lotteries. |
| Date: | 2026–02–19 |
| URL: | https://d.repec.org/n?u=RePEc:osf:metaar:d8gcu_v1 |