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on Sociology of Economics |
| By: | Marianna Brunetti (CEIS & DEF, University of Rome "Tor Vergata", GLO and Cefin); Annalisa Fabretti (CEIS & DEF, University of Rome "Tor Vergata") |
| Abstract: | This study is the first modeling academic career progression using multi-state models, an approach that allows to compute additional quantities of interest never produced in this context, such as the probability of attaining specific academic positions over given time horizons and the average time required to reach them. Potential gender differences along these dimensions are assessed by comparing these metrics computed for male and female scholars separately, while accounting for productivity, individual preferences, and even seniority, which are often cited as explanations for women’s disadvantage in academic careers. Leveraging a suitably-built highly informative dataset covering the universe of professors having worked in Italian universities between 2016 and 2021, we show a gap in the probabilities of career advancement that reduces with the career horizon but never vanishes, stabilizing around 2 (3) percentage points for those starting their career as assistant (associate) professors. In addition, we find that female scholars require up to two additional years to reach the highest academic rank (full professorship) compared with otherwise equivalent male counterparts, a phenomenon we label the “pink queue”. Finally, we find a systematic misalignment in career advancement probabilities between male and female scholars with comparable productivity, whereby women achieve the same advancement chances as men only when they attain higher productivity levels, suggesting the presence of a potential “double standard”. |
| Keywords: | Gender gap, Markov chain, university, productivity, pink queue, double standard |
| JEL: | J16 J71 |
| Date: | 2025–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:618 |
| By: | Zhang, Xi; Fang, Di; Nayga, Rodolfo M. |
| Keywords: | Health Economics and Policy |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343534 |
| By: | Matej Bajgar (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic); Suren Karapetyan (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic) |
| Abstract: | We examine whether competitive research grants generate new research led by the supported principal investigators (PIs), distinguishing publications where the PI made a substantial intellectual contribution (first or last authorship) from all publications. Using data on Czech medical research grants awarded between 2015 and 2019, we apply augmented inverse probability weighting and regression discontinuity designs, comparing funded projects with unfunded projects just below the funding cutoff. Both methods find that grants increase total publications over five years by approximately 2 papers, or 17%. Regression discontinuity estimates further indicate that grants have disproportionately large effects on publications involving substantial intellectual contribution from the PI, increasing first/last-author publications by 1.8 papers, or 40%. Standard outcome measures that ignore authorship position may significantly understate the impact of grants on independent, PI-led scientific output. |
| Keywords: | Regression discontinuity design, Research funding, Scientific productivity |
| JEL: | O38 O30 I23 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2025_30 |
| By: | Kristian S. Blickle; Cecilia Parlatore |
| Abstract: | Banks must extract useful signals of a potential borrower’s quality from a large set of possibly informative characteristics when making lending decisions. A model that speaks to how banks specialize in lending to an industry in order to better extract signals from data, can potentially be applied to a number of real-world scenarios. In this post, we apply lessons from such a model to a topic of timely relevance in economics: job market recommendation letters. Institutions looking to hire new economists must evaluate PhD applicants based on limited and often noisy signals of future performance, including letters of recommendation from these applicants’ advisors or co-authors. Using insights from our model, we argue that the value of these letters depends on who reads them. |
| Keywords: | specialized lending; recommendation letters |
| JEL: | G20 G21 |
| Date: | 2025–12–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102235 |
| By: | Andres Rodriguez-Pose; Leiboyu Xiang; Neil Lee |
| Abstract: | This paper presents the first systematic city-level mapping of global scientific talent, analysing the top 200, 000 star scientists across 3, 635 cities worldwide annually between 2019 and 2023. We use a novel Knowledge Generation Index (KGI) that combines researcher quantity with research impact to reveal extreme spatial concentration in knowledge production. Just four cities — New York, Boston, London and the San Francisco Bay Area — host 12% of the world's star scientists, while much of the Global South remains virtually excluded from frontier research. Beijing's ascent into the global top ten represents a rare challenge to established hierarchies. Our analysis uncovers striking disciplinary variations. Resource-intensive fields like clinical medicine cluster heavily and traditionally dispersed disciplines are increasingly gravitating toward major hubs. Despite these differences, concentration is intensifying across most scientific fields. Even the pandemic's remote collaboration experiment failed to level the playing field. Established innovation centres continued strengthening their advantages while peripheral regions fell further behind. Overall, we find that geography remains destiny, with profound implications for innovation policy confronting widening spatial inequalities in global scientific capacity. |
| Keywords: | Star scientists; geography of knowledge; innovation agglomeration; spatial inequality |
| JEL: | O25 O31 R12 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2540 |