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on Sociology of Economics |
| By: | Prashant Garg; Thiemo Fetzer |
| Abstract: | We analyze over 44, 000 economics working papers from 1980–2023 using a custom language model to construct knowledge graphs mapping economic concepts and their relationships, distinguishing between general claims and those supported by causal inference methods. The share of causal claims within papers rose from about 4% in 1990 to 28% in 2020, reflecting the “credibility revolution.” Our findings reveal a trade-off between factors enhancing publication in top journals and those driving citation impact. While employing causal inference methods, introducing novel causal relationships, and engaging with less central, specialized concepts increase the likelihood of publication in top 5 journals, these features do not necessarily lead to higher citation counts. Instead, papers focusing on central concepts tend to receive more citations once published. However, papers with intricate, interconnected causal narratives—measured by the complexity and depth of causal channels—are more likely to be both published in top journals and receive more citations. Finally, we observe a decline in reporting null results and increased use of private data, which may hinder transparency and replicability of economics research, highlighting the need for research practices that enhance both credibility and accessibility. |
| Keywords: | knowledge graph, credibility revolution, causal inference, narrative complexity, null results, private data, large language models |
| JEL: | A10 B41 C18 C80 D83 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11462 |
| By: | Francesco Giffoni; Emanuela Sirtori; Louis Colnot |
| Abstract: | This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy physics, astronomy, and many others. We argue that existing literature fails to address the collaborative nature of research by overlooking the relationship between coauthorship and scientists productivity. Using the Marginal Cost of Production (MCP) approach, we first highlight the methodological limitations of ignoring this relationship, then propose a generalised MCP model to value co-authorship. As a case study, we examine High-Energy Physics (HEP) collaborations at the Large Hadron Collider (LHC) at CERN, analysing approximately half a million scientific outputs by over 50, 000 authors from 1990 to 2021. Our findings indicate that collaborative adjustments yield monetary valuations for subsets of highly collaborative papers up to 3 orders of magnitude higher than previous estimates, with elevated values correlating with high research quality. This study contributes to the literature on research output evaluation, addressing debates in science policy around assessing research performance and impact. Our methodology is applicable to authorship valuation both within academia and in large-scale scientific collaborations, fitting diverse research impact assessment frameworks or as self-standing procedure. Additionally, we discuss the conditions under which this method may complement survey-based approaches. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10278 |