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on Sociology of Economics |
| By: | Hsieh, Chih-Sheng (Chinese University of Hong Kong); Konig, Michael D. (Centre for Economic Policy Research; Swiss Economic Institute; VU Amsterdam); Liu, Xiaodong (University of Colorado Boulder); Zimmermann, Christian (Federal Reserve Bank of St. Louis) |
| Abstract: | We study the impact of research collaborations in coauthorship networks on research output and how optimal funding can maximize it. Through the links in the collaboration network, researchers create spillovers not only to their direct coauthors but also to researchers indirectly linked to them. We characterize the equilibrium when agents collaborate in multiple and possibly overlapping projects. We bring our model to the data by analyzing the coauthorship network of economists registered in the RePEc Author Service. We rank the authors and research institutions according to their contribution to the aggregate research output and thus provide a novel ranking measure that explicitly takes into account the spillover effect generated in the coauthorship network. Moreover, we analyze funding instruments for individual researchers as well as research institutions and compare them with the economics funding program of the National Science Foundation. Our results indicate that, because current funding schemes do not take into account the availability of coauthorship network data, they are ill-designed to take advantage of the spillover effects generated in scientific knowledge production networks. |
| Keywords: | coauthor networks; scientific collaboration; spillovers; key player; research funding; economics of science |
| JEL: | C72 D43 D85 L14 Z13 |
| Date: | 2018–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2018-028&r=sog |
| By: | Joe Hirschberg (Department of Economics, The University of Melbourne); Jenny Lye (Department of Economics, The University of Melbourne); |
| Abstract: | The Australian Business Deans Council (ABDC) have graded journals in the fields of Economics and Statistics to evaluate the quality of research. This paper examines the consistency of these grades with 44 bibliometric indicators of journal quality and measures of interrater agreement. First, we categorise the bibliometrics employing a unique cluster analysis based on an interrater agreement statistic. Then, we determine which journals have been assigned ABDC grades that do not reflect the rank of the bibliometrics. These cases provide an indication of the extent to which the ABDC journal grades are determined by non-bibliometric factors. |
| Keywords: | Hirsch index, citation count, impact factor, downloads, Euclidian citation score, Altmetrics, interrater agreement statistics, cluster analysis, heatmaps. |
| JEL: | C49 O30 Y10 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:mlb:wpaper:2041&r=sog |
| By: | Yann Giraud (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique); José Edwards (Universidad Adolfo Ibáñez [Santiago]); Christophe Schinckus (Royal Melbourne Institute of Technology) |
| Abstract: | Quantitative approaches are not yet common among historians and methodologists of economics, although they are in the study of science by librarians, information scientists, sociologists, historians, and even economists. The main purpose of this essay is to reflect methodologically on the historiography of economics: is it witnessing a quantitative turn? Is such a turn desirable? We answer the first question by pointing out a "methodological moment", in general, and a noticeable rise of quantitative studies among historians of economics during the past few years. To the second question, all contributors to this special issue bring relatively optimistic answers by highlighting the benefits of using quantitative methodologies as complements to the more traditional meta-analyses of both historians and methodologists of economics. |
| Keywords: | Topic modeling,Network analysis,Quantitative statements,Bibliometrics,Historiography of economics |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-01876415&r=sog |
| By: | Klaus Wohlrabe; Felix de Moya Anegon; Lutz Bornmann |
| Abstract: | While output and impact assessments were initially at the forefront of institutional research evaluations, efficiency measurements have become popular in recent years. Research efficiency is measured by indicators that relate research output to input. The additional consideration of research input in research evaluation is obvious, since the output should be related to the input. The present study is based on a comprehensive dataset with input- and output-data for 50 US universities. As input, we used the amount of budget, and as output the number of highly-cited papers. We employed Data Efficiency Analysis (DEA), Free Disposal Hull (FDH), and two more robust models: the order-m and order-α approach. The results of the DEA and FDH analysis show that Harvard University and Rice University can be called especially efficient compared to the other universities. While the strength of Harvard University lies in its high output of highly-cited papers, the strength of Rice University is its small input. In the order-α and order-m frameworks, Harvard University remains efficient, but Rice University becomes super-efficient. We produced university rankings based on adjusted efficiency scores (subsequent to regression analyses), in which single covariates (e.g. the disciplinary profile) are held constant. |
| Keywords: | University; efficiency analysis; regression analysis; normalized citation impact |
| JEL: | I21 I23 D61 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ifowps:_264&r=sog |