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On the Shapley value and its application to the Italian VQR research assessment exercise

Camil Demetrescu, Francesco Lupia, Angelo Mendicelli, Andrea Ribichini, Francesco Scarcello and Marco Schaerf

Journal of Informetrics, 2019, vol. 13, issue 1, 87-104

Abstract: Research assessment exercises have now become common evaluation tools in a number of countries. These exercises have the goal of guiding merit-based public funds allocation, stimulating improvement of research productivity through competition and assessing the impact of adopted research support policies. One case in point is Italy's most recent research assessment effort, VQR 2011–2014 (Research Quality Evaluation), which, in addition to research institutions, also evaluated university departments, and individuals in some cases (i.e., recently hired research staff and members of PhD committees). However, the way an institution's score was divided, according to VQR rules, between its constituent departments or its staff members does not enjoy many desirable properties well known from coalitional game theory (e.g., budget balance, fairness, marginality). We propose, instead, an alternative score division rule that is based on the notion of Shapley value, a well known solution concept in coalitional game theory, which enjoys the desirable properties mentioned above. For a significant test case (namely, Sapienza University of Rome, the largest university in Italy), we present a detailed comparison of the scores obtained, for substructures and individuals, by applying the official VQR rules, with those resulting from Shapley value computations. We show that there are significant differences in the resulting scores, making room for improvements in the allocation rules used in research assessment exercises.

Keywords: Research assessment; Bibliometrics; Shapley value; Research productivity (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:13:y:2019:i:1:p:87-104

DOI: 10.1016/j.joi.2018.11.008

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