Academic Ranking Scales in Economics: Prediction and Imputation
SFB 649 Discussion Paper 2016-020, Economic Risk, Berlin
40 Pages Posted: 27 Jun 2016
Date Written: May 9, 2016
Publications are a vital element of any scientist’s career. It is not only the number of media outlets but aslo the quality of published research that enters decisions on jobs, salary, tenure, etc. Academic ranking scales in economics and other disciplines are, therefore, widely used in classification, judgment and scientific depth of individual research. These ranking systems are competing, allow for different disciplinary gravity and sometimes give orthogonal results. Here a statistical analysis of the interconnection between Handelsblatt (HB), Research Papers in Economics (RePEc, here RP) and Google Scholar (GS) systems is presented. Quantile regression allows us to successfully predict missing ranking data and to obtain a so-called HB Common Score and to carry out a cross-rankings analysis. Based on the merged ranking data from different data providers, we discuss the ranking systems dependence, analyze the age effect and study the relationship between the research expertise areas and the ranking performance.
Keywords: scientometrics, ranking, quantile regression, Handelsblatt, RePEc, Google Scholar
JEL Classification: C14, C53, C81, M10
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