Academic ranking scales in economics: Prediction and imputation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2016-020

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Forecasting Models; Simulation Methods ; Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access ; Business Administration: General
• Subject: scientometrics ; ranking ; quantile regression ; Handelsblatt ; RePEc ; Google Scholar

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zharova, Alona ; Mihoci, Andrija ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/146189
• Last update: 10.03.2025, 11:43 AM CET