Statistical Uncertainty in the Ranking of Journals and Universities
Economists are obsessed with rankings of institutions, journals, or scholars according to the value of some feature of interest. These rankings are invariably computed using estimates rather than the true values of such features. As a result, there may be considerable uncertainty concerning the ranks. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the ranks. We consider both the problem of constructing marginal confidence sets for the rank of, say, a particular journal as well as simultaneous confidence sets for the ranks of all journals. We apply these confidence sets to draw inferences about uncertainty in the ranking of economics journals and universities by impact factors.
The second author acknowledges support from the National Science Foundation (MMS-1949845). The third author acknowledges support from the National Science Foundation (SES-1530661). The fourth author acknowledges support from the European Research Council (Starting Grant No. 852332). We are grateful for C. Zimmermann sharing the RePEc data with us. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Magne Mogstad & Joseph Romano & Azeem Shaikh & Daniel Wilhelm, 2022. "Statistical Uncertainty in the Ranking of Journals and Universities," AEA Papers and Proceedings, vol 112, pages 630-634. citation courtesy of