Economics at your fingertips  

The productivity of top researchers: a semi-nonparametric approach

Lina Cortés (), Andrés Mora-Valencia and Javier Perote ()

Scientometrics, 2016, vol. 109, issue 2, No 14, 915 pages

Abstract: Abstract Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. The application uses scientific production data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis in Pers Psychol 65(1):79–119, 2012) and publications in the field of finance of 330 academic institutions (Borokhovich et al. in J Finance 50(5):1691–1717, 1995), and shows that the log-SNP distribution outperforms the lognormal and provides more accurate measures for the high quantiles of the productivity distribution.

Keywords: Research evaluation; Research productivity; Heavy tail distributions; Semi-nonparametric modeling (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: The productivity of top researchers: A semi-nonparametric approach (2016) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1007/s11192-016-2072-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2021-02-23
Handle: RePEc:spr:scient:v:109:y:2016:i:2:d:10.1007_s11192-016-2072-5