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The productivity of top researchers: a semi-nonparametric approach

Author

Listed:
  • Lina M. Cortés

    () (Universidad EAFIT)

  • Andrés Mora-Valencia

    () (Universidad de los Andes)

  • Javier Perote

    () (University of Salamanca)

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.

Suggested Citation

  • Lina M. Cortés & Andrés Mora-Valencia & Javier Perote, 2016. "The productivity of top researchers: a semi-nonparametric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 891-915, November.
  • Handle: RePEc:spr:scient:v:109:y:2016:i:2:d:10.1007_s11192-016-2072-5
    DOI: 10.1007/s11192-016-2072-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lina M. Cortés & Javier Perote & Andrés Mora-Valencia, 2017. "Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach," DOCUMENTOS DE TRABAJO CIEF 015923, UNIVERSIDAD EAFIT.
    2. repec:spr:scient:v:115:y:2018:i:1:d:10.1007_s11192-018-2644-7 is not listed on IDEAS
    3. repec:eee:phsmap:v:485:y:2017:i:c:p:35-47 is not listed on IDEAS
    4. Cortés, Lina M. & Mora-Valencia, Andrés & Perote, Javier, 2017. "Measuring firm size distribution with semi-nonparametric densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 35-47.

    More about this item

    Keywords

    Research evaluation; Research productivity; Heavy tail distributions; Semi-nonparametric modeling;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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