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The productivity of top researchers: A semi-nonparametric approach

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  • Lina M. Cortés

    ()

  • Javier Perote

    ()

  • Andrés Mora-Valencia

    ()

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. To compare the results, we use research performance data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis, 2012) and show how the log-SNP distribution provides more accurate measures of the performance of the top researchers in their respective fields of knowledge.

Suggested Citation

  • Lina M. Cortés & Javier Perote & Andrés Mora-Valencia, 2016. "The productivity of top researchers: A semi-nonparametric approach," DOCUMENTOS DE TRABAJO CIEF 014437, UNIVERSIDAD EAFIT.
  • Handle: RePEc:col:000122:014437
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. repec:spr:scient:v:115:y:2018:i:1:d:10.1007_s11192-018-2644-7 is not listed on IDEAS

    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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