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The Great Divide in Scientific Productivity. Why the Average Scientist Does Not Exist

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  • Kelchtermans, Stijn

    (Hogeschool-Universiteit Brussel (HUB), Belgium
    Katholieke Universiteit Leuven, Belgium)

  • Veugelers, Reinhilde

    (BEPA, European Commission, Brussels, Belgium
    Katholieke Universiteit Leuven, Belgium)

Abstract

We use a quantile regression approach to estimate the eects of age, gender, research funding, teaching load and other observed characteristics of academic researchers on the full distribution of research performance, both in its quantity (publications) and quality (citations) dimension. Exploiting the panel nature of our dataset, we estimate a correlated random-eects quantile regression model, accounting for unobserved heterogeneity of researchers. We employ recent advances in quantile regression that allow its application to count data. Estimation of the model for a panel of biomedical and exact scientists at the KU Leuven in the period 1992-2001 shows strong support for our quantile regression approach, revealing the dierential impact of almost all regressors along the distribution. We also .nd that variables like funding, teaching load and cohort have a dierent impact on research quantity than on research quality.

Suggested Citation

  • Kelchtermans, Stijn & Veugelers, Reinhilde, 2009. "The Great Divide in Scientific Productivity. Why the Average Scientist Does Not Exist," Working Papers 2009/01, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    • Handle: RePEc:hub:wpecon:200901
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    economics of science; research productivity; quantile regression; count data; random effects;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L31 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Nonprofit Institutions; NGOs; Social Entrepreneurship
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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