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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

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    Cited by:

    1. Popp, David & Santen, Nidhi & Fisher-Vanden, Karen & Webster, Mort, 2013. "Technology variation vs. R&D uncertainty: What matters most for energy patent success?," Resource and Energy Economics, Elsevier, vol. 35(4), pages 505-533.
    2. Checchi, Daniele & De Fraja, Gianni & Verzillo, Stefano, 2014. "Publish or Perish? Incentives and Careers in Italian Academia," IZA Discussion Papers 8345, Institute of Labor Economics (IZA).
    3. Pedro Albarrán & Raquel Carrasco & Javier Ruiz-Castillo, 2017. "Are Migrants More Productive Than Stayers? Some Evidence From A Set Of Highly Productive Academic Economists," Economic Inquiry, Western Economic Association International, vol. 55(3), pages 1308-1323, July.
    4. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2011. "Overall prestige of journals with ranking score above a given threshold," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 229-243, October.
    5. Damien BESANCENOT & Kim HUYNH & Francisco SERRANITO, 2015. "Co-Authorship and Individual Research Productivity in Economics: Assessing the Assortative Matching Hypothesis," LEO Working Papers / DR LEO 2236, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Diego Aboal & Maren Vairo, 2018. "The impact of subsidies for researchers on the gender scientific productivity gap," Science and Public Policy, Oxford University Press, vol. 45(4), pages 515-532.
    7. Daniele Checchi & Gianni De Fraja & Stefano Verzillo, 2014. "Publish or Perish: An Analysis of the Academic Job Market in Italy," Discussion Papers 14/04, University of Nottingham, School of Economics.
    8. Albarrán, Pedro & Carrasco, Raquel & Ruiz-Castillo, Javier, 2015. "The effect of spatial mobility and other factors on academic productivity : some evidence from a set of highly productive economists," UC3M Working papers. Economics we1415, Universidad Carlos III de Madrid. Departamento de Economía.
    9. Hottenrott, Hanna & Lawson, Cornelia, 2013. "Fishing for Complementarities: Competitive Research Funding and Research Productivity," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201318, University of Turin.
    10. Rotolo, Daniele & Messeni Petruzzelli, Antonio, 2013. "When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties," MPRA Paper 53406, University Library of Munich, Germany.
    11. Hottenrott, Hanna & Thorwarth, Susanne, 2010. "Industry funding of university research and scientific productivity," ZEW Discussion Papers 10-105, ZEW - Leibniz Centre for European Economic Research.
    12. Alberto Baccini & Lucio Barabesi & Martina Cioni & Caterina Pisani, 2013. "Crossing the hurdle: the determinants of individual scientific performance," Department of Economics University of Siena 691, Department of Economics, University of Siena.
    13. Raquel Carrasco & Javier Ruiz-Castillo, 2014. "The Evolution Of The Scientific Productivity Of Highly Productive Economists," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 1-16, January.
    14. A. Baccini & L. Barabesi & M. Cioni & C. Pisani, 2014. "Crossing the hurdle: the determinants of individual scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 2035-2062, December.
    15. Hottenrott, Hanna & Lawson, Cornelia, 2017. "Fishing for complementarities: Research grants and research productivity," International Journal of Industrial Organization, Elsevier, vol. 51(C), pages 1-38.
    16. Matthew J Michalska-Smith & Stefano Allesina, 2017. "And, not or: Quality, quantity in scientific publishing," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-12, June.
    17. Damien Besancenot & Kim Huynh & Francisco Serranito, 2015. " Thou shalt not work alone ," CEPN Working Papers hal-01175758, HAL.
    18. Rossello, Giulia & Cowan, Robin & Mairesse, Jacques, 2020. "Ph.D. research output in STEM: the role of gender and race in supervision," MERIT Working Papers 2020-021, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    19. Cristiano Antonelli & Chiara Franzoni & Aldo Geuna, 2011. "The Contributions of Economics to a Science of Science Policy," Chapters, in: Massimo G. Colombo & Luca Grilli & Lucia Piscitello & Cristina Rossi-Lamastra (ed.), Science and Innovation Policy for the New Knowledge Economy, chapter 1, Edward Elgar Publishing.

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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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