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The great divide in scientific productivity: why the average scientist does not exist

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  • Stijn Kelchtermans
  • Reinhilde Veugelers

Abstract

Using a panel of individual researchers at the KU Leuven, Belgium, we analyze the impact of a range of productivity drivers on research performance at the separate quantiles of the productivity distribution. We estimate a correlated random-effects quantile regression model, accounting for unobserved heterogeneity of researchers and applicable to count data. We find that the effect of most regressors, particularly system-factors incentivizing researchers (like promotion record and access to research resources), as well as the gender of the researcher differ significantly at different points in the distribution, yielding strong support for our quantile regression approach. Comparing publications versus citations as dimensions of research performance, we find the incentive factors to work stronger in affecting research quality. Finally, the split-sample regression results emphasize the heterogeneity across scientific disciplines. Copyright 2011 The Author 2011. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.

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  • Stijn Kelchtermans & Reinhilde Veugelers, 2011. "The great divide in scientific productivity: why the average scientist does not exist," Industrial and Corporate Change, Oxford University Press, vol. 20(1), pages 295-336, February.
  • Handle: RePEc:oup:indcch:v:20:y:2011:i:1:p:295-336
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    File URL: http://hdl.handle.net/10.1093/icc/dtq074
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. Hottenrott, Hanna & Lawson, Cornelia, 2013. "Fishing for complementarities: Competitive research funding and research productivity," DICE Discussion Papers 129, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    8. Checchi, Daniele & De Fraja, Gianni & Verzillo, Stefano, 2014. "Publish or Perish? Incentives and Careers in Italian Academia," CEPR Discussion Papers 10084, C.E.P.R. Discussion Papers.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Damien Besancenot & Kim Huynh & Francisco Serranito, 2015. "Co-Authorship And Individual Research Productivity In Economics: Assessing The Assortative Matching Hypothesis," CEPN Working Papers halshs-01252373, HAL.
    14. Damien Besancenot & Kim Huynh & Francisco Serranito, 2015. " Thou shalt not work alone ," CEPN Working Papers hal-01175758, HAL.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.

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    More about this item

    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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