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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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    1. Levin, Sharon G & Stephan, Paula E, 1991. "Research Productivity over the Life Cycle: Evidence for Academic Scientists," American Economic Review, American Economic Association, vol. 81(1), pages 114-132, March.
    2. Martin Beck & Bernd Fitzenberger, 2004. "Changes in Union Membership Over Time: A Panel Analysis for West Germany," LABOUR, CEIS, vol. 18(3), pages 329-362, September.
    3. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
    4. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    5. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    6. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    7. Stephan, Paula E., 2010. "The Economics of Science," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 217-273, Elsevier.
    8. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    9. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    12. Veugelers, Reinhilde & Kelchtermans, Stijn, 2005. "Top Research Productivity and its Persistence," CEPR Discussion Papers 5415, C.E.P.R. Discussion Papers.
    13. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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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. Daniele Checchi & Gianni De Fraja & Stefano Verzillo, 2014. "Publish or Perish? Incentives and Careers in Italian Academia," CEIS Research Paper 323, Tor Vergata University, CEIS, revised 07 Aug 2014.
    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," Working Papers halshs-01252373, HAL.
    6. 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.
    7. Daniele Rotolo & Michael Hopkins & Nicola Grassano, 2023. "Do funding sources complement or substitute? Examining the impact of cancer research publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 50-66, January.
    8. Ryazanova, Olga & Jaskiene, Jolanta, 2022. "Managing individual research productivity in academic organizations: A review of the evidence and a path forward," Research Policy, Elsevier, vol. 51(2).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Damien Besancenot & Kim Van Huynh & Francisco Serranito, 2015. " Thou shalt not work alone ," CEPN Working Papers hal-01175758, HAL.
    22. Alexander Serenko & Mauricio Marrone & John Dumay, 2022. "Scientometric portraits of recognized scientists: a structured literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4827-4846, August.

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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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