IDEAS home Printed from https://ideas.repec.org/p/hub/wpecon/200901.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://lirias.hubrussel.be/bitstream/123456789/2165/1/09HRP01.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, Fall.
    8. Stephan, Paula E., 2010. "The Economics of Science," Handbook of the Economics of Innovation, Elsevier.
    9. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    10. 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.
    11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    12. 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.
    13. Kelchtermans, Stijn & Veugelers, Reinhilde, 2005. "Top Research Productivity and its Persistence," CEPR Discussion Papers 5415, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 for the Study of Labor (IZA).
    3. repec:bla:ecinqu:v:55:y:2017:i:3:p:1308-1323 is not listed on IDEAS
    4. 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.
    5. repec:spr:scient:v:89:y:2011:i:1:d:10.1007_s11192-011-0442-6 is not listed on IDEAS
    6. 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.
    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. 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.
    9. Hottenrott, Hanna & Thorwarth, Susanne, 2010. "Industry funding of university research and scientific productivity," ZEW Discussion Papers 10-105, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    10. repec:spr:scient:v:101:y:2014:i:3:d:10.1007_s11192-014-1395-3 is not listed on IDEAS
    11. 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.
    12. 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.
    13. Cristiano Antonelli & Chiara Franzoni & Aldo Geuna, 2011. "The Contributions of Economics to a Science of Science Policy," Chapters,in: Science and Innovation Policy for the New Knowledge Economy, chapter 1 Edward Elgar Publishing.
    14. Ruiz-Castillo, Javier & Carrasco, Raquel & Albarrán, Pedro, 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.
    15. 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.
    16. 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.
    17. 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.
    18. Damien Besancenot & Kim Huynh & Francisco Serranito, 2015. " Thou shalt not work alone ," Working Papers hal-01175758, HAL.
    19. Damien Besancenot & Kim Huynh & Francisco Serranito, 2015. " Thou shalt not work alone ," CEPN Working Papers hal-01175758, HAL.

    More about this item

    Keywords

    economics of science; research productivity; quantile regression; count data; random effects;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hub:wpecon:200901. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sabine Janssens). General contact details of provider: http://edirc.repec.org/data/emhubbe.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.