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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.

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Bibliographic Info

Paper provided by Hogeschool-Universiteit Brussel, Faculteit Economie en Management in its series Working Papers with number 2009/01.

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Length: 36 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:hub:wpecon:200901

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Web page: http://research.hubrussel.be
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Keywords: economics of science; research productivity; quantile regression; count data; random effects;

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References

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  1. 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, 09.
  2. Kelchtermans, Stijn & Veugelers, Reinhilde, 2005. "Top Research Productivity and its Persistence," CEPR Discussion Papers 5415, C.E.P.R. Discussion Papers.
  3. 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.
  4. 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.
  5. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-58, March.
  6. 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.
  7. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
  8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  9. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
  10. Omar Arias & Kevin F. Hallock & Walter Sosa Escudero, 1999. "Individual Heterogeneity in the Returns to Schooling: Instrumental Variables Quantile Regression using Twins Data," Department of Economics, Working Papers 016, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
  11. 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 114-32, March.
  12. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
  13. Paula E. Stephan, 1996. "The Economics of Science," Journal of Economic Literature, American Economic Association, vol. 34(3), pages 1199-1235, September.
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Cited by:
  1. 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.
  2. 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.
  3. Pedro Albarrán & Raquel Carrasco & Javier Ruiz-Castillo, 2014. "The effect of spatial mobility and other factors on academic productivity : some evidence from a set of highly productive economists," Economics Working Papers we1415, Universidad Carlos III, Departamento de Economía.
  4. 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.
  5. 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.
  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. 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.

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