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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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Bibliographic Info

Article provided by Oxford University Press in its journal Industrial and Corporate Change.

Volume (Year): 20 (2011)
Issue (Month): 1 (February)
Pages: 295-336

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Handle: RePEc:oup:indcch:v:20:y:2011:i:1:p:295-336

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  1. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 91(1), pages 74-89, October.
  2. Paula E. Stephan, 1996. "The Economics of Science," Journal of Economic Literature, American Economic Association, American Economic Association, vol. 34(3), pages 1199-1235, September.
  3. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, Springer, vol. 26(1), pages 199-220.
  4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, Econometric Society, vol. 46(1), pages 33-50, January.
  5. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, 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, University of Wisconsin Press, vol. 33(1), pages 88-126.
  7. Martin Beck & Bernd Fitzenberger, 2004. "Changes in Union Membership Over Time: A Panel Analysis for West Germany," LABOUR, CEIS, CEIS, vol. 18(3), pages 329-362, 09.
  8. 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, Springer, vol. 26(1), pages 7-40.
  9. Kelchtermans, Stijn & Veugelers, Reinhilde, 2005. "Top Research Productivity and its Persistence," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5415, C.E.P.R. Discussion Papers.
  10. J.A.F. Machado & J. M. C. Santos Silva, 2003. "Quantiles for Counts," Econometrics, EconWPA 0303001, EconWPA.
  11. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521845731.
  12. Levin, Sharon G & Stephan, Paula E, 1991. "Research Productivity over the Life Cycle: Evidence for Academic Scientists," American Economic Review, American Economic Association, American Economic Association, vol. 81(1), pages 114-32, March.
  13. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 18(1), pages 5-46, January.
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Cited by:
  1. Hanna Hottenrott & Cornelia Lawson, 2013. "Fishing for Complementarities: Competitive Research Funding and Research Productivity," Carlo Alberto Notebooks, Collegio Carlo Alberto 334, Collegio Carlo Alberto.
  2. 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, Elsevier, vol. 35(4), pages 505-533.
  3. 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).
  4. 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.
  5. Daniele Checchi & Gianni De Fraja & Stefano Verzillo, 2014. "Publish or Perish: An Analysis of the Academic Job Market in Italy," Discussion Papers, University of Nottingham, School of Economics 14/04, University of Nottingham, School of Economics.
  6. Alberto Baccini & Lucio Barabesi & Martina Cioni & Caterina Pisani, 2013. "Crossing the hurdle: the determinants of individual scientific performance," Department of Economics University of Siena, Department of Economics, University of Siena 691, Department of Economics, University of Siena.
  7. Hottenrott, Hanna & Thorwarth, Susanne, 2011. "Industry funding of university research and scientific productivity," ZEW Discussion Papers, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research 10-105 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  8. 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, Universidad Carlos III, Departamento de Economía we1415, Universidad Carlos III, Departamento de Economía.

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