IDEAS home Printed from https://ideas.repec.org/p/grt/wpegrt/2017-04.html
   My bibliography  Save this paper

The Impact of Project-Based Funding in Science: \r\nLessons from the ANR Experience

Author

Listed:
  • Nicolas CARAYOL
  • Marianne LANOË

Abstract

Competitive allocation of funds to research proposals is a mechanism widely used by government agencies to sustain the projects of researchers in universities and other research institutions. However, little is known about how efficient this mechanism is in practice, how it affects the recipients’ behaviors and how it would be possible to improve the precise design of such funding allocation mechanisms. This article provides new answers to those questions, relying on empirical evidence stemming from the creation of a French generalist and nationwide research funding agency in 2005. The impact of receiving a grant on the research outputs as well as on the collaborations of the grantees is precisely quantified. Moreover, the impact on citations turns out to be more than double when funds are distributed in the more competitive non-thematic programs and to be significantly larger when allocated to younger recipients.

Suggested Citation

  • Nicolas CARAYOL & Marianne LANOË, 2017. "The Impact of Project-Based Funding in Science: \r\nLessons from the ANR Experience," Cahiers du GREThA (2007-2019) 2017-04, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    • Handle: RePEc:grt:wpegrt:2017-04
    as

    Download full text from publisher

    File URL: http://cahiersdugretha.u-bordeaux.fr/2017/2017-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    2. Pierre Azoulay & Joshua S. Graff Zivin & Gustavo Manso, 2011. "Incentives and creativity: evidence from the academic life sciences," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 527-554, September.
    3. 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.
    4. Markus Frlich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
    5. Ashish Arora & Alfonso Gambardella, 2010. "The Impact of NSF Support for Basic Research in Economics," NBER Chapters, in: Contributions in Memory of Zvi Griliches, pages 91-115, National Bureau of Economic Research, Inc.
    6. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    7. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    8. Jason Gush & Adam Jaffe & Victoria Larsen & Athene Laws, 2018. "The effect of public funding on research output: the New Zealand Marsden Fund," New Zealand Economic Papers, Taylor & Francis Journals, vol. 52(2), pages 227-248, May.
    9. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    10. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    11. Jacob, Brian A. & Lefgren, Lars, 2011. "The impact of research grant funding on scientific productivity," Journal of Public Economics, Elsevier, vol. 95(9), pages 1168-1177.
    12. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    13. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    14. repec:adr:anecst:y:2005:i:79-80:p:04 is not listed on IDEAS
    15. McDowell, John M, 1982. "Obsolescence of Knowledge and Career Publication Profiles: Some Evidence of Differences among Fields in Costs of Interrupted Careers," American Economic Review, American Economic Association, vol. 72(4), pages 752-768, September.
    16. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    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. Ayoubi, Charles & Pezzoni, Michele & Visentin, Fabiana, 2019. "The important thing is not to win, it is to take part: What if scientists benefit from participating in research grant competitions?," Research Policy, Elsevier, vol. 48(1), pages 84-97.
    2. Lawson, Cornelia & Geuna, Aldo & Finardi, Ugo, 2021. "The funding-productivity-gender nexus in science, a multistage analysis," Research Policy, Elsevier, vol. 50(3).
    3. Charles Ayoubi & Michele Pezzoni & Fabiana Visentin, 2017. "The Important Thing is not to Win, it is to Take Part: What If Scientists Benefit from Participating in Competitive Grant Races?," GREDEG Working Papers 2017-27, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    4. Lawson, Cornelia & Geuna, Aldo & Finardi, Ugo, 2019. "Nurturing knowledge? The impact of funding and family on scientific performance," 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 201902, University of Turin.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    2. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    3. Christian Volpe Martincus & Jerónimo Carballo & Pablo M. Garcia, 2012. "Public programmes to promote firms’ exports in developing countries: are there heterogeneous effects by size categories?," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 471-491, February.
    4. Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
    5. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    6. Chabé-Ferret, Sylvain, 2015. "Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 110-123.
    7. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    8. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    9. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    10. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).
    11. Rodrigo Dorantes-Gilardi & Aurora A. Ramírez-Álvarez & Diana Terrazas-Santamaría, 2023. "Is there a differentiated gender effect of collaboration with super-cited authors? Evidence from junior researchers in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2317-2336, April.
    12. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    13. Sophie Guthmuller & Florence Jusot & Jérôme Wittwer & Caroline Despres, 2010. "Le recours à l’Aide complémentaire santé : les enseignements d’une expérimentation sociale à Lille," Working Papers DT36, IRDES institut for research and information in health economics, revised Dec 2010.
    14. Nakashima, Kiyotaka, 2016. "An econometric evaluation of bank recapitalization programs with bank- and loan-level data," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 1-24.
    15. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
    16. Christian Volpe Martincus, 2010. "Odyssey in International Markets: An Assessment of the Effectiveness of Export Promotion in Latin America and the Caribbean," IDB Publications (Books), Inter-American Development Bank, number 16458, February.
    17. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    18. Galina Besstremyannaya, 2014. "Heterogeneous effect of coinsurance rate on healthcare costs: generalized finite mixtures and matching estimators," Discussion Papers 14-014, Stanford Institute for Economic Policy Research.
    19. Erlend E. Bø & Elin Halvorsen & Thor O. Thoresen, 2019. "Heterogeneity of the Carnegie Effect," Journal of Human Resources, University of Wisconsin Press, vol. 54(3), pages 726-759.
    20. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.

    More about this item

    Keywords

    project-based funding; competitive grants; early-career scientists; scientific productivity; conditional difference-in-differences;
    All these keywords.

    JEL classification:

    • H5 - Public Economics - - National Government Expenditures and Related Policies
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:grt:wpegrt:2017-04. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ernest Miguelez (email available below). General contact details of provider: https://edirc.repec.org/data/ifredfr.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.