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Publication Bias in the Returns to R&D Literature

Author

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  • Jarle Møen

    () (Norwegian School of Economics)

  • Helge Sandvig Thorsen

    () (Norwegian School of Economics)

Abstract

Abstract The returns to R&D literature is large and has been surveyed on several occasions. We complement previous surveys by discussing the scope for publication bias and illustrate how formal meta analytic techniques can be used to analyse the problem. We find evidence consistent with positive publication bias and discuss possible interpretations. The bias appears to be particularly strong in the part of the literature that controls for unobserved firm fixed effects. The reason may be that fixed effects specifications are particularly susceptible to measurement errors and therefore have a high probability of producing implausibly low return estimates. Implausible estimates are likely to be filtered out before being reported, and our analysis suggests that 23 % of a hypothetical complete literature is missing. Future reviews should take into account that the full effect of negative specifications biases may be masked by reporting and publication bias.

Suggested Citation

  • Jarle Møen & Helge Sandvig Thorsen, 2017. "Publication Bias in the Returns to R&D Literature," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(3), pages 987-1013, September.
  • Handle: RePEc:spr:jknowl:v:8:y:2017:i:3:d:10.1007_s13132-015-0309-9
    DOI: 10.1007/s13132-015-0309-9
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    as
    1. T.D. Stanley & Hristos Doucouliagos, 2010. "Picture This: A Simple Graph That Reveals Much Ado About Research," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 170-191, February.
    2. Markus Eberhardt & Christian Helmers & Hubert Strauss, 2013. "Do Spillovers Matter When Estimating Private Returns to R&D?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 436-448, May.
    3. Carla Haelermans & Lex Borghans, 2012. "Wage Effects of On-the-Job Training: A Meta-Analysis," British Journal of Industrial Relations, London School of Economics, vol. 50(3), pages 502-528, September.
    4. Doraszelski, Ulrich & Jaumandreu, Jordi, 2006. "R&D and productivity: Estimating production functions when productivity is endogenous," MPRA Paper 1246, University Library of Munich, Germany.
    5. Henri Capron & Michele Cincera, 1998. "Exploring the Spillover Impact on Productivity of World-Wide Manufacturing Firms," Annals of Economics and Statistics, GENES, issue 49-50, pages 565-587.
    6. Bronwyn H. Hall, 1993. "Industrial Research during the 1980s: Did the Rate of Return Fall?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 24(2 Microec), pages 289-343.
    7. Castellacci, Fulvio & Lie, Christine Mee, 2015. "Do the effects of R&D tax credits vary across industries? A meta-regression analysis," Research Policy, Elsevier, vol. 44(4), pages 819-832.
    8. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    9. Stanley, T. D. & Doucouliagos, Chris, 2007. "Identifying and correcting publication selection bias in the efficiency-wage literature: Heckman Meta-Regression," Working Papers eco_2007_11, Deakin University, Department of Economics.
    10. Mark Rogers, 2010. "R&D and productivity: using UK firm-level data to inform policy," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 329-359, July.
    11. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    12. Jacques Mairesse & Mohamed Sassenou, 1991. "R&D Productivity: A Survey of Econometric Studies at the Firm Level," NBER Working Papers 3666, National Bureau of Economic Research, Inc.
    13. Jacques Mairesse & Bronwyn H. Hall, 1996. "Estimating the Productivity of Research and Development: An Exploration of GMM Methods Using Data on French & United States Manufacturing Firms," NBER Working Papers 5501, National Bureau of Economic Research, Inc.
    14. Zvi Griliches, 1998. "Comparing Productivity Growth: An Exploration of French and U.S. Industrial and Firm Data," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 157-186 National Bureau of Economic Research, Inc.
    15. Thomas J. Steichen, 2001. "Nonparametric trim and fill analysis of publication bias in meta-analysis," Stata Technical Bulletin, StataCorp LP, vol. 10(57).
    16. Neumark, David & Wascher, William, 1998. "Is the Time-Series Evidence on Minimum Wage Effects Contaminated by Publication Bias?," Economic Inquiry, Western Economic Association International, vol. 36(3), pages 458-470, July.
    17. Ugur, Mehmet & Trushin, Eshref & Solomon, Edna & Guidi, Francesco, 2016. "R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis," Research Policy, Elsevier, vol. 45(10), pages 2069-2086.
    18. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    19. Sue Duval & Richard Tweedie, 2000. "Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis," Biometrics, The International Biometric Society, vol. 56(2), pages 455-463, June.
    20. Wakelin, Katharine, 2001. "Productivity growth and R&D expenditure in UK manufacturing firms," Research Policy, Elsevier, vol. 30(7), pages 1079-1090, August.
    21. M. Ishaq Nadiri, 1993. "Innovations and Technological Spillovers," NBER Working Papers 4423, National Bureau of Economic Research, Inc.
    22. Jora R. Minasian, 1962. "The Economics of Research and Development," NBER Chapters,in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 93-142 National Bureau of Economic Research, Inc.
    23. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    24. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 17-45 National Bureau of Economic Research, Inc.
    25. De Long, J Bradford & Lang, Kevin, 1992. "Are All Economic Hypotheses False?," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1257-1272, December.
    26. Stanley, T. D. & Doucouliagos, Hristos, 2013. "Better than random: weighted least squares meta-regression analysis," Working Papers eco_2013_2, Deakin University, Department of Economics.
    27. Link, Albert N., 1983. "Inter-firm technology flows and productivity growth," Economics Letters, Elsevier, vol. 11(1-2), pages 179-184.
    28. Raquel Ortega-Argiles & Mariacristina Piva & Lesley Potters & Marco Vivarelli, 2009. "Is corporate R&D investment in high-tech sectors more effective? Some guidelines for European research policy," Jena Economic Research Papers 2009-038, Friedrich-Schiller-University Jena.
    29. Hristos Doucouliagos & T. D. Stanley, 2009. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," British Journal of Industrial Relations, London School of Economics, vol. 47(2), pages 406-428, June.
    30. Lichtenberg, Frank R & Siegel, Donald, 1991. "The Impact of R&D Investment on Productivity--New Evidence Using Linked R&D-LRD Data," Economic Inquiry, Western Economic Association International, vol. 29(2), pages 203-229, April.
    31. Scherer, F. M. & Harhoff, Dietmar, 2000. "Technology policy for a world of skew-distributed outcomes," Research Policy, Elsevier, vol. 29(4-5), pages 559-566, April.
    32. Ashenfelter, Orley & Harmon, Colm & Oosterbeek, Hessel, 1999. "A review of estimates of the schooling/earnings relationship, with tests for publication bias," Labour Economics, Elsevier, vol. 6(4), pages 453-470, November.
    33. T. D. Stanley, 2008. "Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 103-127, February.
    34. Zvi Griliches, 1998. "The Search for R&D Spillovers," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 251-268 National Bureau of Economic Research, Inc.
    35. Holger Görg & Eric Strobl, 2016. "Multinational Companies And Productivity Spillovers: A Meta-Analysis," World Scientific Book Chapters,in: MULTINATIONAL ENTERPRISES AND HOST COUNTRY DEVELOPMENT Volume 53: World Scientific Studies in International Economics, chapter 8, pages 145-161 World Scientific Publishing Co. Pte. Ltd..
    36. Peter Warda & Urban Gråsjö & Charlie Karlsson, 2012. "Spatial Knowledge Spillovers in Europe: A Meta-Analysis," ERSA conference papers ersa12p622, European Regional Science Association.
    37. Jacques Mairesse & Pierre Mohnen, 1990. "Recherche-Développement et productivité : un survol de la littérature économétrique," Économie et Statistique, Programme National Persée, vol. 237(1), pages 99-108.
    38. Robert Wieser, 2005. "Research And Development Productivity And Spillovers: Empirical Evidence At The Firm Level," Journal of Economic Surveys, Wiley Blackwell, vol. 19(4), pages 587-621, September.
    39. T.D. Stanley & Hristos Doucouliagos & Margaret Giles & Jost H. Heckemeyer & Robert J. Johnston & Patrice Laroche & Jon P. Nelson & Martin Paldam & Jacques Poot & Geoff Pugh & Randall S. Rosenberger & , 2013. "Meta-Analysis Of Economics Research Reporting Guidelines," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 390-394, April.
    40. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    41. Maria Abreu Henri L. F. de Groot & Raymond J. G. M. Florax, 2005. "A Meta-Analysis of β-Convergence: the Legendary 2%," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 389-420, July.
    42. Card, David & Krueger, Alan B, 1995. "Time-Series Minimum-Wage Studies: A Meta-analysis," American Economic Review, American Economic Association, vol. 85(2), pages 238-243, May.
    43. Mansfield, Edwin, 1980. "Basic Research and Productivity Increase in Manufacturing," American Economic Review, American Economic Association, vol. 70(5), pages 863-873, December.
    44. repec:adr:anecst:y:1998:i:49-50:p:22 is not listed on IDEAS
    45. Orley Ashenfelter & Colm Harmon & Hessel Oosterbeek, 1999. "A Review of Estimates of the Schooling/Earnings Relationship, with Tests for Publication Bias," Working Papers 804, Princeton University, Department of Economics, Industrial Relations Section..
    46. Nadiri, M.I., 1993. "Innovations and Technological Spillovers," Working Papers 93-31, C.V. Starr Center for Applied Economics, New York University.
    47. Giuseppe Medda & Claudio Piga & Donald S. Siegel, 2003. "On the Relationship between R&D and Productivity: a Treatment Effect Analysis," Rensselaer Working Papers in Economics 0307, Rensselaer Polytechnic Institute, Department of Economics.
    48. Schankerman, Mark, 1981. "The Effects of Double-Counting and Expensing on the Measured Returns to R&D," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 454-458, August.
    49. Jiann-Chyuan Wang & Kuen-Hung Tsai, 2003. "Productivity Growth and R&D Expenditure in Taiwan's Manufacturing Firms," NBER Working Papers 9724, National Bureau of Economic Research, Inc.
    50. Nelson, Jon P., 2014. "Estimating the price elasticity of beer: Meta-analysis of data with heterogeneity, dependence, and publication bias," Journal of Health Economics, Elsevier, vol. 33(C), pages 180-187.
    51. Jacques Mairesse & Philippe Cunéo, 1985. "Recherche-développement et performances des entreprises : une étude économétrique sur données individuelles," Revue Économique, Programme National Persée, vol. 36(5), pages 1001-1042.
    52. repec:fth:prinin:425 is not listed on IDEAS
    53. Raquel Ortega-Argilés & Maria-Cristina Piva & Lesley Potters & Marco Vivarelli, 2009. "Is Corporate R&D Investment in High-tech Sectors more Efficient? Some Guidelines for European Research Policy," JRC Working Papers on Corporate R&D and Innovation 2009-9, Joint Research Centre (Seville site).
    54. Mario Kafouros, 2005. "R&D and productivity growth: Evidence from the UK," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(6), pages 479-497.
    55. repec:hoo:wpaper:e-93-10 is not listed on IDEAS
    56. repec:crs:wpaper:9833 is not listed on IDEAS
    57. Odagiri, Hiroyuki, 1983. "R & D Expenditures, Royalty Payments, and Sales Growth in Japanese Manufacturing Corporations," Journal of Industrial Economics, Wiley Blackwell, vol. 32(1), pages 61-71, September.
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    Cited by:

    1. Møen, Jarle, 2018. "Corporate returns to subsidized R&D projects: Direct grants vs tax credit financing," Discussion Papers 2018/9, Norwegian School of Economics, Department of Business and Management Science.

    More about this item

    Keywords

    Returns to R&D; Meta-analysis; Publication bias; Funnel asymmetry; Trim-and-fill method; FAT – PET – PEESE;

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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