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Exploring heterogeneous returns to collaborative R&D: A marginal treatment effects perspective

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  • Spanos, Yiannis E.

Abstract

I examine returns to collaborative R&D using the marginal treatment effects framework. This framework allows me to examine whether the impacts of participation in collaborative R&D on the benefits of product innovation are homogeneous, or if instead firms derive heterogeneous returns based on unobserved characteristics and expectations. Assuming that returns are indeed heterogeneous, I develop two alternative hypotheses representing different underlying mechanisms driving the connection between collaboration and expected returns: If firms evaluate the pros and cons of collaboration based on idiosyncratic traits and expectations, then it is logical to expect that those most likely to collaborate are also those most likely to derive significant benefits from collaboration. This represents the notion of positive selection. On the other hand, it might be possible that those firms least likely to collaborate are in fact those that would have benefited the most had they chosen to collaborate. This reflects the notion of negative selection. Using anonymized data from the 2006 Community Innovation Survey, I confirm that there exists significant heterogeneity in the returns to collaborative R&D due to both unobservable and observable firm characteristics; moreover, the findings clearly support the hypothesis of negative selection. It appears that collaborative R&D plays an equalizing role on the benefits of product innovation for resource-constrained firms.

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  • Spanos, Yiannis E., 2021. "Exploring heterogeneous returns to collaborative R&D: A marginal treatment effects perspective," Research Policy, Elsevier, vol. 50(5).
  • Handle: RePEc:eee:respol:v:50:y:2021:i:5:s0048733321000275
    DOI: 10.1016/j.respol.2021.104223
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    as
    1. d'Aspremont, Claude & Jacquemin, Alexis, 1988. "Cooperative and Noncooperative R&D in Duopoly with Spillovers," American Economic Review, American Economic Association, vol. 78(5), pages 1133-1137, December.
    2. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    3. Marco Caliendo & Reinhard Hujer, 2006. "The microeconometric estimation of treatment effects—An overview," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 199-215, March.
    4. Bruce Kogut & Udo Zander, 1992. "Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology," Organization Science, INFORMS, vol. 3(3), pages 383-397, August.
    5. Joel A. C. Baum & Tony Calabrese & Brian S. Silverman, 2000. "Don't go it alone: alliance network composition and startups' performance in Canadian biotechnology," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 267-294, March.
    6. David J. TEECE, 2008. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," World Scientific Book Chapters, in: The Transfer And Licensing Of Know-How And Intellectual Property Understanding the Multinational Enterprise in the Modern World, chapter 5, pages 67-87, World Scientific Publishing Co. Pte. Ltd..
    7. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    8. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    9. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    10. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    11. Pedro Carneiro & James J. Heckman, 2002. "The Evidence on Credit Constraints in Post--secondary Schooling," Economic Journal, Royal Economic Society, vol. 112(482), pages 705-734, October.
    12. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    13. Mairesse, Jacques & Mohnen, Pierre, 2010. "Using Innovation Surveys for Econometric Analysis," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1129-1155, Elsevier.
    14. Castellacci, Fulvio, 2008. "Technological paradigms, regimes and trajectories: Manufacturing and service industries in a new taxonomy of sectoral patterns of innovation," Research Policy, Elsevier, vol. 37(6-7), pages 978-994, July.
    15. Sebastian Kobarg & Jutta Stumpf-Wollersheim & Isabell M. Welpe, 2018. "University-industry collaborations and product innovation performance: the moderating effects of absorptive capacity and innovation competencies," The Journal of Technology Transfer, Springer, vol. 43(6), pages 1696-1724, December.
    16. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    17. Breschi, Stefano & Malerba, Franco & Orsenigo, Luigi, 2000. "Technological Regimes and Schumpeterian Patterns of Innovation," Economic Journal, Royal Economic Society, vol. 110(463), pages 388-410, April.
    18. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
    19. Andræs Barge-Gil, 2013. "Open Strategies and Innovation Performance," Industry and Innovation, Taylor & Francis Journals, vol. 20(7), pages 585-610, October.
    20. Bruno Cassiman & Reinhilde Veugelers, 2002. "R&D Cooperation and Spillovers: Some Empirical Evidence from Belgium," American Economic Review, American Economic Association, vol. 92(4), pages 1169-1184, September.
    21. Rachelle C. Sampson, 2005. "Experience effects and collaborative returns in R&D alliances," Strategic Management Journal, Wiley Blackwell, vol. 26(11), pages 1009-1031, November.
    22. John Hagedoorn, 1993. "Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences," Strategic Management Journal, Wiley Blackwell, vol. 14(5), pages 371-385, July.
    23. Peter J. Lane & Michael Lubatkin, 1998. "Relative absorptive capacity and interorganizational learning," Post-Print hal-02311860, HAL.
    24. Belderbos, Rene & Carree, Martin & Diederen, Bert & Lokshin, Boris & Veugelers, Reinhilde, 2004. "Heterogeneity in R&D cooperation strategies," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1237-1263, November.
    25. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    26. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre.
    27. Sascha O. Becker, 2016. "Using instrumental variables to establish causality," IZA World of Labor, Institute of Labor Economics (IZA), pages 250-250, April.
    28. Damian Clarke & Benjamín Matta, 2018. "Practical considerations for questionable IVs," Stata Journal, StataCorp LP, vol. 18(3), pages 663-691, September.
    29. Geroski, P A, 1993. "Antitrust Policy towards Co-operative R&D Ventures," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 9(2), pages 58-71, Summer.
    30. Martin Eckhoff Andresen, 2018. "Exploring marginal treatment effects: Flexible estimation using Stata," Stata Journal, StataCorp LP, vol. 18(1), pages 118-158, March.
    31. Leiponen, Aija, 2005. "Skills and innovation," International Journal of Industrial Organization, Elsevier, vol. 23(5-6), pages 303-323, June.
    32. repec:bla:pacecr:v:9:y:2004:i:3:p:155-171 is not listed on IDEAS
    33. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
    34. Michael Lokshin & Zurab Sajaia, 2004. "Maximum likelihood estimation of endogenous switching regression models," Stata Journal, StataCorp LP, vol. 4(3), pages 282-289, September.
    35. Xiang Zhou & Yu Xie, 2019. "Marginal Treatment Effects from a Propensity Score Perspective," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 3070-3084.
    36. Hanna Hottenrott & Cindy Lopes-Bento, 2015. "Quantity or quality? Knowledge alliances and their effects on patenting," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 24(5), pages 981-1011.
    37. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    38. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70, Elsevier.
    39. Leiponen, Aija & Byma, Justin, 2009. "If you cannot block, you better run: Small firms, cooperative innovation, and appropriation strategies," Research Policy, Elsevier, vol. 38(9), pages 1478-1488, November.
    40. Scott Brave & Thomas Walstrum, 2014. "Estimating marginal treatment effects using parametric and semiparametric methods," Stata Journal, StataCorp LP, vol. 14(1), pages 191-217, March.
    41. Richard R. Nelson, 1959. "The Simple Economics of Basic Scientific Research," Journal of Political Economy, University of Chicago Press, vol. 67(3), pages 297-297.
    42. Giovanni Cerulli, 2017. "Identification and estimation of treatment effects in the presence of (correlated) neighborhood interactions: Model and Stata implementation via ntreatreg," Stata Journal, StataCorp LP, vol. 17(4), pages 803-833, December.
    43. Miotti, Luis & Sachwald, Frederique, 2003. "Co-operative R&D: why and with whom?: An integrated framework of analysis," Research Policy, Elsevier, vol. 32(8), pages 1481-1499, September.
    44. Oguguo, Prince C. & Bodas Freitas, Isabel Maria & Genet, Corine, 2020. "Multilevel institutional analyses of firm benefits from R&D collaboration," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    45. Seung Ho Park & Gerardo R. Ungson, 2001. "Interfirm Rivalry and Managerial Complexity: A Conceptual Framework of Alliance Failure," Organization Science, INFORMS, vol. 12(1), pages 37-53, February.
    46. Erika Raquel Badillo & Rosina Moreno, 2018. "Does absorptive capacity determine collaboration returns to innovation? A geographical dimension," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(3), pages 473-499, May.
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    More about this item

    Keywords

    Collaborative R&D; (benefits of) product innovation; Marginal treatment effects; Community Innovation Survey;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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