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Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models

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  • Rouven E. Haschka
  • Helmut Herwartz
Abstract
This study provides an assessment of the R&D–patent relation of European pharmaceutical firms that are not flawed by endogeneity biases. Firms invest in R&D and generate latent knowledge which then manifests in observable patent outcomes through a Poisson model. The process of turning R&D into knowledge is described by a production process subject to inefficiency and endogeneity. To estimate a Poisson stochastic frontier model, the suggested novel copula‐based approach directly accounts for the dependence between the endogenous regressors and the inefficiency component. Hence, its implementation does not require any instrumental variables. Simulation results underline that the proposed estimator outperforms conventional instrumental variable estimators. Neglecting endogeneity leads to a substantial underestimation of the R&D elasticity of patents generated in the European pharmaceutical industry.

Suggested Citation

  • Rouven E. Haschka & Helmut Herwartz, 2022. "Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 942-960, November.
    • Handle: RePEc:bla:jemstr:v:31:y:2022:i:4:p:942-960
    DOI: 10.1111/jems.12491
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    References listed on IDEAS

    as
    1. Simar, Léopold & Vanhems, Anne & Van Keilegom, Ingrid, 2016. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," Journal of Econometrics, Elsevier, vol. 190(2), pages 360-373.
    2. Rouven Edgar Haschka & Katharina Schley & Helmut Herwartz, 2020. "Provision of health care services and regional diversity in Germany: insights from a Bayesian health frontier analysis with spatial dependencies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(1), pages 55-71, February.
    3. repec:adr:anecst:y:2005:i:79-80:p:07 is not listed on IDEAS
    4. Dirk Czarnitzki & Andrew A. Toole, 2011. "Patent Protection, Market Uncertainty, and R&D Investment," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 147-159, February.
    5. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    6. Valentina Meliciani, 2000. "The relationship between R&D, investment and patents: a panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 32(11), pages 1429-1437.
    7. Bronwyn H. Hall & Adam Jaffe & Manuel Trajtenberg, 2005. "Market Value and Patent Citations," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 16-38, Spring.
    8. Hashimoto, Akihiro & Haneda, Shoko, 2008. "Measuring the change in R&D efficiency of the Japanese pharmaceutical industry," Research Policy, Elsevier, vol. 37(10), pages 1829-1836, December.
    9. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    10. Pieri, Fabio & Vecchi, Michela & Venturini, Francesco, 2018. "Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach," Research Policy, Elsevier, vol. 47(9), pages 1842-1852.
    11. Ilan Oshri & Julia Kotlarsky & Leslie P. Willcocks, 2015. "The Handbook of Global Outsourcing and Offshoring," Palgrave Macmillan Books, Palgrave Macmillan, edition 3, number 978-1-137-43744-0, October.
    12. Zvi Griliches, 1998. "R&D and Productivity: The Econometric Evidence," NBER Books, National Bureau of Economic Research, Inc, number gril98-1.
    13. Szücs, Florian, 2018. "Research subsidies, industry–university cooperation and innovation," Research Policy, Elsevier, vol. 47(7), pages 1256-1266.
    14. Jeffrey Furman & Margaret K. Kyle & Alain Cockburn & Rebecca M. Henderson, 2005. "Public & Private Spillovers: Location and the Productivity of Pharmaceutical Research," Annals of Economics and Statistics, GENES, issue 79-80, pages 165-188.
    15. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
    16. Siebert, Ralph Bernd, 2017. "A structural model on the impact of prediscovery licensing and research joint ventures on innovation and product market efficiency," International Journal of Industrial Organization, Elsevier, vol. 54(C), pages 89-124.
    17. Michelle Alexopoulos, 2011. "Read All about It!! What Happens Following a Technology Shock?," American Economic Review, American Economic Association, vol. 101(4), pages 1144-1179, June.
    18. Edwin Mansfield, 1986. "Patents and Innovation: An Empirical Study," Management Science, INFORMS, vol. 32(2), pages 173-181, February.
    19. Windmeijer, F A G & Silva, J M C Santos, 1997. "Endogeneity in Count Data Models: An Application to Demand for Health Care," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 281-294, May-June.
    20. Nelson, Andrew J., 2009. "Measuring knowledge spillovers: What patents, licenses and publications reveal about innovation diffusion," Research Policy, Elsevier, vol. 38(6), pages 994-1005, July.
    21. Wang, Ning & Hagedoorn, John, 2014. "The lag structure of the relationship between patenting and internal R&D revisited," Research Policy, Elsevier, vol. 43(8), pages 1275-1285.
    22. Cincera, Michele, 1997. "Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 265-280, May-June.
    23. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    24. Camilla Mastromarco & Léopold Simar, 2021. "Latent heterogeneity to evaluate the effect of human capital on world technology frontier," Journal of Productivity Analysis, Springer, vol. 55(2), pages 71-89, April.
    25. Melissa A. Schilling, 2015. "Technology Shocks, Technological Collaboration, and Innovation Outcomes," Organization Science, INFORMS, vol. 26(3), pages 668-686, June.
    26. Jaffe, Adam B., 1989. "Characterizing the "technological position" of firms, with application to quantifying technological opportunity and research spillovers," Research Policy, Elsevier, vol. 18(2), pages 87-97, April.
    27. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    28. Kutlu, Levent, 2010. "Battese-coelli estimator with endogenous regressors," Economics Letters, Elsevier, vol. 109(2), pages 79-81, November.
    29. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    30. Siegel, Donald S & Westhead, Paul & Wright, Mike, 2003. "Science Parks and the Performance of New Technology-Based Firms: A Review of Recent U.K. Evidence and an Agenda for Future Research," Small Business Economics, Springer, vol. 20(2), pages 177-184, March.
    31. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    32. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, December.
    33. Sungho Park & Sachin Gupta, 2012. "Handling Endogenous Regressors by Joint Estimation Using Copulas," Marketing Science, INFORMS, vol. 31(4), pages 567-586, July.
    34. Haschka, Rouven E. & Herwartz, Helmut, 2020. "Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach," Research Policy, Elsevier, vol. 49(8).
    35. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    36. Tran, Kien C. & Tsionas, Efthymios G., 2015. "Endogeneity in stochastic frontier models: Copula approach without external instruments," Economics Letters, Elsevier, vol. 133(C), pages 85-88.
    37. Griliches, Zvi, 1998. "R&D and Productivity," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226308869, September.
    38. Jan-Michael Becker & Dorian Proksch & Christian M. Ringle, 2022. "Revisiting Gaussian copulas to handle endogenous regressors," Journal of the Academy of Marketing Science, Springer, vol. 50(1), pages 46-66, January.
    39. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, September.
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