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Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms

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  • Shiferaw Gurmu

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  • Fidel Pérez-Sebastián

Abstract

Hausman, Hall and Griliches (1984) and Hall, Griliches and Hausman (1986) investigated whether there was a lag in the patent-R&D relationship for the U.S. manufacturing sector using 1970¿s data. They found that there was little evidence of anything but contemporaneous movement of patents and R&D. We reexamine this important issue employing new longitudinal patent data at the firm level for the U.S. manufacturing sector from 1982 to 1992. To address unique features of the data, we estimate various distributed lag and dynamic multiplicative panel count data models. The paper also develops a new class of count panel data models based on series expansion of the distribution of individual effects. The empirical analyses show that, although results are somewhat sensitive to different estimation methods, the contemporaneous relationship between patenting and R&D expenditures continues to be rather strong, accounting for over 60% of the total R&D elasticity. Regarding the lag structure of the patents-R&D relationship, we do find a significant lag in all empirical specifications. Moreover, the estimated lag effects are higher than have previously been found, suggesting that the contribution of R&D history to current patenting has increased from the 1970¿s to the 1980¿s.
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Suggested Citation

  • Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
  • Handle: RePEc:spr:empeco:v:35:y:2008:i:3:p:507-526
    DOI: 10.1007/s00181-007-0176-8
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    References listed on IDEAS

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    1. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
    2. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
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    4. Guo, Jie Q & Trivedi, Pravin K, 2002. " Flexible Parametric Models for Long-Tailed Patent Count Distributions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(1), pages 63-82, February.
    5. Montalvo, Jose G, 1997. "GMM Estimation of Count-Panel-Data Models with Fixed Effects and Predetermined Instruments," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 82-89, January.
    6. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    7. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
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    17. Frank Windmeijer, 2002. "ExpEnd, A Gauss programme for non-linear GMM estimation of exponential models with endogenous regressors for cross section and panel data," CeMMAP working papers CWP14/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-263, May-June.
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    Cited by:

    1. Dechezlepretre, Antoine & Einiö, Elias & Martin, Ralf & Nguyen, Kieu-Trang & Reenen, John Van, 2016. "Do tax incentives for research increase firm innovation? An RD design for R&D," LSE Research Online Documents on Economics 66428, London School of Economics and Political Science, LSE Library.
    2. Rodil, Óscar & Vence, Xavier & Sánchez, María del Carmen, 2016. "The relationship between innovation and export behaviour: The case of Galician firms," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 248-265.
    3. Emilie-Pauline Gallié & Diègo Legros, 2012. "Firms’ human capital, R&D and innovation: a study on French firms," Empirical Economics, Springer, vol. 43(2), pages 581-596, October.
    4. Hagedoorn, John & Wang, Ning, 2010. "Is there complementarity or substitutability between internal and external R&D strategies?," MERIT Working Papers 005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    5. Qiang Liang & Xinchun Li & Xueru Yang & Danming Lin & Danhui Zheng, 2013. "How does family involvement affect innovation in China?," Asia Pacific Journal of Management, Springer, vol. 30(3), pages 677-695, September.
    6. Olof Ejermo & John Källström, 2016. "What is the causal effect of R&D on patenting activity in a “professor’s privilege” country? Evidence from Sweden," Small Business Economics, Springer, vol. 47(3), pages 677-694, October.
    7. repec:spr:empeco:v:53:y:2017:i:3:d:10.1007_s00181-016-1153-x is not listed on IDEAS
    8. Vicente German-Soto & Luis Gutiérrez Flores, 2015. "A Standardized Coefficients Model to Analyze the Regional Patents Activity: Evidence from the Mexican States," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 6(1), pages 72-89, March.
    9. Hagedoorn, John & Wang, Ning, 2012. "Is there complementarity or substitutability between internal and external R&D strategies?," Research Policy, Elsevier, vol. 41(6), pages 1072-1083.
    10. Yoshitsugu Kitazawa, 2012. "An improved theoretical ground for the linear feedback model and a new indicator," Discussion Papers 58, Kyushu Sangyo University, Faculty of Economics.

    More about this item

    Keywords

    Innovative activity; Patents and R&D; Individual effects; Count panel data methods; C20; O30;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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