Patents and R&D An Econometric Investigation Using Applications for German, European and US Patents by German Companies

  • Georg Licht
  • Konrad Zoz


Based on the data of the first wave of the Mannheim Innovation panel, this paper explores the link between R&D expenditures and patents. Our data allow a detailed analysis of the firm size distribution of R&D and patent applications at different patent offices. It is shown that the share of R&D performing firms is stictly increasing win firm size. The share of firms applying for patents shows an even steeper increase with firm size. Moreover, large firms are more likely apply for patents in more than one country. The home patent office appears especially important for small firms. Using various count data models, the paper explores the relationship between R&D and patents at the firm level. We carefully test several distributional assumptions for count data models. A negative binomial hurdle model seems to be the most appropriate count data model for our data as the decision to patent inventions and the productivity of R&D are ruled by different mechanisms. Our estimates point towards significant returns to scale of R&D. Furthermore, the empirical results can be interpreted towards minor and insignificant spillover effects. Even after controlling for a variety of firm characteristics, firm size exhibits a large effect on the propensity to patent.


Firm Size Innovation Process Patent Application Innovation Activity Firm Characteristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bound, J., Cummings, C., Griliches, Z., Hall, B., Jaffe, A. (1984). — “Who Does R&D and Who Patents”, in: Griliches, Z., (Ed.), R&D, Patents, and Productivity, National Bureau of Economic Research, University of Chicago Press, 21–54.Google Scholar
  2. Cameron, A. C., Trivedi, P. K. (1986). — “Econometric Models Based on Count Data: Compariosn and Applications of Some Estimators and Tests”, Journal of Applied Econometrics, 1, pp. 29–53.CrossRefGoogle Scholar
  3. Cameron, A. C., Trivedi, P. K. (1990). — “Regression-Based for Overdispersion in the Poisson Model”, Journal of Econometrics, 46, pp. 347–364.CrossRefGoogle Scholar
  4. Chernoff, H. (1954). — “On the Distribution of the Likelihood Ratio”, Annals of Mathematical Statistics, 25, pp. 573–578.CrossRefGoogle Scholar
  5. Cohen, W. M., Levin, R. C. (1989). — “Empirical Studies of Innovation and Market Structure”, in: R. Schmalensee and R. Willig (Eds.), Handbook of Industrial Organisation, North-Holland: Amsterdam.Google Scholar
  6. Cohen, W. M., et al. (1997). — “Appropriability Conditions and Why Firms Patent and Why They Do Not in the American Manufacturing Sector”, Paper presented at the conference on “Economics and Economics of Innovation”, 3–6 Jun 1996, Strasbourg.Google Scholar
  7. Crépon, B., Duguet, E. (1996). — “Innovation: Measurement, Returns and Competition”, INSEE Studies, No. 1, pp. 83–96.Google Scholar
  8. Crépon, B., Duguet, E. (1997). — “Research and Development, Competition and Innovation. Pseudo Maximum Likelihood and Stimulated Maximum Likelihood Methods Applied to Count data Models with Heterogeneity”, Journal of Econometrics, 79, pp. 355–378.CrossRefGoogle Scholar
  9. Evenson, R. E. (1993). — “Patents, R&D, and Invention Potential: International Evidence”, AEA Papers and Proceedings, 83 No. 2, pp. 463–468.Google Scholar
  10. Felder, J., Light, G., Nerlinger, E., Stahl, H. (1996). — “Factors Determining R&D and Innovation Expenditure in German Manufacturing Industries”, in: Kleinknecht, A., (1996), R&D and Innovation. Evidence from New Indicators, Macmillan Press: Basingstoke, pp. 125–154.Google Scholar
  11. Giese, E., Stoutz, R. V. (1997). — “Indikatorfunktion von Patentanmeldung für regionalanalytische Zwecke in der Bundesrepublik Deutschland”, Studien zur Wirtschaftsgeographie, Universität Giessen.Google Scholar
  12. Gourieroux, C., Monfort, A., Trognon, A. (1984). — “Pseudo Maximum Likelihood Methods: Application to Poisson Models”, Econometrica, 52, pp. 701–720.CrossRefGoogle Scholar
  13. Greene, W. H. (1994). — “Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models”, New York University Discussion Paper, EC-94–10.Google Scholar
  14. Griliches, Z. (1990). — “Patent Statistics as Economic Indicators: a Survey”, Journal of Economic Literature, 28, pp. 1661–1707.Google Scholar
  15. Harhoff, D. (1994). — “R&D and Productivity in German Manufacturing Firms”, ZEW-Discussion Paper 94–01, Mannheim.Google Scholar
  16. Harhoff, D., Licht, G., Beise, M., Felder, J., Nerlinger, E., Stahl, H. (1996). — Innovationsaktivitäten kleiner and mittlerer Unternehmen. Ergebnisse des Mannheimer Innovationspanels, Nomos-Verlag: Baden-Baden.Google Scholar
  17. Harter, J. F. R. (1993). — “The Propensity to Patent with Differentiated Products”, Southern Economic Journal, 61, pp. 195–201.CrossRefGoogle Scholar
  18. Hausman, J., Hall, B., Griliches, Z. (1984). — “Econometric Models for Count Data with an Application to the Patents-R&D Relationship”, Econometrica, 52, pp. 909–938.CrossRefGoogle Scholar
  19. Horstmann, I., Macdonald, G. M., Slivinski, A. (1985). — “Patents as Information Transfer Mechanisms: To Patent or (Maybe) Not to Patent”, Journal of Political Economy, 93, pp. 837–858.CrossRefGoogle Scholar
  20. Jaffe, A. B. (1989). — “Characterizing the ‘Technological Position’ of Firms, with Application to Quantifying Technological Opportunity and Research Spillovers”, Research Policy, 18, pp. 87–97.CrossRefGoogle Scholar
  21. Kabla, I. (1996). — “The Patent as Indicator of Innovation”, INSEE Studies, No. 1, pp. 57–72.Google Scholar
  22. Kleinknecht, A., Reijnen, J. O. N. (1991). — “New Evidence on the Undercounting of Small Firm R&D”, Research Policy, 20, pp. 579–587.CrossRefGoogle Scholar
  23. König, H., Licht, G. (1995). — “Patents, R&D and Innovation. Evidence from the Mannheim Innovation Panel”, ifo-Studien, 41, pp. 521–545.Google Scholar
  24. Lach, S. (1995). — “Patents and Productivity Growth at the Industry Level: A First Look”, Economics Letters, 49, pp. 101–108.CrossRefGoogle Scholar
  25. Lambert, D. (1992). — “Zero Inflated Poisson Regression with an Application to Defects in Manufacturing”, Technometrics, 34, pp. 1–14.CrossRefGoogle Scholar
  26. Lanjouw, J. O., Pakes, A., Putnam, J. (1996). — “How to Count Patents and Value Intellectual Property: Uses of Patent Renewal and Application Data”, Mimeo, Yale University, New Haven.Google Scholar
  27. Lawless, J. F. (1987). — “Negative Binomial and Mixed Poisson Regression”, The Canadian Journal of Statistics, Vol. 15, No. 3, pp. 209–225.CrossRefGoogle Scholar
  28. Levin, R. C., Reiss, P. C. (1987). — “Cost-reducing and Demand-creating R&D with Spillovers”, RAND Journal of Economics, 19, pp. 538–556.CrossRefGoogle Scholar
  29. Levin, R. C., Klevorik, A. K., Nelson, R. R., Winter, S. G. (1987). — “Appropriating the Returns from Industrial Research and Development”, Brookings Papers on Economic Activity. Special Issue on Microeconomics, pp. 783–831.Google Scholar
  30. Mccullagh, P., Nelder, J. A. (1989). — Generalized Linear Models, 2nd ed., Chapman and Hall: London.Google Scholar
  31. Mullahy, J. (1986). — “Specification and Testing of Some Modified Count Data Models”, Journal of Econometrics, 33, pp. 341–365.CrossRefGoogle Scholar
  32. National Science Board (1996). — Science & Engineering Indicators 1996, Washington, DC: U.S. Government Printing Office 1996.Google Scholar
  33. NIW, DIW, ISI, ZEW (1996). — Germany’s Technological Performance. Updated and expanded report, Hannover/Berlin/Karlsruhe/Mannheim.Google Scholar
  34. OECD (1993). — Proposed Standard Practice For Surveys of Research and Experimental Development — Frascati Manual, Paris.Google Scholar
  35. OECD (1994). — Using Patent Data as Science and Technology IndicatorsPatent Manual, Paris.Google Scholar
  36. OECD (1997). — OECD Proposed Guidelines For Collecting and Interpreting Technological Innovation Data — OSLO Manual, Second edition, Paris.CrossRefGoogle Scholar
  37. Pakes, A. (1985). — “Patents, R&D, and the Stock Market Rate of Return”, Journal of Political Economy, 93, pp. 390–409.CrossRefGoogle Scholar
  38. Pavitt, K. (1985). — “Patent Statistics as Indicators of Innovative Activities: Possibilities and Problems”, Scientometrics, 7, pp. 77–99.CrossRefGoogle Scholar
  39. Pohlmeier, W., Ulrich, V. (1995). — “An Econometric Model of the Two-Part Decision Process in the Demand for Health”, Journal of Human Resources, 30, pp. 339–361.CrossRefGoogle Scholar
  40. Scherer, F. M. (1983). — “The Propensity to Patent”, International Journal of Industrial Organisation, 1, pp. 107–128.CrossRefGoogle Scholar
  41. Sirilli, G. (1987). — “Patents and Inventors: An Empirical Study”, Research Policy, 16, pp. 157–174.CrossRefGoogle Scholar
  42. SV-Wissenschaftsstatistik (1994). — FuE-Info. Forschung und Entwicklung in der Wirtschaft. Ergebnisse 1992, 1993, Planung 1994, Essen.Google Scholar
  43. Vuong, Q. H. (1989). — “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses”, Econometrica, Vol. 57, pp. 307–333.CrossRefGoogle Scholar
  44. Winkelmann, R. (1994). — “Count Data Models: Econometnc Theory and an Application to Labour Mobility”, Springer: Heidelberg, Berlin, New York.Google Scholar
  45. Winkelmann, R., Zimmermann, K. F. (1995). — “Recent Developments in Count Data Modelling: Theory and Application”, Journal of Economic Surveys, 9, pp. 1–24.CrossRefGoogle Scholar
  46. Zimmermann, K. F., Schwalbach, J. (1991). — “Determinanten der Patentaktivität”, ifo-Studien, 37, pp. 201–227.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Georg Licht
    • 1
  • Konrad Zoz
    • 2
  1. 1.ZEWMannheimGermany
  2. 2.University of WürzburgGermany

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