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R&D Spillovers and Patent Production of British, French and German Firms

DIME Pécs workshop, 2011. R&D Spillovers and Patent Production of British, French and German Firms. Yih-Luan Chyi Department of Economics, National Tsing Hua University Taiwan. Outline. Introduction Analytical Framework Empirical Specification and Econometric Analysis

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R&D Spillovers and Patent Production of British, French and German Firms

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  1. DIME Pécs workshop, 2011 R&D Spillovers and Patent Production of British, French and German Firms Yih-Luan Chyi Department of Economics, National Tsing Hua University Taiwan

  2. Outline • Introduction • Analytical Framework • Empirical Specification and Econometric Analysis • Data Descriptions and Empirical Results • Conclusion DIME Workshop, Univ of Pecs

  3. I. Introduction: stylized fact 1 • Innovation activities and cross-EU distributions of R&D are concentrated in DE, FR, and UK : • Top 3 patent granted European countries in USPTO. • National R&D expenditures in total are more than 60 % of EU’s R&D in 2006. (Unit: Million Euro) DIME Workshop, Univ of Pecs

  4. I. Introduction: stylized fact 2 Table 1: Distribution of Top 1000 Corporate R&D investments across Europe in 2006 (Unit: Million Euro) Source: 2006 EU industrial R&D investment Scoreboard DIME Workshop, Univ of Pecs

  5. I. Introduction: Literature • Innovation activities and knowledge flows are concentrated in space. • Feldman (1994, 1999), and Moreno et al. (2005). • Jaffe et al. (1993), Branstetter (2001), Maurseth and Verspagen (2002), Bottazzi and Peri (2003), and Sonn and Storper (2008). • The geographical determinants of innovation at firm level are less explored; fewer empirical studies focus on EU: • US: Jaffe, 1989; Jaffe et al., 1993; Acs et al., 1994; Audrestsch and Feldman, 1996; Anselin et al., 1997; Adams, 2002. • Europe: Maurseth and Verspaen, 2002; Bottazzi and Peri, 2003; and Moreno et al., 2005. DIME Workshop, Univ of Pecs

  6. I. Introduction: research question • How do regional R&D spillovers affect innovation performances of heavy R&D investing firms located in British, French and German regions? • The impacts can be positive or negative depending on whether technological spillover effects dominate product market rivalry effects. • Bloom et al. (2010) • Firms belong to industries with diverse technological environments, and product market rivalry effects are varied in different industries. • Wilson (1977) DIME Workshop, Univ of Pecs

  7. I. Introduction: results highlights • Estimation results of GMM panel count model: • R&D spillovers positively influence patenting for British and German firms on average, whilst these effects vary by sectors. • Firms in British and French bio-pharm. industries and for those in German telecom. industry have negative spillovers effects • Different industry-wide technological environments in terms of opportunity and complexity; the product rivalry relative to tech spillovers are intensive in some industries. DIME Workshop, Univ of Pecs

  8. II. Analytical Framework: A Two-stage Game • Stage 1: firms choose R&D and f,the proportion of innovation output (y) it seeks for patent protection, Pi. • yi =g(RDi , RDτ); RDt: R&D invested by other firms interacting with it only in technology space. • 3 types of firms: i, t, and m • Stage 2: firms compete in product market by choosing price/quantity, z, conditional on its innovation rent, resulting in a profit: π(zi, zm, λiyi). • li =fiq+ (1－fi): average rent earned from innovation; • q ≥ 1: rents made from innovation with patent protection relative to those from the rest of innovation. DIME Workshop, Univ of Pecs

  9. II. Analytical Framework: Solving the model • Product Market Competition (Stage 2) • Nash decisions in zi*and zm* yield a reduced-form profit function of firm i in stage 1 as: π (zi*, zm*, λiyi) = Π(λiyi, λmym) • Knowledge Production (Stage 1) • Firm i sets RDi and fi to maximize the objective function, c: a constant cost of patenting per unit of patent output DIME Workshop, Univ of Pecs

  10. II. Analytical Framework: Theoretical results (Comparative statics on FOCs to sign partial derivatives) • Result 1: if sufficiently small P11 & positive g12 or strong enough technology spillovers. • P is concave, P1>0, P2 <0; • g(RDi, RDτ) is concave, non-decreasing in both arguments. • Result 2: if P11 is sufficiently small. • Strategic substitution, P12 < 0 • Strategic complementarity, P12 > 0. DIME Workshop, Univ of Pecs

  11. II. Analytical Framework: Reconciling two results with data constraints • Cannot distinguish two spillover effects empirically, yi,= f (RDi, RD-i) RD-i : R&D spending of other firms in close geographical space. • Assuming a fraction d of R&D investment of neighboring firms arising from firms in close technology space, • LKS have either positive or negative spillover effects: • Positive impacts of R&D spillovers on patent production if technology spillover effects tend to dominate, i.e., if large d , g12 > 0 and P12 > 0. DIME Workshop, Univ of Pecs

  12. III. Empirical Specification and Econometric Strategy: Wilson (1977) • Two dimensions of technological environments matter for propensity to patent and product rivalry. • Good opportunities for product innovation--more patents--technology spillovers • Degree of complexity: easy changing products; combination product innovations--competitive markets • The relative strengths vary across different industries. • The value of d depends on the relative intensity of product rivalry and thus spilovers effects on firms’ patent production are sector specific. DIME Workshop, Univ of Pecs

  13. III. Empirical Specification and Econometric Strategy • Reduced form and empirical analogue of the patent production function: • LKS: localized knowledge pool generating from proximate firms’ R&D activities • Kit: firm characteristics • From LKS to ILKS: industry specific R&D spillovers measures; Interacting LKS-i, with an indicator function DIME Workshop, Univ of Pecs

  14. III. Empirical Specification and Econometric Strategy • The GMM framework is adopted to estimate the patent production functions. • The moment conditions: • lagged values of panel-style instruments, RDi,t-s, s=1…,Ti. • To estimate βby minimizing: W:positive definite weighting matrix DIME Workshop, Univ of Pecs

  15. IV. Data Descriptions and Empirical Results:Data Sources • Panel data file: EU industrial R&D Scoreboard • 322 British firms from 2005 to 2007; • 114 French firms from 2002 to 2004; • 170 German firms in 2003, 2005 and 2007. • Patent: EPO • R&D and size (no. employees): EU industrial R&D Scoreboard • Regional R&D at the NUTS 2 level: Eurostat, census surveys • Classifications of industries: ICB • UK: bio, chemicals, machinery and support • FR: bio, auto, aerospace and telecom • DE: auto, bio, chemicals, electrical and telecom DIME Workshop, Univ of Pecs

  16. IV. Data Descriptions and Empirical Results: Descriptive Statistics in UK (Table 5A-1) DIME Workshop, Univ of Pecs

  17. IV. Data Descriptions and Empirical Results: Descriptive Statistics in FR (Table 5A-2) DIME Workshop, Univ of Pecs

  18. IV. Data Descriptions and Empirical Results: Descriptive Statistics in DE (Table 5A-3) DIME Workshop, Univ of Pecs

  19. IV. Data Descriptions and Empirical Results • rd: positive effects on patent production; • ilks: positive impacts on patent production of firms in UK and DE; • Spillovers effect has insignificant effect for French firms. DIME Workshop, Univ of Pecs

  20. IV. Data Descriptions and Empirical Results: • Main results hold after the effects of geographical concentration on firm innovation performance are controlled for. DIME Workshop, Univ of Pecs

  21. IV. Data Descriptions and Empirical Results DIME Workshop, Univ of Pecs

  22. IV. Data Descriptions and Empirical Results: Positive LKS Effects • Positive in the chemicals industry of UK and DE firms. • Evidence: expansion strategies such as vertical integration and alliances and cooperative agreements on technological grounds observed in European chemistry industry. (Arvanitis and Villavicencio, 2000) • Conceptual explanations: a high value of δ since good opportunities of technology advancements and thus firms are more likely to patent as well as benefit from spillovers in the technology space. • Negative effects in the telecom industry in DE. • Though high technological opportunities, but complex configurations of products intensify the product competition and lead to a low value of δ. DIME Workshop, Univ of Pecs

  23. IV. Data Descriptions and Empirical Results: Negative LKS Effects • Negative spillover effects in the bio industry • Rich opportunities of product changes: link to research institutions or where policy supports provided (Fuchs and Koch, 2005). • High degree of product rivalry since possible knowledge leak to rival firms with the similar technology in the bio industry (Shaver and Flyer, 2000) • Implication: dominance of negative spillovers effects from business stealing over the beneficial effects of technology spillovers. • Tests for model specifications: p-values of J statistics in all of the GMM models are significantly greater than 0.1, indicating cannot reject the specifications. DIME Workshop, Univ of Pecs

  24. V. Conclusion • A conceptual framework suggests that the localized knowledge pool may have positive or negative spillover effects on firm patent production depending on the share of R&D provided by firms interacting in technology space and how firms interact strategically. • Opportunities of product innovation and degree of product complexities vary across different industries; hence R&D spillovers may generate different impacts on patent production across various sectors. • Three specifications are estimated using GMM framework with the unbalanced panel data of top R&D investing firms in EU members: UK, FR and DE. DIME Workshop, Univ of Pecs

  25. V. Conclusion • Localized knowledge pool may enhance firm patenting in 2 out of 3 countries, while the innovation effects of R&D spillovers vary by sectors. • Positive spillover effects on patent production found for the chemicals industry of UK and DE firms, the auto industry of FR firms and the electrical industry of DE firms. • Negative spillover impacts found for the bio industry in UK and FR and for the telecom industry in DE. DIME Workshop, Univ of Pecs

  26. V. Conclusion • Problems in this study • Why there exists such a industry-specific or country-wide difference? any particular industry-region characteristic or government industry policies? • Short of evidence analogue to the interpretations of d. • Only one control for firm characteristics, alternative controls like firm age, total sales, … • Any policy implication (for firm or for government) can be drawn from this study? • To encourage sector-specific R&D cooperation or competition? DIME Workshop, Univ of Pecs

  27. V. Conclusion-cont. • Issues in future study • Disentangling the two opposite effects of R&D spillover on firm patent production; • Firms may cooperate or compete in technological or product market space to internalize spillovers. What are appropriate measures of localized knowledge pool in specific or agglomeration in general? • Overinvestment or underinvestment in firms R&D? What are policy implications? DIME Workshop, Univ of Pecs

  28. The End THANK YOU! DIME Workshop, Univ of Pecs

  29. Top 10 Nations of USPTO Granted Patents in 2006 DIME Workshop, Univ of Pecs

  30. NUTS 2 in FR DIME Workshop, Univ of Pecs

  31. NUTS 2 in DE DIME Workshop, Univ of Pecs

  32. NUTS 2 in UK DIME Workshop, Univ of Pecs

  33. Annual Patent Application by Firms in UK 33 33 DIME Workshop, Univ of Pecs

  34. Annual Patent Application by Firms in FR 34 34 DIME Workshop, Univ of Pecs

  35. Annual Patent Application by Firms in DE 35 35 DIME Workshop, Univ of Pecs

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