Ludwig Maximilians Universität München 15 th January, International R&D Competition, Spillovers and Firms Economic Performance

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1 INNO-tec Workshop Ludwig Maximilians Universität München 15 th January, 2004 International R&D Competition, Spillovers and Firms Economic Performance Prof. Dr. Michele Cincera Université Libre de Bruxelles DULBEA & CEPR

2 1. Introduction OBJECTIVES OF THE PAPER: Assessing the impacts of main determinants of firms technological activity, i.e. R&D capital, R&D of competitors, technological spillovers on their R&D intensity and economic performance (TFP growth). Extend Jaffe s methodology by examining alternative methods for measuring R&D spillovers and test their impacts in terms of the robustness of results.

3 1. Introduction OUTLINE OF THIS TALK: 2. Determinants and outcomes of firm s technological activities. 3. concepts of technological spillovers. 4. Framework for characterising and differentiating the technological determinants. 5. Firms attribution to techn. clusters and distortion measures. 6. Data sample (625 world-wide manufacturing firms, ). 7. R&D reaction functions, productivity equation and alternative specifications of spillovers. 8. Empirical findings. 9. Conclusion.

4 2. Determinants and effects of the firm s technological activity TECHNOLOGY PUSH FACTORS DEMAND PULL FACTORS GEOGRAPHIC FACTORS * technological spillovers : * market power (ex ante) : * geographic spillovers [+], [-] diffusion (Griliches 92 ; Jaffe 86) [+] [-] replacement effect (Arrow 62) [-] * geographic opportunity [+], [-] competitive (Loury 79, Reinganum 81) [+] preventive effect (Gilbert &Newberry 82) [+] * technological opportunity : * market power (expected ex post) : (Rosenberg 74, 83 ; Scherer 68, 82) [+] (Schumpeter 42 ; Schmookler 66) [+] * market opportunity [+] TECHNOLOGICAL ACTIVITY OF INNOVATION input Output R&D expenditures technological metaproduction function Innovation (L r&d, K r&d) (process, products) OUTCOMES OF THE TECHNOLOGICAL ACTIVITY OF INNOVATION * R&D strategic adjustments * Profits * Number of patent *Employment applications *TFP *Exports * Production costs * Market share (ex post)

5 3. concepts of technological spillovers Rent spillovers. Pure knowledge spillovers. Competitive spillovers. Geographic spillovers. Organizational spillovers. NB: difficult to disentangle (both theoretically and empirically) between these types of spillovers.

6 4. Framework for characterising and differentiating the technological determinants FIRM s OWN R&D CAPITAL: permanent inventory method. R&D CAPITAL of competitors: sum of competitors R&D.

7 4. Formalizing pure knowledge spillovers (1/4) S = P RD i ij j j j i Potential spillover pool = weighted sum of all other firms R&D weights depend on firms technological proximities: a) No weight b) Transaction based linkages: Inter-firm (or inter-industry or international) flows of good & services, capital goods, R&D personnel, patents, innovations, citations, r&d collaborations, c) Technology based linkages: Vectors characterizing firms (industries, regions) into a technological space space NB: b) and c) are complementary, b) underestimate the real magnitude of pure knowledge spillovers (embody rent spillovers)

8 4. Formalizing pure knowledge spillovers (2/4) TECHNOLOGICAL SPACE (Jaffe 1986, 88, 89): Technological position vector of firm i: 1 T= i t ( i1,..., tik) j i 3 k K Technological proximity measure = uncentered correlation of technological vectors: 2 P ij TT i = TT TT i i ' j j ' j ' [ 01, ]

9 4. Formalizing pure knowledge spillovers (3/4) example: NPA DP SL RN HD 3272 Du Pont Solvay Renault Honda limits of such a formulation: - appropriability/absorptive conditions are the same for all firms - symmetrical - firms which do not patent - firms with rather diversified technol. activities - relevant measure?

10 4. Formalizing pure knowledge spillovers (4/4) splitting the potential spillover stock in several components: national stock LNS NS local LS international stock LIS national stock ENS external ES international stock EIS IS

11 5. Firms attribution to techn.. clusters: 3 methods K-means (Jaffe). K-means strong centers. agglomerative hierarchical. clustering algorithm uses factorial the coordinates of a binary correspondence analysis based on the contingency table of the firms patent distribution across 50 IPC classes. Ward criterion: within and without cluster inertia. optimal number of clusters? (Thorndike, 1953).

12 5. Firms attribution to techn.. clusters: 3 methods

13 5. Firms attribution to techn.. clusters: 3 methods

14 5. Firms attribution to techn.. clusters: 3 methods

15 5. Alternative technological proximity measures

16 5. Alternative technological proximity measures

17 6. DATA DATA SAMPLE AND CONSTRUCTED VARIABLES: 2676 international firms net sales, employment, net plant, property & equipment. R&D expenditures. industry sectors (SIC). patent application over patent classes (IPC) (1618 firms). variables deflated and converted in 90 $. constructed variables: * R&D capital, * spillover stocks, * technological, market and geographic dummies. BALANCED AND CLEANED PANEL: BALANCED AND CLEANED PANEL: 625 firms,

18 6. DATA REPRESENTATIVENESS: Rest of the world Europe Japan United-States SECTORIAL AND GEOGRAPHICAL CHARACTERISTICS OF VARIABLES: (average over the period ) 1994)

19 6. DATA Number of firmsa Physical capital rateb R&D intensityb Spillovers potentialc EU JP US Aircraft Chemicals Computer Construction Drugs Electrical Electronics Fabricated metal Food Instruments Machinery Mining Motor vehicles Paper Primary metal Rubber Software Stone Textiles Wood Average Europe Japan United-States Notes: a: among which 13 firms from Australia and Canada; b: %; spillover pool / R&D exp.

20 7. R&D reaction function and productivity equation R&D REACTION FUNCTION: ln R it = α i + β 1 ln R it-1 + β 2 ln S it + β 3 ln C it + ln X it +γln Z it + Σφ M mt m + Σφ L lt l + Σφ G gt g + ε it where ln = natural logarithm; Rit = annual R&D exp. of firm i at time t (i = 1 to 625, t = 1 to 8); Sit = net sales; Cit = stock of physical capital; Xit = vector of spillover components; Zit = vector of R&D of competitors; Tm, Tl, Tg = vectors of dummies for technological, sectoral and geographical opportunities; αi = firm fixed effect and εit = disturbance term.

21 7. R&D reaction function and productivity equation R&D REACTION FUNCTION: ln R it = α i + β 1 ln R it-1 + β 2 ln S it + β 3 ln C it + ln X it +γln Z it + Σφ M mt m + Σφ L lt l + Σφ G gt g + ε it STOCK OF COMPETITORS R&D: inter-industry R&D equation (estimated on the basis of the balanced sample of 625 firms): γ ln Z it = γ ln SR it = γ K lnσ i and j K, i j R jt where K = industrial sectors, i.e. K {aircraft,..., wood}. intra-industry R&D equation (estimated on the basis of the set of industry specific samples of firms): γ ln Z it = γ ln RG = Σ G L [γ G lnσ j G R jt ] where G= three geographic areas, i.e. G {EU, JP, US};

22 7. R&D reaction function and productivity equation PRODUCTIVITY EQUATION: ln S it = α i + β 1 ln L it + β 2 ln C it + β 3 ln K it + γln X it +λ t +ε it where L it = employment; K it = stock of R&D capital. STOCK OF SPILLOVERS: specification I: γln X it = γ T ln TS it, where TS = stock of total spillovers. specification II: γln X it = γ T ln TS it + γ L [LS it / TS it ] where LS = stock of local spillovers.

23 7. R&D reaction function and productivity equation STOCK OF SPILLOVERS: specification III: δ ln X it = δ T ln TS it +δ N [NS it / TS it ] where NS = stock of national spillovers. specification IV: γln X it = γ N ln NS it + γ I ln IS it where NS = stock of national spillovers; IS = stock of international spillovers.

24 8. R&D intensity: inter industry Dependent Variable: ln R instruments: lag 2 and lower values of regressors sample: 625 firms x 8 years specification model I IA II IIA III IIIA ln R(t-1).07 (.04)a.12 (.03).10 (.03).13 (.02).31 (.06).15 (.02) ln S.40 (.10).36 (.07).46 (.06).39 (.05).44 (.07).34 (.05) ln C (.10) (.07) (.06) (.05) (.09) ln TS (1.01) (.50) (.38) (.35) (.58) ln SR (.08) (.06) LS/TS (.09) (.08) NS/TS.30 (.22) overidentificationb χ 2 (D.F.) [prob.]d (88) (112) (112) (136) (62) simultaneityc χ2 (D.F.) [prob.] [.201] lags (39) [.000] [.171] lags (50) [.000] [.047] lags (72) [.000] [.115] lags (83) [.000] [.236] lags (25) [.001] (.05).93 (.34).26 (.07).13 (.19) (136) [.068] lags (83) [.000] notes: a: heteroskedastic-consistent standard errors in brackets, b: test of the validity of moment restrictions, c: test of the most recent lag admitted as instruments, d: upper tail area

25 - 8. R&D intensity: intra industry AIRCRAFT CHEMICALS EU EU US JP US JP DRUGS ELECTRONICS EU EU US JP US JP INSTRUMENTS MOTOR VEHICLES EU EU US JP US JP dependent Variable: ln R Industry COMPUTERS # of firms # of periods Lag ln R(t-1) -.01 (.01).02 (.01) -.01 (.01) ln S.35 (.25).27 (.16).30 (.08) ln C.28 (.13).11 (.13).10 (.07) ln REU -.14 (.56).16 (.54) ln RJP -.09 (1.1).15 (.23) ln RUS -.98 (2.69).94 (.42) R² ad US COMPUTERS EU + JP

26 8. Productivity estimates by geographic area dependent Variable: ln S WITHIN Level OLS F.D. GMM-IV F.D. US sample 3024 (2646) obs. LnL.66 (.030) a * lnl.47 (.031)* lnl.51 (.012)* LnC.11 (.027)* lnc.13 (.025)* lnc.10 (.001)* lnk.18 (.024)* lnk.28 (.039)* lnk.25 (.013)* lnns.69 (.179)* lnns.59 (.202)* lnns.56 (.075)* lnis -.02 (.155) lnis -.43 (.273) lnis -.35 (.122)* Ra².995 Ra².468 X² [d.f.]b [195] Sim.c: S JP sample 1064 (931) obs. lnl.23 (.053)* lnl.11 (.040)* lnl.09 (.001)* lnc.28 (.033)* lnc.18 (.035)* lnc.12 (.001)* LnK.07 (.040)** lnk.28 (.114)* lnk.10 (.001)* LnNS -.17 (.149) lnns -.23 (.403) lnns.28 (.028)* LnIS.91 (.307)* lnis 1.46 (.621)* lnis.97 (.065)* Ra².992 Ra².221 X² [d.f.] [120] Sim.: W EU sample 808 (707) obs. LnL.63 (.052)* lnl.53 (.066)* lnl.56 (.001)* LnC.18 (.035)* lnc.09 (.040)* lnc.11 (.001)* LnK.04 (.053) lnk.22 (.105)* lnk.15 (.001)* LnNS.13 (.140) lnns.13 (.281) lnns.12 (.032)* LnIS.32 (.269) lnis.06 (.565) lnis -.12 (.030)* Ra².996 Ra².417 X² [d.f] 97.4 [95] Sim.: +L1 a: heteroskedastic-consistent standard errors in brackets (except for JP and EU GMM estimates), * (**) =statistically significant at the 5 (10)% level; b: overidentification test; c: predeterminancy of Xit: W(S)= weak (strong) exogeneity, +L3 =lag 3 and lower values of Xit as instruments

27 8. Productivity estimates by geographic area no spillovers EU US.6 JP.3 national international spillovers 1.5 spillovers

28 8. Productivity estimates: local and external spillover stock

29 8. Robusteness of spillovers effects

30 8. Robusteness of spillovers effects

31 8. Robusteness of spillovers effects

32 8. Robusteness of spillovers effects

33 9. Conclusion Empirical results: Empirical results: R&D intensity Firms are to different degrees sensitive to R&D outlays of competitors, behaviours are not homogeneous across industries and among countries. Increase of 1% of R&D spillovers could stimulate an increase from.6 to 1 % of firm s own R&D. Productivity growth Positive and higher (a.c. own R&D) impact of R&D spillovers, (local international stock especially) on productivity performance USA, Japan and Europe appear to adopt very differentiated behaviours: US firms: mainly concerned with their national spillover stock. Japanese ones: more receptive to the international stock. European ones do not particularly benefit from both sources of spillovers.

34 9. Conclusion Clustering methods and proximity measures : Results robust to alternative construction of spillover variable. Choice of a distance metric can affect the nature of results. Further research: Further research: Extend sample of firms (1998), 5000 R&D firms. US patents (USPTO) + citations. High tech, low tech intra- and international spillover components. Other channels of knowledge diffusion, e.g. R&D collaborations: Cincera, van Pottelsberghe and Veugelers (2003). Crepon, Duguet, Mairesse (1998) model. Timing of spillovers effects.

35 Danke für Ihre Aufmerksamkeit!