1. Innovation and Productivity in France: A firm-level analysis for Manufacturing and Services (1998-2000 and 2002-2004) Jacques Mairesse Stéphane Robin CREST-ENSAE,BETA UNU-MERIT, and NBER University of Strasbourg 1 Innovacio a nivell empresarial XREAP, Universitat de Barcelona, 1de juliol de 2008

2. Jacques Mairesse Stephane Robin Barcelona, 1 July 20082 OUTLINE Bird-eye view of the « CDM » model Model and empirical strategy Data Specification of the model Results Comparisons and further analyses Conclusion

3. Jacques Mairesse Stephane Robin Barcelona, 1 July 20083 The « CDM » model with Bruno Crepon and Emmanuel Duguet Brings together the three main fields of investigation in the econometrics of research and innovation Proposes a “simple” framework articulating innovative and productive activities Takes advantage of the innovation survey information Uses estimation methods appropriate to the specification of the model and nature of data

4. Jacques Mairesse Stephane Robin Barcelona, 1 July 20084 The « CDM » model Diversification Market share Knowledge Capital R&D Innovation Productivity Size Industry Demand Pull Technology Push Physical Capital Skills

5. Jacques Mairesse Stephane Robin Barcelona, 1 July 20085 Past and on-going work (1) Crépon, B., E. Duguet and J. Mairesse (1998), “Research and Development, Innovation and Productivity: An Econometric Analysis at the Firm Level”, Economics of Innovation and New Technology, 7(2), 115-158. Mairesse, J. and P. Mohnen (2001), “To Be or Not to Be Innovative: An Exercise in Measurement”, STI Review. Special Issue on New Science and Technology Indicators, OECD, 27, 103-129. Mairesse, J. and P. Mohnen (2002), “Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application”, American Economic Review, Papers and Proceedings, 92(2), 226-230. Mairesse, J. and P. Mohnen (2005), “The Importance of R&D for Innovation: A Reassessment Using French Survey Data”, The Journal of Technology Transfer, special issue in memory of Edwin Mansfield, 30(1-2), 183-197. Hall, B.H. and J. Mairesse (2006),"Empirical Studies of Innovation in the Knowledge Driven Economy", Introduction to a special issue on: “Empirical studies of innovation in the knowledge driven economy”, Economics of Innovation and New Technology, 15(4/5), 289-299. Mohnen, P., J. Mairesse and M. Dagenais (2006), “Innovativity: A Comparison across Seven European Countries”, special issue on: “Empirical studies of innovation in the knowledge driven economy”Economics of Innovation and New Technology, 15(4/5), 391-413.

6. Jacques Mairesse Stephane Robin Barcelona, 1 July 20086 Past and on-going work (2) Kremp, E., and J. Mairesse (2003), “Knowledge Management, Innovation and Productivity: A Firm Level Exploration Based on the French CIS3 Data”, in D. Foray and F. Gault eds., “Measuring Knowledge management in the Business Sector”, OECD. Mairesse, J. and P. Mohnen (2004), “Intellectual Property in Services: What Do We Learn from Innovation Surveys ?”, in Patents, Innovation, and Economic Performance, OECD Conference Proceedings, OECD, Paris, 227-245. Griffith R., E. Huergo, J. Mairesse and Bettina Peters (2006), "Innovation and Productivity across Four European Countries", Oxford Review of Economic Policy, 22(4), 483-498. Hall, B. H., F. Lotti and J. Mairesse (2007) "Employment, Innovation and Productivity: Evidence from Italian MicroData", Industrial and Corporate Change, Forthcoming. Harrison, R., J. Jaumandreu, J. Mairesse and Bettina Peters, (2005), "Does Innovation Stimulate Employment? A Firm-Level Analysis Using Comparable Micro Data from Four European Countries", Mimeo, January. Kremp, E., J. Mairesse and P. Mohnen (2005), “The Importance of R&D and Innovation for Productivity: A Reexamination in Light of the 2000 French Innovation Survey”, Draft, November.

7. Jacques Mairesse Stephane Robin Barcelona, 1 July 20087 Special issue on Empirical studies of innovation in the knowledge driven economy in Economics of Innovation and New Technology, B. Hall and J. Mairesse, guest eds. (2006) Benavente, J-M., “The Role of Research and Innovation in Promoting Productivity in Chile”. Heshmati, A. and H. Lööf, “Knowledge Capital and Heterogeneity in Firm Performance. A Sensitivity Analysis”. Jefferson, G., B. Huamao, G. Xiaojing and Y. Xiaoyun, “R&D performance in Chinese industry”. Van Leeuven G., and L. Klomp, “On the Contribution of Innovation to Multi-Factor Productivity”. Duguet, E., “Innovation Height, Spillovers and TFP Growth at the Firm Level: Evidence from French Manufacturing for Company Performance”. ….

8. Jacques Mairesse Stephane Robin Barcelona, 1 July 20088 OTHER STUDIES (very very incomplete) Klomp, L. and G. Van Leeuwen G. (2001), “Linking Innovation and Firm Performance: A New Approach”, Journal of the Economics of Business, 8(3), 343- 364. Lööf, H. and A. Heshmati (2002), “Knowledge Capital and Performance Heterogeneity: A Firm-Level Innovation Study”, International Journal of Production Economics, 76(1), 61-85. Lööf, H., A. Heshmati, R. Apslund and S.O. Nås (2002), “Innovation and Performance in Manufacturing Firms: A comparison of the Nordic Countries”, mimeo. Criscuolo, C. and J. Haskel (2003), “Innovations and Productivity Growth in the UK: Evidence from CIS2 and CIS3”, CeRiBa discussion paper. Galia, F. and D. Legros (2003), “Research and Development, Innovation, Training, Quality and Profitability: Econometric Evidence from France”, mimeo. Janz, N., H. Lööf, and B. Peters (2004), “Firm level Innovation and Productivity: Is There a Common Story across Countries?”, Problems and Perspectives in Management, 2, 184-204. Parisi, M., F. Schiantarelli and A. Sembenelli (2006), “Productivity, Innovation and R&D: Micro Evidence for Italy”, European Economic Review, 50, 2037–2061.

9. Jacques Mairesse Stephane Robin Barcelona, 1 July 20089 Objectives of the present study The present study (re-)investigates the links between R&D, innovation and productivity in the French Manufacturing and Services industries Estimating a variant of the “CDM” econometric framework on the last two waves of the French CIS for the periods (1998-2000) and (2002-2004).

10. Jacques Mairesse Stephane Robin Barcelona, 1 July 200810 Empirical strategy Our model is a variant of the one estimated by Griffith, Huergo, Peters & Mairesse (2006), which is itself a convenient simplification of the original CDM model. We try to improve on Griffith and al.(2006): by including indicators of “demand pull / technology push” that are available only in the French CIS survey by estimating process and product innovation simultaneously

11. Jacques Mairesse Stephane Robin Barcelona, 1 July 200811 Data: the French CIS3 and CIS4 Our data comes from the 3 rd and 4 th waves of the Community Innovation Survey (CIS3 and CIS4). CIS : harmonised survey carried out by national statistical agencies in all 27 EU Member States under the co-ordination of Eurostat (core questionnaire + country-specific questionnaire). CIS3 covers the period 1998-2000, and CIS4 the period 2002-2004. Both surveys gives information on: R&D activities, product and process innovation, intellectual property protection, effects of innovation, abandoned innovations, hampering factors.

12. Jacques Mairesse Stephane Robin Barcelona, 1 July 200812 Differences between CIS3 and CIS4 In CIS3: information about investment in physical capital and quality of labour force. This information is not available in CIS4. In CIS4, sampling is based on firms with 10+ employees (vs 20+ employees in CIS3). CIS3: representative of manufacturing industry only, whereas CIS4: representative of both manufacturing and services. We will compare: manufacturing industry in France from 1998-2000 to 2002-2004. manufacturing and services industries in the recent period (2002-2004).

13. Jacques Mairesse Stephane Robin Barcelona, 1 July 200813 Comparison of CIS3 and CIS4

14. Jacques Mairesse Stephane Robin Barcelona, 1 July 200814 Comparison of manufacturing and services

15. Jacques Mairesse Stephane Robin Barcelona, 1 July 200815 Specification of the model Stage 1: R&D equations (Generalized Tobit model)  Pr(Continuous R&D)=  (International market, Appropriability conditions, firm  size, Demand pulled / Technology pushed, 2-digit industry dummies)  Log(R&D intensity)=f(International market, Cooperation, Public funding,  Appropriability conditions, Demand pulled / Technology pushed,  Sources of information, 2-digit industry dummies) Stage 2: innovation production function (bivariate Probit model)  Pr(Product innovation)=  (Appropriability conditions, firm size,  Predicted Log-R&D intensity, 2-digit industry dummies)  Pr(Process innovation)=  (Appropriability conditions, firm size,  Predicted Log-R&D intensity, 2-digit industry dummies) Stage 3: productivity equation (linear model) Log(Labour Productivity)=f(predicted probabilities of: product only, process only, product and process innovation, firm size, 2-digit industry dummies)

16. Jacques Mairesse Stephane Robin Barcelona, 1 July 200816 Main results: stage 1 We report marginal effects, with robust standard errors in brackets. Significance: * 10%, ** 5%, *** 1%

17. Jacques Mairesse Stephane Robin Barcelona, 1 July 200817 Main results: stage 2 We report marginal effects, with robust standard errors in brackets. Significance: * 10%, ** 5%, *** 1%

18. Jacques Mairesse Stephane Robin Barcelona, 1 July 200818 Main results: stage 3 We report marginal effects, with robust standard errors in brackets. Significance: * 10%, ** 5%, *** 1% Productivity equation, dependent variable: Log of labour productivity

19. Jacques Mairesse Stephane Robin Barcelona, 1 July 200819 Robustness checks We replicated our analysis on CIS2 (1994-1996), w/ some changes in the specification of the model. The results we obtain are consistent with those obtained on CIS3 and CIS4. We also estimated our model on pooled CIS3 - CIS4 data (1717 manufacturing firms), including a time effect and a time*industry effect. The analysis on the pooled data confirms our previous results.

20. Jacques Mairesse Stephane Robin Barcelona, 1 July 200820 Lagged analysis and growth of LP We estimated the model with CIS4 dependent variables, using both CIS4 variables and lagged (CIS3) explanatory variables. In addition, we implemented two alternative specifications involving the growth of labour productivity instead of its level. As before, we find that product innovation (on its own or combined w/ process innovation) is associated with a higher labour productivity. Process innovation has no significant effect. However, we fail to find any significant effect of innovation on the growth of labour productivity.

21. Jacques Mairesse Stephane Robin Barcelona, 1 July 200821 Conclusion We estimated a three-stage econometric model linking research, innovation, and labour productivity using recent French data (CIS3 and CIS4). We compare two periods (1998-2000 and 2002-2004) and two sectors in the recent period (manufacturing and services) We find that product innovation (on its own or combined with process innovation) has a positive impact on labour productivity. The effect of process innovation appears to be weak. These results hold for the previous period (CIS2: 1994-1996) and are robust when we use a matched CIS3-CIS4 sample

22. “Innovation is not the product of logical thought” (Albert Einstein, 1879-1955)