1. A comparative analysis of Innovation and Productivity: lessons learned and work ahead Chiara Criscuolo Centre for Economics Performance London School of Economics

2. This is a joint effort with... 1.Australia: David (ABS) 2.Austria: Martin Berger 3.Belgium: Jeoffrey Malek 4.Brazil: Bruno Araújo and João De Negri 5.Canada: Petr Hanel and Pierre Therrien 6.Denmark: Carter Bloch and Ebbe Graversen 7.Finland: Mariagrazia Squicciarini Olavi Lehtoranta Mervi Niemi 8.France: Stephane Robin and Jacques Mairesse 9.Germany: Bettina Peters 10.Italy: Francesco Crespi Mario Denni Rinaldo Evangelista and Mario Pianta 11.Japan: Tomohiro Ijichi (could not participate for data problems) 12.Korea: Seok-Hyeon Kim 13.Luxembourg: Anna-Leena Asikainen 14.Netherlands: George van Leeuwen, Pierre Mohnen, Michael Polder, Wladimir Raymond 15.New Zealand: Richard Fabling 16.Norway: Svein Olav Nås and Mark Knell 17.Sweden: Hans Loof 18.Switzerland: Spyros Arvanitis 19.UK: Chiara Criscuolo A big THANK YOU to all!

3. The challenge Comparative analysis of the innovation productivity link using a “structural” model Same model across countries: same variables; same estimation method So that estimates are “comparable” across countries Tools: Innovation Surveys Estimation routines

4. The difficulties Countries data cannot be pooled so each country must estimate model separately (in general no one outside country team can see the data) Not all countries could be at all meetings Innovation surveys similar (CIS “harmonized”) but not exactly the same across countries both in terms of variables used and presence of “filter” questions HIGH COORDINATIONS COSTS!!!!!!

5. Solutions Use the same model across countries: – Minimum common denominator set of variables – Use a model that control for selection for all countries “Centralize” implementation of estimation routines: – Lead-country decides model – Modify specific country model so that it is estimable in all countries (variables included – see also IPR topic; flexibility in using different variable names)

6. Questions Was the centralized model the right one? Should we have used a bottom up approach? Should have more countries be involved with the formulation of the model? How useful was it to have the programming routine? …Should we change the approach for the follow- up project?

7. A brief outline of the model followed… …To estimate the effects of innovation on productivity controlling for selection and endogeneity Following the Crepon-Duguet Mairesse “tradition” we estimate a 3 stage/4 equation model: 1 st innovation equation 2 nd innovation input equation 3 rd innovation output equation 4 th productivity equation

8. The model 3 stage with 4 equations 1 st stage explains firms’ decision whether to engage in innovation activities or not and the decision on the amount of innovation expenditure Prob(innovation=1)=f(size; Group; Foreign Market, Obstacles to innovation due to knowledge; costs and market; industry dummies) Ln(innovation expenditure per employee)=f(Group; Foreign Market; Cooperation; Financial Support; industry dummies) In the 2 nd stage we estimate the knowledge production function where innovative sales depends on investment in innovation. Ln(innovative sales per employee)=f(Innovation expenditure; Size; Group; process innovation; Cooperation with clients; suppliers; other private and public agents; industry dummies; Mills ratio [to correct for selection]) The 3 rd stage we estimate the innovation output productivity link using an augmented Cobb-Douglas production function using IV. Ln(sales per employee)=f(Size; Group; Process Innovation; log innovative sales per employee; industry dummies [Human Capital and Physical Capital])

9. Obstacles to innovation

10. Cooperation partners

11. Extensions/variations of the model... Some countries could add Human Capital (H); Physical Capital (K) and Materials (M) in the productivity equation: – Austria (H,K); Belgium (H,K); Brazil (H,K,M); Canada (H,K); Finland (H,K,M); Germany (H,K,M); New Zealand (K,M); UK (H) Sales per employee is a very rough measure of productivity. Ideally we would want value added per employee (Labour Productivity measure) or Multi Factor Productivity measures To do this innovation surveys must be combined with other production datasets. (follow-up of current project?)

12. …Extensions/variations of the model... Most countries estimate separately for small vs large firms and manufacturing vs services firms (excl. Italy and Norway); Korea and Canada only manuf; Luxembourg serv and small. Important to look at differences Standard size threshold 250 employees but with some variation Issue for small countries and/or small surveys problem of small sample sizes

13. …Extensions/variations of the model… Switzerland estimated a slightly different version of the model (e.g. no group variable; cooperation with foreign counterparts) Germany/Netherlands suggested a modification of the model to deal with endogeneity Canada: could only estimate on manufacturing and weighted regressions and no information on obstacles to innovation Austria: estimate it on CIS3 rather than CIS4 Australia: no information on foreign market; inputed group information and 2005 rather than 2004 New Zealand: again differences in variable definitions

14. …Extensions/variations of the model… The original model only deals with selection and endogeneity of innovation sales eq. In the productivity equation but we also wanted to deal with endogenity of innovation expenditure equation in the innovative sales equation: Ln(innov. sales per employee)=f(Inn. Exp.; Size; Group; process innovation; Cooperation with clients; suppliers; other private and public agents; industry dummies; inverse Mills ratio) Option A: use predicted innovation expenditure rather than actual innovation expenditure and bootstrap standard errors Option B: instrument innovation expenditure in innovative sales equation (options suggested by Germany/Netherlands)

15. …Extensions/variations of the model In current version we use: – “strict” definition of innovation: firms are innovative if innov. Exp.>0 and innov. Sales>0 – log innovative sales per employee Alternative version: – “wide” definition of innovation: firms are innovative if innov. Exp.>0 and innov. sales>=0 – Share of innovative sales: use TOBIT rather than OLS for estimating innovative sales eq. Tested using data for Canada; UK; Denmark

16. RESULTS Simple “non-structural model” – Probit Innovation – OLS : innovation expenditure eq. – OLS: productivity equation “structural” model: – Heckman – Innovative sales eq. – Productivity eq.

17. The results: Innovation equation

18. The results: innovation investment equation

19. Results: the productivity equation

20. Controlling for Selection: innovation equation (Heckman selection eq.)

21. Heckman outcome equation: innovation expenditure eq. Careful: group and foreign market are not marginal effects

22. Innovation Sales eq.

23. Productivity equation

24. Summary of results When significant, coefficients are surprisingly similar Serving a foreign market; being large and being part of a group are generally associated with higher probability of being innovative and financial support and cooperation activity with higher investment in innovation Using a selection model is appropriate for most countries (exc. Austria; Luxembourg and UK) In the innovation sales eq. the elasticity of innovative sales to innovation expenditure is mostly between 0.2 -0.35 In productivity eq. 0.3-0.6

25. Some counterintuitive results Obstacles to innovation have mostly positive coefficients. More innovative firms try harder and therefore find more obstacles? Process innovation is mostly negative in productivity equation. Measurement issues? Adjustment costs? (Possibly future work?)

26. Issues with the model Use of log innovative sales per employee Use of detailed cooperation variables in innovative sales eq. And cooperation in innovation exp. Eq. Use of process in both innovative sales eq. And productivity eq. Perhaps a simpler model? E.g. only cooperation in innovative sales eq. And process in productivity eq.?

27. Lessons learned and steps ahead What have we learned? 18 countries! Interesting results High coordination costs/model more suited for some countries What next? Match CIS with production data both for better productivity measure and longitudinal dimension? Alternative “organisational/coordination” model?