1. Using innovation survey data to evaluate R&D policy in Flanders Additionality research Kris Aerts Dirk Czarnitzki K.U.Leuven K.U.Leuven Steunpunt O&O Statistieken Steunpunt O&O StatistiekenBelgium

2. 2 Contents 1. Introduction 2. Literature review 3. Evaluation of the Flemish R&D policy 4. Conclusion

3. 1. Introduction

4. 4 R&D in Europe Barcelona target: 2010: 3% of GDP EU R&D 2010: 3% of GDP EU  R&D 1/3 public 2/3 private funding But: private R&D ~ public good  positive externalities!  subsidies!

5. 5 Subsidies: economic dilemma Crowding out effect? public grants - private investment Empirical analysis relationship between R&D subsidies and R&D activities  treatment effects analysis  Flanders

6. 2. Literature review

7. 7 Literature review ► Blank & Stigler (1957) ► David et al. (2000) ► Klette et al. (2000)  Inconclusive BUT: Selection bias “picking the winner” strategy

8. 8 Selection bias REAL QUESTION: “How much would the recipients have invested if they had not participated in a public policy scheme?”  Matching estimator 1.Probit model on participation dummy 2.Regression of R&D activity (including selection correction: accounting for different propensities of firms to be publicly funded)  Selection model

9. 9 Recent research ► ► Wallsten (2000) – US ► ► Lach (2002) – Israel ► ► Czarnitzki et al. (2001, 2002, 2003) & Hussinger (2003) – Germany ► ► Duguet (2004) – France ► ► González et al. (2004) – Spain   Majority of recent studies: complimentary effects but no complete rejection of crowding out effects ► ► Holemans & Sleuwaegen (1988), Meeusen & Janssens (2001) & Suetens (2002) – R&D-performing firms in Belgium (not controlling for selection bias)

10. 3. Evaluation of the Flemish R&D policy

11. 11 Tackle problem of selection bias  Matching estimator  Selection model

12. 12 Matching estimator “What would a treated firm with given characteristics have done if it had not been treated?” (treatment = receipt of a subsidy for R&D) Variation on Heckman’s selection model  well suited for cross-sectional data  no assumption on functional form or distribution  only controlling for observed heterogeneity among treated and non treated firms

13. 13 Matching estimator (2) Average treatment effect on treated firms: Outcome variable: R&D spending Status: S=1 treated S=0 not treated Potential outcome if treated group would not have been treated Directly observable?

14. 14 Matching estimator (3) Problem: E(Y C |S=1) = ?   Rubin (1977): conditional independence assumption Participation and potential outcome are independent for individuals with the same set of exogenous characteristics XTHUS:

15. 15 Matching estimator (4) Best matching: more than one matching argument BUT: Curse of dimensionality Solution: Propensity score Rosenbaum/Rubin (1983): probit model on receipt of subsidies Lechner (1998): hybrid matching  include additional variables

16. 16 Matching protocol 1. 1. Specify and estimate probit model to obtain propensity scores 2. 2. Restrict sample to common support (remove outliers) 3. 3. Choose one observation from sub sample of treated firms and delete it from that pool 4. 4. Calculate Mahalanobis distance between this firm and all non-subsidized firms in order to find most similar control observation 5. 5. Select observation with minimum distance from remaining sample (selected controls are not deleted from the control group) 6. 6. Repeat steps 3 to 5 for all observations on subsidized firms 7. 7. The average effect on the treated = mean difference of matched samples: 8. 8. Sampling with replacement  ordinary t-statistic on mean differences is biased (neglects appearance of repeated observations)  correct standard errors: Lechner (2001)  estimator for an asymptotic approximation of the standard errors

17. 17 Selection model Effect of the treatment on the treated firms: BUT we need an instrumental variable!!! effect on probability to receive funding but no effect on R&D and innovative activity

18. 18 Dataset ► Flemish companies ► Sources:  Third Community Innovation Survey (CIS III) 1998-2000 774 observations – 179 subsidy recipients  ICAROS database IWT IWT= main company funding institution in Flanders  Patent data from European Patent Office (EPO) data on all patent applications since 1978

19. 19 Variables ► Receipt of subsides: ► Receipt of subsides: dummy variable (local government, national government and EU) ► Outcome variables:  R&D:  R&D: R&D expenditure at firm level in 2000  R&Dint:  R&Dint: R&D expenditure / turnover *100 (very skewed distribution  also logarithmic transformation scales)  Patent/EMP:  Patent/EMP: patent applications in 2000 per employee  D(Patent>0):  D(Patent>0): dummy variable for patenting firms

20. 20 Variables ► Control variables (1):  nprj:  nprj: number of projects applied for in the past Control for previous funding history  lnEmp:  lnEmp: number of employees in 1998 ln smoothens variable  export :  export : exports/turnover Degree of international competition  group:  group: part of group  foreign:  foreign: owned by foreign parent company

21. 21 Variables ► Control variables (2):  PStock/Emp:  PStock/Emp: firm’s patent stock per employee  control for previous (successful) R&D activities  per employee: avoid multicollinearity with firm size  1979 to 1997: past innovation activities Depreciation rate of knowledge: 0,15 e.g. Hall (1990) Patent Stock of firm i in period t Patent applications filed at EPO of firm i in period t

22. 22 Descriptive statistics subsidized firmspotential control group p-value of two- sided t-test on mean equality N 1 = 179N 0 = 596 MeanStd. Dev.MeanStd. Dev. NPRJ0.4531.9810.0760.384p = 0.0122 lnEMP4.3991.4273.7791.239p < 0.0000 GROUP0.6820.4670.5390.499p = 0.0005 FOREIGN0.2960.4580.2550.436p = 0.2936 EXPORT0.5330.3310.3530.340p < 0.0000 PSTOCK/EMP0.7202.4120.1260.936p = 0.0015 R&D1.6234.6020.2991.2113p = 0.0002 R&DINT4.6138.4211.7195.116p < 0.0000 lnR&D-2.0333.107-5.8263.947p < 0.0000 lnR&DINT-0.0922.437-2.7473.071p < 0.0000 D(PATENT>0)0.0720.2600.0170.129p = 0.0062 PATENT/EMP0.0920.5060.0150.139p = 0.0455 Differences: treatment or other characteristics?  Matching technique Observations without common support are dropped => 174 firms

23. 23 Matching procedure Probit estimation on the receipt of subsidies CoefficientStd. err. NPRJ lnEMP0.184***0.047 PSTOCK/EMP0.106***0.038 GROUP0.1810.133 FOREIGN-0.337**0.142 EXPORT0.725***0.171 Constant term-1.926***0.319 Test on joint significance on industry dummies  2 (11) = 16.49 Log-Likelihood-378.1717 Pseudo R-squared0.1002 Number of obs.774 *** (**, *) significance level of 1% (5, 10%) The regression includes 11 industry dummies CoefficientStd. err. 0.292***0.103 0.173*0.058 0.097*0.038 0.1800.136 -0.2660.198 0.693***0.175 -1.926***0.437  2 (11) = 14.10 -370.4076 0.1151 774

24. 24 Matching procedure Propensity score (+ size)  select nearest neighbour Kernel density estimates BEFORE matching AFTER matching propensity score size

25. 25 Matching results subsidized firmspotential control group p-value of two-sided t- test on mean equality N 1 = 174N 0 = 174 MeanStd. Dev.MeanStd. Dev. NPRJ0.2760.6210.2410.729p = 0.635 lnEMP4.3791.4084.3691.302p = 0.943 PSTOCK/EMP0.4621.3190.4771.722p = 0.927 GROUP0.6840.4660.6780.469p = 0.909 FOREIGN0.2930.4560.2240.418p = 0.143 EXPORT0.5250.3300.4990.330p = 0.466 Propensity score 0.3040.1480.2990.138 p = 0.763 R&D1.2923.5630.5181.213p = 0.007 lnR&D-2.1423.073-3.9963.988p = 0.000 R&DINT4.3708.2022.2084.653p = 0.003 lnR&DINT-0.1552.436-1.6242.962p = 0.000 D(PATENT>0)0.1090.3120.0910.289p = 0.639 PATENT/EMP0.2240.9420.1790.712p = 0.647

26. 26 Selection model N(obs)Mean differenceStd. Dev. R&D 1791.758***1.909 R&DInt 1792.129***1.971 lnR&D 1792.424***0.638 lnR&DInt 1791.978***0.258 *** (**, *) significance level of 1% (5, 10%) Instrumental variable NPRJ valid?

27. 4. Conclusion

28. 28 Conclusion ► Matching estimator ► Selection model  No full crowding out

29. 29 Future research ► Time series analysis:  robustness of analysis + lag variables ► Amount of subsidies ► Relationship with output variables  productivity / performance ► Including dataset on all subsidies applied for at IWT (Flemish government)

30. 30 Evaluation of the usefulness of the CIS in this domain  rich dataset, especially when combined with other data sources  no amounts of funding; only dummy  firm-level data versus project-level data  link with output?  link with other variables? (behavioral additionality)

31. 31 Evolution of CIS question in this domain: Evolution of CIS question in this domain: CIS III Did your company receive financial government support for innovative activities between 1998 and 2000?  Belgian governments: OYES: Which institution(s)?........ ONO  The European Union: OYES: FP4 or FP5? O YES O NO ONO

32. 32 Evolution of CIS question in this domain: Evolution of CIS question in this domain: CIS IV Did your company receive any government support for innovative activities between 2002 and 2004?  Local or regional governments O YES O NO  Federal government O YES O NO  The EU O YES: O NO FP5 or FP6? O YES O NO 3xNO: GO to next question Was (part of) this government support granted for activities in which your company was involved in a collaboration agreement? O YES O NO: GO to next question Was a university or public research institution involved in (one of) these collaboration agreement(s)? O YES O NO

33. ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? QUESTIONS?