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Using innovation survey data to evaluate R&D policy in Flanders Additionality research

Using innovation survey data to evaluate R&D policy in Flanders Additionality research. Kris Aerts Dirk Czarnitzki K.U.Leuven Steunpunt O&O Statistieken Belgium. Contents. Introduction Literature review Evaluation of the Flemish R&D policy Conclusion.

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Using innovation survey data to evaluate R&D policy in Flanders Additionality research

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

  2. Contents • Introduction • Literature review • Evaluation of the Flemish R&D policy • Conclusion

  3. 1. Introduction

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

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

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

  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. Tackle problem of selection bias • Matching estimator • Selection model

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

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

  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. Matching protocol • Specify and estimate probit model to obtain propensity scores • Restrict sample to common support (remove outliers) • Choose one observation from sub sample of treated firms and delete it from that pool • Calculate Mahalanobis distance between this firm and all non-subsidized firms in order to find most similar control observation • Select observation with minimum distance from remaining sample (selected controls are not deleted from the control group) • Repeat steps 3 to 5 for all observations on subsidized firms • The average effect on the treated = mean difference of matched samples: • 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. 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. 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. Variables • Receipt of subsides:dummy variable (local government, national government and EU) • Outcome variables: • R&D:R&D expenditure at firm level in 2000 • R&Dint:R&D expenditure / turnover *100 (very skewed distribution  also logarithmic transformation scales) • Patent/EMP: patent applications in 2000 per employee • D(Patent>0): dummy variable for patenting firms

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

  21. Depreciation rate of knowledge: 0,15 e.g. Hall (1990) Patent applications filed at EPO of firm i in period t Patent Stock of firm i in period t Variables • Control variables (2): • 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

  22. Descriptive statistics Differences: treatment or other characteristics?  Matching technique Observations without common support are dropped => 174 firms

  23. Matching procedure Probit estimation on the receipt of subsidies *** (**, *) significance level of 1% (5, 10%) The regression includes 11 industry dummies

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

  25. Matching results

  26. Selection model *** (**, *) significance level of 1% (5, 10%) Instrumental variable NPRJ valid?

  27. 4. Conclusion

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

  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. 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. Evolution of CIS question in this domain: CIS III

  32. Evolution of CIS question in this domain: CIS IV

  33. QUESTIONS?

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