1. Georg Licht, Andreas Fier, Birgit Aschhoff, Heide Löhlein Centre for European Economic Research (ZEW), Mannheim Behavioural Additionality and Public R&D Funding in Germany Results of the OECD/TIP project “Behavioral Additionality” from Germany International Workshop on the Evaluation of Publicly Funded Research 26/27 September 2005 Wissenschaftszentrum Berlin © Paul David

2. OECD / TIP Project Participating countries Australia, Austria, Belgium, Finland, Germany, Ireland, Japan, Norway, Korea, UK, US Topics e.g.:  Acceleration, scale, scope, project additionalities  (Long-term) changes in R&D staff (Number, skills)  Engaging in R&D project involving higher risks  Co-operation in R&D (more complex networks)  Continuation of the funded project: yes/no, scale, length…

3. Outline Changing structures of public R&D grants Input, output and behavioral additionality Assessing the additionality of public R&D grants Public R&D subsidies and Science-Industry- Networks Some Reflections

4. R&D project funding in Germany 1980- 2003 Source: BMBF PROFI - database

5. Excluding funding area Y29000 (improving vocational training) R&D project funding 1980-2003 Number of projects Source: BMBF PROFI - database

6. Rationales  Internalizing spillovers via R&D collaborations  Stimulate technology-transfer  Insider-Outsider problems w.r.t. PP R&D partnerships  Overcoming obstacles to PP R&D partnerships and induce learning effect  Pooling resources and competencies  Using intra-group relation to “monitor” project performance within a R&D consortium ….

7. What is behavioural additionality? “The change in a company’s way of undertaking R&D which can be attributed to policy actions.” (Buisseret et al. 1995) For example, changes in… - Organization of R&D projects - Long-term planning of their research strategy -Management of collaborative research -Reconfiguration of a firm’s R&D network

8. A simplified representation of private R&D O: Output indicator R: R&D input X: Other factors which influence the transformation of inputs to outputs W: Other factors stimulating firm‘s R&D investments

9. Research Questions (a)Is public R&D funding suitable to foster a change of firms’ cooperative behaviour, i.e., does collaborative R&D funding give incentives for firms to test new types of partnerships, in particular multidisciplinary R&D collaborations? (b)Are newly initiated collaborations within a publicly funded R&D project lasting when public funding has ended?

10. The Evaluation Problem „At the heart of … evaluation is a missing variable problem since an individual („a firm“) is either in the programme … or not, but not both. If we could observe the outcome variable for those in the programme had they not participated then there would be no evaluation problem … Thus, constructing the counterfactual is the central issue that the evaluation methods … address.“ Excellent Surveys on micro-econometrics methods in evaluation: Blundell / Costa Dias (2000): Evaluation Methods for Non-Experimental Data, Fiscal Studies, 21, 427-468. Blundell / Costa Dias (2002): Alternative Approaches to Evaluation in Empirical Microeconomics, Institute for Fiscal Studies at UCL, cemmap Working Paper CWP 10/02. (appeared in Portugese Economics Review)

11. Four Main Families of Econometric Approaches Social experiment Random selection of firms into the programmes Natural experiment / Difference in Difference Estimation … finding a „naturally“ occurring comparison group which is not affected by the programme at all Matching Estimators … selecting observable factors that any two firms with the same factors will display no systematic difference in their reaction to the policy programme Instrument Variable Estimators … finding a variable which is correlated with the decision to enter the programme but not correlated with the programme impact

12. A more formal statement of the problem Y: The outcome variable X: Observable characteristics (not affected by the programme) D: 1= in the programme / 0 = out of the programm U: Unobservables Programme impact Programme participation D=1 if IN > 0 D=0 otherwise Programme outcome

13. Treatment effect in case of experimental data Average treatment effect Problems Rarely occurring situation in real world R&D policy Assuming no general equilibrium effects (e.g. spillovers) Firms may randomly drop out of the programme Participation in competing programmes Programme agencies may pass other information to the randomly unselected than to randomly selected firms

14. Treatment effect for non-experimental data Average treatment effect Hence, selection of participation on unobservables induce bias unless in the rare event that the two last term on RHS exactly cancel out The solution to this problem depends Available data Underlying model (linking funding to input, output and behavior) Parameters of interest

15. Matching Estimators Solution: Conditional independence between outcomes and programme participation (CIA) Common Support Assumption All participates have a counterpart in the groups of non-participants Rosenbaum / Rubin (1983): CIA remains valid if we use Instead of As a consequence: Average treatment effect on the treated

16. Virtues and Drawbacks No need to specify a parametric relation for the outcome equation BUT Need of common support Strong requirements on the amount and quality of data Problem of common support increases with the amount of information that is available (trade-off)

17. Steps of a matching approach 1.Reduce dimensionality by finding P(X) to characterise participants and non-participants 2.Establish control group / Finding control observations a)Split sample in treated {(1)} and non-treated firms {(0)} b)Randomly select a firm from {(1)} c)Find firm j from {(0)} which is closest to i in terms of P(X) d)Select firm j as “twin” of i e)Store j and i in data set f)[ Put j back in basket {(0)} ] g)Repeat procedure from b) as long as there are firms in {(1)} 3.Estimating the average treatment effect by:

18. Research Questions (a)Is public R&D funding suitable to foster a change of firms’ cooperative behaviour, i.e., does collaborative R&D funding give incentives for firms to test new types of partnerships, in particular multidisciplinary R&D collaborations? (b)Are newly initiated collaborations within a publicly funded R&D project lasting when public funding has ended?

19. Data Direct R&D project funding data from the database PROFI Mannheim Innovation Panel (=Community Innovation Survey ) for 2001 and 2004 Patent application database (German Patent Office) Telephone interviews with randomly selected programme participants

20. Step I: Estimating probability of public support + establishing a control group

21. Step II: Comparing Structure of R&D partnerships Business-only co-operation Science-only co-operation Business-science co-operation

22. Step III: Permanent impact of partnership structure? Estimating the probability whether partnerships are continued after the end of the publicly (co-)financed project

23. What drives participation in public R&D programmes? Source: ZEW Mannheim Innovationpanel 2002

24. There are good reasons to believe that public R&D subsidies have positive social returns by inducing additional R&D expenditures (i.e. positive input & output additionality) BUT …… Empirical evidence on behavioral additionality is hard to find at least when applying econometric standards A Tentative Summary

25. Finally… the end Georg Licht ZEW L7,1 69181 Mannheim Email: Licht@zew.deLicht@zew.de Phone: +49 621 1235 197

26. Literature  Surveys: David/Hall/Toole, David/Hall, Hall/VanReenen; Klette/Moen/Griliches Research Policy 29 (2000)  Recent Papers (Micro-level):  Wallsten (2000) RJE; Lach (2002) JIE; Busom (2002) EINT; Duguet (2002) REP; Blanes/Busom (2002) WP Barcelona; Gonzalez/Jaumandreu/Pazo (2005) RJ; Gonzalez/Paso (2005); Kaiser (2004); Czarnitzki/Hanel/Rosa (2004) ZEW WP  for Germany: Fier (2002); Czarnitzki/Fier (2002,2003) ZEW WP; Almus/Czarnitzki (2003) JBES; Hussinger (2003) ZEW WP; Hujer/Radic (2005) ZEW WP The majority of papers at the micro-level suggests no crowding-out or even crowding-in effects of public R&D subsidies on privately financed R&D investments