georg licht, andreas fier, birgit aschhoff, heide löhlein centre for european economic research...
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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
OECD / TIP Project
Participating countriesAustralia, 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…
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
R&D project funding in Germany 1980-2003
0
500
1000
1500
2000
2500
3000
3500
4000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
in M
ill. E
uro
Network Projects: Business - Science
Network Projects: Science
Network Projects: Business
Network Projects: Others
Source: BMBF PROFI - database
0
2000
4000
6000
8000
10000
12000
14000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Nu
mb
er o
f P
roje
cts
Individually Conducted Projects
Network Projects: Business - Science
Network Projects: Science
Network Projects: Business
Network Projects: Others
Excluding funding area Y29000 (improving vocational training)
R&D project funding 1980-2003Number of projects
Source: BMBF PROFI - database
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….
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
A simplified representation of private R&D
Pr P
Pr P
, ,
( , )
ivate ublic
ivate ublic
O f R R X
R g R W
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
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?
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)
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
A more formal statement of the problem
( )i i iIN h W v
1 1 1
0 0 0
, if 1
, if 0
i i i i
i i i i
Y g X u D
Y g X u D
Y: The outcome variableX: Observable characteristics (not
affected by the programme)D: 1= in the programme /
0 = out of the programmU: Unobservables
Programme impact
Programme participation
D=1 if IN > 0D=0 otherwise
1 0 1 0 1 0[ ] [ ]i i i i i i iY Y g X g X u u
Programme outcome
Treatment effect in case of experimental data
Average treatment effect
1 0ate Y Y
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
Treatment effect for non-experimental data
Average treatment effect
1 0 | 1) | 0)( ) ( (ate i i i iD DE Y Y E u E u
Hence, selection of participation on unobservables induce bias unlessin 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
Matching Estimators
Solution: Conditional independence between outcomes and programme participation (CIA)
1 0, 1|Y Y D X
Common Support AssumptionAll participates have a counterpart in the groups of non-participants
1 0, 1| ( )Y Y D P X
Rosenbaum / Rubin (1983): CIA remains valid if we use
1 0, 1|Y Y D X Instead of
As a consequence: Average treatment effect on the treated
1 01
1
1( )tte i i
i
Y YN
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)
Steps of a matching approach
1. Reduce dimensionality by finding P(X) to characterise participants and non-participants
2. Establish control group / Finding control observationsa) 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 ie) Store j and i in data setf) [ 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:
1
1 0
11
1 ˆN
A T T i ii
Y YN
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?
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
Step I: Estimating probability of public support + establishing a control group
Step II: Comparing Structure of R&D partnerships
Not publicly funded firms
36%
19%
45%
Publicly funded firms
13%
21%
66%
Business-only co-operation
Science-only co-operationBusiness-science co-operation
Step III: Permanent impact of partnership structure?Estimating the probability whether partnerships are continued after the end of the publicly (co-)financed project
What drives participation in public R&D programmes?
0% 10% 20% 30% 40%
Other firms
Research Institutions
Financial service
Consultants
Professional assocations
Technology transfer offices
Trigger forparticipation
Initial information
Source: ZEW Mannheim Innovationpanel 2002
There are good reasons to believe thatpublic 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 findat least when applying econometric standards
A Tentative Summary
Literature Surveys:
David/Hall/Toole, David/Hall, Hall/VanReenen; Klette/Moen/GrilichesResearch 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