Behavioural Additionality and
Public R&D Funding in Germany
Results of the OECD/TIP project “Behavioral Additionality” from Germany
Georg Licht, Andreas Fier, Birgit Aschhoff, Heide
Löhlein
Centre for European Economic Research (ZEW),
Mannheim
International Workshop on the Evaluation of Publicly Funded Research
26/27 September 2005
Wissenschaftszentrum Berlin
© Paul David
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…
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-IndustryNetworks

Some Reflections
R&D project funding in Germany 19802003
4000
Network Projects: Business - Science
3500
Network Projects: Science
Network Projects: Business
3000
2500
2000
1500
1000
500
20
02
20
00
19
98
19
96
19
94
19
92
19
90
19
88
19
86
19
84
19
82
0
19
80
in Mill. Euro
Network Projects: Others
Source: BMBF PROFI - database
R&D project funding 1980-2003
Number of projects
Source: BMBF PROFI - database
Excluding funding area Y29000 (improving vocational training)
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
O  f R
R
Pr ivate
Pr ivate
 g (R
,R
P ublic
P ublic
,X
,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
Programme impact
Yi1  g 1  X i   ui1 , if Di  1
Yi 0  g 0  X i   ui0 , if Di  0
Programme participation
INi  h(Wi )  vi
Y: The outcome variable
X: Observable characteristics (not
affected by the programme)
D: 1= in the programme /
0 = out of the programm
U: Unobservables
D=1 if IN > 0
D=0 otherwise
Programme outcome
i  Yi1  Yi 0  [ g1  X i   g 0  X i ]  [ui1  ui0 ]
Treatment effect in case of experimental
data
Average treatment effect
 ate  Y 1  Y 0
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
E ( ate )  Y 1  Y 0  E (ui | Di  1)  E (ui | Di  0)
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
Matching Estimators
Solution: Conditional independence between
outcomes and programme participation (CIA)
Y 1 , Y 0  D  1| X
Common Support Assumption
All participates have a counterpart in the groups of non-participants
Rosenbaum / Rubin (1983): CIA remains valid if we use
Y 1 , Y 0  D  1| P( X )
Instead of
Y 1 , Y 0  D  1| X
As a consequence: Average treatment effect on the treated
1
 tte  1  (Yi1  Yi 0 )
N i1
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 observations
a)
b)
c)
d)
e)
f)
g)
Split sample in treated {(1)} and non-treated firms {(0)}
Randomly select a firm from {(1)}
Find firm j from {(0)} which is closest to i in terms of P(X)
Select firm j as “twin” of i
Store j and i in data set
[ Put j back in basket {(0)} ]
Repeat procedure from b) as long as there are firms in {(1)}
3. Estimating the average treatment effect by:
 ATT 
N1

1
1
0
ˆ
Y

Y
 i i
N 1 i 1

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:from
Estimating
probability of public support
Results
Probit
+ establishing a control group
Estimation method:
probit with heteroscedasticity
Firm size
Probability of
public (co-)
funding
Firm is foreign controlled
+
+
+
+
-
Firm is located in Eastern Germany
+
Credit-rating index
+
Permanent R&D
Stock of patents (t-1)
Age of firm
Export activity (yes/no)
Firm is part of group
Industry effects
included
Step II: Comparing Structure of R&D partnerships
Not publicly funded firms
Publicly funded firms
13%
36%
45%
21%
66%
19%
Business-only co-operation
Science-only co-operation
Business-science co-operation
Step III: Permanent impact of partnership
structure?
Results
from Probit the probability whether partnerships are
Estimating
continued after the end of the publicly (co-)financed project
Estimation method: bivariate probit
Past R&D collaboration with science
Collaboration
with Science
Collaboration
with other firms
-
Past R&D collaboration with other firms
+
+
0
0
+
0
0
0
+
0
0
Industry effects
included
included
Technology effects
included
included
Size of project
Project size extended due to public R&D grant
Acceleration of project due to public funding
Firm size
Region (Eastern Germany)
What drives participation in public R&D
programmes?
Technology transfer offices
Professional assocations
Trigger for
participation
Initial information
Consultants
Financial service
Research Institutions
Other firms
0%
10%
20%
30%
40%
Source: ZEW Mannheim Innovationpanel 2002
A Tentative Summary
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
Finally
… the end
Georg Licht
ZEW
L7,1
69181 Mannheim
Email: [email protected]
Phone: +49 621 1235 197
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