The Evaluation of 'Behavioural Additionality' - IWT

CHAPTER 2 > Measuring Additionality of R&D Subsidies with Surveys:
Towards an Evaluation Methodology for IWT-Flanders
>
the existing R&D survey and the Community
Innovation Survey, which do measure input
and output of the R&D and innovation
process, but not at the project or programme
level. The latter is more interesting for an
impact analysis since R&D grants are attributed
at that level. In addition, the survey
data can be used to enrich the econometric
studies which are as of today limited to a small
number of company level variables.
The paper unfolds along the following lines.
First, we discuss the concept of additionality
and in particular behavioural additionality.
Second, we describe the research population
that was used to investigate the presence of
the different additionality types and to
develop an appropriate questionnaire. This
population was segmented into four groups:
the large R&D intensive companies, the large
companies without permanent R&D activities,
the high tech start-ups and the SMEs without
permanent R&D activities. For each of these
segments, we discuss the instrument (questionnaire),
the research design (sampling, data
collection method) and the lessons, which we
have learned from the first exploratory
research with this approach. Finally, we conclude
with an analysis of the cases that were
included in this exploratory research and formulate
concluding suggestions concerning a
full-scale impact evaluation.
2. TOWARDS A DEFINITION
OF ADDITIONALITY
We refer to the paper published by Luke
Georghiou in this chapter 1 for a more indepth
discussion of the additionality concept.
Here, we provide a summary of the
issues raised in his paper that are key for our
approach. Measuring additionality has been
a challenge for many R&D subsidy agencies.
In simple terms the range of additionality
perspectives is:
• Input additionality: the proportion of
inputs which would not have been allocated
without public support. Strictly it is
measured as the amount spent by the
firm to R&D in response to the subsidy
provided.
• Output additionality: the proportion of
outputs which would not have been
achieved without public support. This of
course begs the question of what is an
output – are we dealing with the direct
results in terms of papers and patents,
or with final effects such as sales of new
products or applications of processes
and services. The latter effects are sometimes
referred to as outcome additionality.
For simplicity, we group everything
here under the umbrella of output additionality.
• Behavioural additionality: “the difference
in firm behaviour resulting from the intervention,
that is not likely to be
generated by alternative market
resources”. The assumption is that the
behaviour is changed in a desirable direction,
though an evaluation should also be
sensitive to perverse effects, for example
encouraging firms to take risks that they
cannot afford.
Because of lack of data, behavioural additionality
has generally been ignored by
econometric studies on the effects of R&D
support. These studies focus on input additionality,
where estimates are made of additional
R&D expenditure, or output additionality,
whereby firm performance is
compared between recipients and nonrecipients
of public support.
Davenport and Grimes (1999) in assessing
the effects of company support in New
Zealand found that the behavioural additionality
concept provided an explanation
for their findings. Managers and policy
administrators, they argue, can exploit the
occurrence of behavioural additionality to
maximize the impact of a research policy, on
the basis that modified behaviour is likely to
strengthen a policy's latent ability to influence
the creation of output additionality.
They conclude that managers and policymakers
should identify those interventions
that lead to sustained improvements in
managerial practice and in firm competitiveness.
The aim then should be to manage
their diffusion within firms and throughout
industries.
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