Recently, the European Commission (EC) advocated the use of fiscal incentives to stimulate private sector R&D and innovation activity. Germany and Finland are one of the few countries in Europe without a fiscal incentives scheme in place. However, both countries run a large variety of R&D promotion schemes, maintain a large public research sector and foster technological co-operation between private and public sector R&D. Even more, both countries abolished fiscal incentives schemes some years ago in favour of schemes based on direct project based grants for R&D projects run by private firms. The main argument for not having a fiscal incentive schemes was that fiscal incentives are characterised by both a low degree of additionality and productivity. That said, it seems appropriate to look at R&D and innovation for these two countries in more detail.

This study focuses on variables of research output such as patent activities, which are suitable to measure research productivity. The use of firm and funding data (CIS data, BMBF-funding data, TEKES-funding data) offers variables to identify productivity effects which may occur from public funding in a large economy like Germany as well as in a small economy, like Finland.

More specifically, we will approach three objectives:

• The first objective is to present a brief literature review on private business R&D, public funding and productivity. The overview should give us a better insight in the context of certain indicators, which has implications on the empirical analysis. Research will focus on evaluation studies available for Finland and Germany.
• The second objective is to identify micro-level indicators which explain R&D productivity using a matched sample consisting of patent data, CIS data and R&D support data. R&D productivity will be measure in terms of the patent to R&D ratio as well as more usual productivity measures. Variables in CIS measure R&D and innovation inputs at the firm level as well as links between public and private R&D. All indicators concerning the commercialisation of R&D are used to estimate the R&D productivity. The estimates and interpretation will explicitly take into account the level of support (size of the R&D grant) which is not possible when CIS-type data is used. The interpretation will take into account the performance variables, productivity variables and input variables which are measured at the firm level, while public support data is measured on the project level and hence must be aggregated to firm level data.
• The third objective is to sum up the empirical results from Germany and Finland. We will discuss evidence of a direct relationship between the countries R&D, public sector intervention and private innovation outcome/productivity. We will discuss the result especially with respect to the ongoing innovation policy discussion in the EU (e.g. in context with the Barcelona 3% objective and the Lisbon strategy).