This paper develops a framework for testing discrete complementarities in innovation policy using European data on obstacles to innovation. We propose a discrete test of supermodularity in innovation policy leading to a number of inequality constraints. We apply our test to two types of innovation decisions: to innovate or not, and if so, by how much. We find that industries display a considerable amount of complementarity, with some industries being complementary across all obstacles. We also find that the lack internal human capital (skilled personnel) is complementary to all the other obstacles in almost all industries. In this sense, our results suggest that internal human capital is key for any innovation policy, insofar that it is complementary to all the other factors that might hamper innovation activities. We like to thank Dietmar Harhoff, Bernard Sinclair-Desgagne, and David Soskice for their detailed comments. We also like to thank Susan Athey, Astrid Jung, and Jacques Mairesse, as well as the participants of the WZB-Cirano workshop on "Innovation and Supermodularity", June 15-17, Montreal.

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This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales.

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After presenting the history, the evolution and the content of innovation surveys, we discuss the characteristics of the data they contain and the challenge they pose to the analyst and the econometrician. We document the two uses that have been made of these data: the construction of scoreboards for monitoring innovation and the scholarly analysis of various issue related to innovation. In particular we review the questions examined and the results obtained regarding the determinants, the effects, the complementarities, and the dynamics of innovation. We conclude by suggesting ways to improve the data collection and their econometric analysis. JEL: O30, O50, C35, C81,

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We propose a model where both R&D and ICT investment feed into a system of three innovation output equations (product, process and organizational innovation), which ultimately feeds into a productivity equation. We find that ICT investment and usage are important drivers of innovation in both manufacturing and services. Doing more R&D has a positive effect on product innovation in manufacturing. The strongest productivity effects are derived from organizational innovation. We find positive effects of product and process innovation when combined with an organizational innovation. There is evidence that organizational innovation is complementary to process innovation.