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Entrepreneurship education ranks highly on policy agendas in Europe and the US, but little research is available to assess its impacts. In this context it is of primary importance to understand whether entrepreneurship education raises intentions to be entrepreneurial generally or whether it helps students determine how well suited they are for entrepreneurship. We develop a theoretical model of Bayesian learning in which entrepreneurship education generates signals which help students to evaluate their own aptitude for entrepreneurial tasks. We derive predictions from the model and test them using data from a compulsory entrepreneurship course at a German university. Using survey responses from 189 students ex ante and ex post, we find that entrepreneurial propensity declined somewhat in spite of generally good evaluations of the class. Our tests of Bayesian updating provide support for the notion that students receive valuable signals and learn about their own type in the entrepreneurship course.JEL Classification: D83, J24, L26, M13

We examine empirically the role of lending relationships in determining the costs and collateral requirements for external funds. The data originate from a recently concluded survey of small and medium-sized German ®rms. In our descriptive analysis, we explore the borrowing patterns and the concentration of borrowing from ®nancial institutions. Using data on L/C interest rates, collateral requirements, and the ®rm's use of fast payment discounts we ®nd that relationship variables may have some bearing on the price of external funds, but much more so on loan collateralization and availability. Firms in ®nancial distress face comparatively high L/C interest rates and reduced credit availability. Ó 1998 Elsevier Science B.V. All rights reserved.JEL classification: G21; D45

Empirical studies of innovation have found that end users frequently develop important product and process innovations. Defying conventional wisdom on the negative effects of uncompensated spillovers, innovative users also often openly reveal their innovations to competing users and to manufacturers. Rival users are thus in a position to reproduce the innovation in-house and benefit from using it, and manufacturers are in a position to refine the innovation and sell it to all users, including competitors of the user revealing its innovation. In this paper we explore the incentives that users might have to freely reveal their proprietary innovations. We then develop a game-theoretic model to explore the effect of these incentives on users' decisions to reveal or hide their proprietary information. We find that, under realistic parameter constellations, free revealing pays. We conclude by discussing some implications of our findings.

This paper tests for the importance of cash flow on investment in fixed capital and R&D using firm-level panel data in two countries between 1985 and 1994. For German firms, cash flow is not informative in simple econometric models of fixed investment or R&D. In identical specifications for British firms, cash flow is informative about investment, although not about the level of R&D spending conditional on the R&D participation decision. In the UK, we also find that investment is less sensitive to cash flow for R&D-performing firms, and that cash flow predicts whether firms perform R&D or not. We confirm that these differences do not simply reflect a greater role for current cash flow in forecasting future sales. These results suggest that financial constraints are more significant in Britain, that they affect the decision to engage in R&D rather than the level of R&D spending by participants, and that consequently the British firms that do engage in R&D are a self-selected group where financing constraints tend to be less binding.

This paper employs data from an extensive European survey to produce one of the first systematic assessments of the private economic value of patents. The estimated mean of our patent value distribution is higher than 3 million euros, the median is about one-tenth of it, and the mode is around a few thousand euros. This is in line with previous findings about the skewed distribution of patent values. Our measure is significantly correlated with the number of patent citations, references, claims, and countries in which the patent is applied. Citations explain value as much as the other three indicators combined, and the right tail of citations is correlated with the right tail of our value measure. Yet, the four indicators only explain 2.7% of the variance of patent value. Thus, while the use of these indicators as proxies for value, particularly citations, may be justified, predictions based on these indicators carry significant noise. After using country, technology, and patent class fixed effects, we only explain 11.3% of the variation in patent value. The ‘measure of our ignorance’ about the determinants of patent value is then still sizable, which calls for additional research to fill the gap

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