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

Many industrial processes are difficult to control because product quality cannot be measured economically on line. One solution to this problem is t o use secondary measurements in conjunction with a mathematical model of the process to estimate product quality. This paper presents a method for designing a static estimator which predicts product quality from a linear combination of process input and output measurements. The design method includes procedures for selecting a subset of the available output measurements so as to obtain a n estimator which is relatively insensitive to modeling errors and measurement noise. Application of the estimator to a simulated multicomponent distillation column shows that the composition control achieved with an estimator based on temperature, reflux, and steam flow measurements is comparable to that achieved with instantaneous composition measurements. The composition control using the estimator is far superior to the composition control achieved by attempting to maintain a constant temperature on any single stage of the column.The control of many industrial processes is complicated by problems associated with the on-line measurement of the product quality. Occasionally, the required measurement technology simply does not exist. More frequently, the needed instrumentation is either prohibitively expensive and/or the measurement lags and sampling delays associated with the measurement are so large as to make it impossible to design an effective feedback control system. In such cases secondary measurements can be used to infer the effect of process disturbances on the product quality. This is relatively easily done when there is a thermodynamic or physical relationship between the available measurements and the unmeasurable product quality. For example, in binary distillation one can use either the temperature and pressure of a stage, or the output of a vapor pressure bulb inserted into the liquid on the stage, to estimate the composition of the liquid on the stage. When there is no direct physical relationship between the available measurements and the product quality, then the estimation of product quality from secondary measurements requires some knowledge of how the process operates. Feed-forward control systems use process inputoutput relationships and measurements of major process disturbances to estimate the effect of the measured disturbance on the product quality. The controller then adjusts the control effort so as to maintain the estimated product quality at the desired level. Usually, the estimation and control steps are comhined and are not separately distinguishable.The control strategy proposed in this paper is to use selected measurements of both process inputs and outputs to estimate the effect of measured and unmeasured disturbances on the product quality and to then use a standard control system to adjust the control effort so as to maintain the product quality at the desired level. This strategy reduces approximately to that of a feed-forward control system w...

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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

No abstract