The Impact of Government Procurement Composition on Private R&D Activities
Viktor Slavtchev, Simon Wiederhold
Abstract
This paper addresses the question of whether government procurement can work as a de facto innovation policy tool. We develop an endogenous growth model with quality-improving in-novation that incorporates industries with heterogeneous innovation sizes. Government demand in high-tech industries increases the market size in these industries and, with it, the incentives for private firms to invest in R&D. At the economy-wide level, the additional R&D induced in high-tech industries outweighs the R&D foregone in all remaining industries. The implications of the model are empirically tested using a unique data set that includes federal procurement in U.S. states. We find evidence that a shift in the composition of government purchases toward high-tech industries indeed stimulates privately funded company R&D.
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Evaluation of Further Training Programmes with an Optimal Matching Algorithm
Eva Reinowski, Birgit Schultz, Jürgen Wiemers
IWH Discussion Papers,
Nr. 188,
2004
Abstract
This study evaluates the effects of further training on the individual unemployment duration of different groups of persons representing individual characteristics and some aspects of the economic environment. The Micro Census Saxony enables us to include additional information about a person's employment history to eliminate the bias resulting from unobservable characteristics and to avoid Ashenfelter's Dip. In order to solve the sample selection problem we employ an optimal full matching assignment, the Hungarian algorithm. The impact of participation in further training is evaluated by comparing the unemployment duration between participants and non-participants using the Kaplan-Meier-estimator. Overall, we find empirical evidence that participation in further training programmes results in even longer unemployment duration.
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