Simar

Abstract: The explanation of productivity differentials is very important to identify the economic conditions that create inefficiency and to improve managerial performance. In the literature two main approaches have been developed: one-stage approaches and two-stage approaches. Daraio and Simar (2005, J Prod Anal 24(1):93-121) propose a fully nonparametric methodology based on conditional FDH and conditional order-m frontiers without any convexity assumption on the technology. However, convexity has always been assumed in mainstream production theory and general equilibrium. In addition, in many empirical applications, the convexity assumption can be reasonable and sometimes natural. Lead by these considerations, in this paper we propose a unifying approach to introduce external-environmental variables in nonparametric frontier models for convex and nonconvex technologies. Extending earlier contributions by Daraio and Simar (2005, J Prod Anal 24(1):93-121) as well as Cazals et al. (2002, J Econometrics 106:1-25), we introduce a conditional DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is proposed too. These various measures of efficiency provide also indicators of convexity which we illustrate using simulated and real data.

Abstract: This paper explores scale, scope and trade-off effects in scientific research and education. External conditions may dramatically affect the measurement of performance. We apply theDaraio&Simar's (2005) nonparametric methodology to robustlytake into account these factors and decompose the indicators of productivity accordingly. From a preliminary investigation on the Italian system of universities, we find that economies of scale and scope are not significant factors in explaining research and education productivity. We do not find any evidence of the trade-off research vs teaching. About the trade-off academic publications vs industry oriented research, it seems that, initially, collaboration with industry may improve productivity, but beyond a certain level the compliance with industry expectations may be too demanding and deteriorate the publication profile. Robust nonparametric methods in efficiency analysis are shown as useful tools for measuring and explaining the performance of a public research system of universities.