The aim of the present paper is to examine the observed differences in Students’ test performance across public and private‐voucher schools in Spain. For this purpose, we explicitly consider that education is a multi‐input multi‐output production process subject to inefficient behaviors, which can be identified at student level using a parametric stochastic distance function approach. The empirical application of this model, based on Spanish data from the Programme for International Student Assessment implemented by the Organization for Economic Co‐operation and Development in 2003, allows us to identify different aspects of the underlying educational technology. Among other things, the results provide insights into how student background, peer group, school characteristics and personal circumstances interact with educational outputs. Moreover, our findings suggest that, once educational inputs and potential bias due to school choice endogeneity are taken into account, no further unexplained difference remains between students’ efficiency levels across public and private‐voucher schools.

The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications in efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear production function is carried out. The results suggest that ANNs are a promising alternative to traditional approaches, econometric models and non-parametric methods such as data envelopment analysis, to fit production functions and measure efficiency under non-linear contexts.