FINITE-SAMPLE EVIDENCE ON THE PERFORMANCE OF STOCHASTIC FRONTIERS AND DATA ENVELOPMENT ANALYSIS USING PANEL DATA

In recent years a number of alternative methods have been proposed with which to measure technical efficiency. However, we know little of their comparative performance. In this study we examine the relative strengths of two different methodologies - stochastic frontier models (SF) and data envelopment analysis (DEA) - in estimating firm-specific technical efficiency. To address the limitations of previous studies we utilize Monte Carlo techniques which allow us to control the structure of the underlying technology and the stochastic environment. Most stochastic frontier models have focused on estimating average technical efficiency across all firms. The failure to estimate firm-specific technical efficiency has been regarded as a major limitation of previous stochastic frontier models. To overcome this limitation we estimate firm-specific technical efficiency using panel data. We also examine the performance of stochastic frontier models using panel data for three estimators - maximum likelihood random effects, generalized least squares random effects, and within fixed effects. Our results indicate that for simple underlying technologies the relative performance of the stochastic frontier models vis-a-vis DEA relies on the choice of functional forms. If the employed form is close to the given underlying technology, stochastic frontier models outperform DEA using a number of metrics. As the misspecification of the functional form becomes more serious and as the degree of correlatedness of inefficiency with regressors increases, DEA's appeal becomes more compelling. Our results also indicate that the preferred estimator for the SF model is the within estimator, which addresses two problems common to stochastic frontier models - the possible correlatedness of input levels and technical efficiency and the dependence of stochastic frontier models on distributional assumptions concerning the form of technical inefficiency.