Multiple Comparisons With The Best: Bayesian Precision Measures Of Efficiency Rankings
A large literature exists on measuring the allocative and technical efficiency of a set of firms. A segment of this literature uses data envelopment analysis (DEA), creating relative efficiency rankings that are nonstochastic and thus cannot be evaluated according to the precision of the rankings. A parallel literature uses econometric techniques to estimate stochastic production frontiers or distance functions, providing at least the possibility of computing the precision of the resulting efficiency rankings. Recently, Horrace and Schmidt (2000) have applied sampling theoretic statistical techniques known as multiple comparisons with control (MCC) and multiple comparisons with the best (MCB) to the issue of measuring the precision of efficiency rankings. This paper offers a Bayesian multiple comparison alternative that we argue is simpler to implement, gives the researcher increased exibility over the type of comparison made, and provides greater, and more in-tuitive, information content. We demonstrate this method on technical efficiency rankings of a set of U.S. electric generating firms derived within a distance function framework.
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