MULTIPLE COMPARISONS WITH THE BEST: BAYESIAN PRECISION MEASURES OF EFFICIENCY RANKINGS

Abstract

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 accord-
ing to the precision of the rankings. A parallel literature uses econometric techniques to
estimate stochastic production frontiers or distance functions, providing at least the pos-
sibility of computing the precision of the resulting efficiency rankings. Recently, Horrace
and Schmidt (2000) have applied sampling theoretic statistical techniques known as mul-
tiple 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 flexibility 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.

Keywords: distance functions, electric utilities, Gibbs sampling, technical efficiency rank-
ings, electric utilities, multiple comparisons with the best.

JEL classification: C11, C32, D24

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