Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities
We estimate an input distance function for U.S. electric utilities under the assumption that non-negative variables associated with technical inefficiency are timeinvariant. We use Bayesian methodology to impose curvature restrictions implied by microeconomic theory and obtain exact finite-sample results for nonlinear functions of the parameters (eg. technical efficiency scores). We find that Bayesian point estimates of elasticities are more plausible than maximum likelihood estimates, technical efficiency scores from a random effects specification are higher than those obtained from a fixed effects model, and there is evidence of increasing returns to scale in the industry.
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