This paper examines the efficiency of electric power generation plants in the United States. A 1996 data set from the Utility Data Institute and county-level wage data from the Bureau of Labor statistics provide the information needed to construct measures of cost, output, and input prices for 78 steam plants using natural gas as the primary fuel. This paper uses a Bayesian stochastic frontier model that imposes concavity and monotonicity restrictions implied by microeconomic theory to measure efficiency, price elasticities, and returns to scale of these plants. Results indicate that plants on average could reduce costs by up to 13% by eliminating production inefficiency. Results also indicate that most plants operate at increasing returns to scale, suggesting further cost savings could be achieved through increasing output.

Assessing the impact of new technologies on health care costs is an important research area. This paper evaluates two technologies used to treat coronary artery disease. We estimate two separate stochastic frontier models—one for balloon angioplasty patients and one for cardiac bypass surgery patient—using data taken from detailed chart and cost files of a large urban hospital. Cost estimates for the two technologies are purged of inefficiency so that forecasts of the cost consequences of technology shift can be based on ‘best-practice’ production techniques. Learning behavior, physician effects, and patient clinical characteristics are also taken into account. We find that there are potential cost savings associated with making angioplasty a more perfect substitute for bypass surgery, as well as current inefficiency in production.