An Econometric Approach To Estimating Support Prices And Measures Of Productivity Change In Public Hospitals

Abstract

In industry sectors where market prices are unavailable it is common to represent multiple-input multiple-output production technologies using distance functions. Econometric estimation of such functions is complicated by the fact that more than one variable in the function may be endogenous. In such cases, maximum likelihood estimation can lead to biased and inconsistent estimates of the model parameters and associated measures of firm performance. We solve the problem by using linear programming to construct a quantity index. The distance function is then written in the form of a conventional stochastic frontier model where the explanatory variables are unambiguously exogenous. We use this approach to estimate productivity indexes and support (or shadow) prices for a sample of Australian public hospitals. We decompose the productivity index into several measures of environmental change and efficiency change. We find that the productivity effects of improvements in input-oriented technical efficiency have been largely offset by the effects of deteriorations in the production environment over time.