We extend the emerging literature on spatial frontier methods in a number of respects. One contribution includes accounting for unobserved heterogeneity. This involves developing a random effects spatial autoregressive stochastic frontier model which we generalize to a common correlated effects specification to account for correlation between the regressors and the unit specific effects. Another contribution is the introduction of the concept of a spatial efficiency multiplier to show that the efficiency frontiers from the structural and reduced forms of a spatial frontier model differ. To demonstrate various features of the estimators we develop we carry out a Monte Carlo simulation analysis and provide an empirical application. The application is to a state level cost frontier for U.S. agriculture which is a popular case in the efficiency literature and is thus well-suited to highlighting the features of the estimators we propose.
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