We present a panel stochastic frontier model that handles the endogeneity problem. This model can treat the endogeneity of both frontier and inefficiency variables. We apply our method to examine the technical efficiency of Japanese cotton spinning industry. Our results indicate that market concentration is endogenous, and when its endogeneity is properly handled, it has a larger negative impact on the technical efficiency of cotton spinning plants. We find that the exogenous model substantially overestimates efficiency in concentrated markets.

In this article, I introduce sfkk, a new command for fitting endogenous stochastic frontier models. sfkk provides estimators for the parameters of a linear model with a disturbance assumed to be a mixture of two components: a measure of inefficiency that is strictly nonnegative and a two-sided error term from a symmetric distribution. sfkk can handle endogenous variables in the frontier or the inefficiency, and the sfkk estimates outperform the standard frontier estimates that ignore endogeneity.

Some education policy studies suggest that consolidation of public school districts saves resources. However, endogeneity in cost models would result in incorrect estimates of the effects of consolidation. We use a new stochastic frontier methodology to examine district expenditures while handling endogeneity. Using the data from California, we find that the effects of student achievement and education market concentration on expenditure per pupil are substantially larger when endogeneity is handled. Our findings are robust to concerns such as instrumental variable adequacy and spatial interactions. Our consolidation simulations indicate that failure to address endogeneity can result in unrealistic expectations of savings.

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