Shadow directional distance functions with bads: GMM estimation of optimal directions and efficiencies

Because of its greater flexibility, the directional distance function (DDF) has been employed with increasing frequency to estimate multiple-input and multiple-output production, where inputs and outputs can be good or bad. However, typically researchers make three restrictive assumptions. First, they assume a direction of movement of firm production toward the frontier. Second, they assume that actual quantities of inputs and outputs are allocatively or price efficient. Third, they assume exogeneity of all inputs and all outputs, except for the normalized one. The first contribution of this paper is to include parameters to estimate optimal directions which correspond to the firm’s profit-maximizing (PM) position. The second contribution is to generalize the DDF to a shadow-quantity DDF. This entails adding distortion parameters to each input and output quantity of the DDF, creating shadow quantities. To estimate the shadow quantities and the structural parameters, we form the shadow DDF system, which includes the shadow DDF and all the first-order price equations from the shadow-PM problem. These include prices for bad inputs and bad outputs, where we approximate their missing prices for use in their first-order price equations. The third contribution is that we estimate the shadow DDF system using a Generalized Method of Moments approach, where all variables are potentially endogenous. This approach is simpler than the Bayesian one employed in Atkinson et al. (Estimating efficient production with bad inputs and outputs using latent prices and optimal directions. Working paper, University of Georgia, Athens, 2016), which estimated shadow prices and optimal directions. Using the same data set, both sets of results are qualitatively very similar, although they differ somewhat quantitatively.