Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador

Improving the productivity of the agricultural sector is part of one of the Sustainable Development Goals set by the United Nations. To this end, many international organizations have funded training and technology transfer programs that aim to promote productivity and income growth, fight poverty and enhance food security among small farmers in developing countries. Stochastic production frontier analysis can be a useful tool when evaluating the effectiveness of these programs. However, accounting for endogenous selection into treatment, often intrinsic to these interventions, only recently received any attention in the stochastic frontier literature. In this work, we extend the classical maximum likelihood estimation of stochastic production frontier models by allowing both the production frontier and inefficiency to depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism for the treatment, and we explicitly model the density of the first and second-stage composite error terms. We provide empirical evidence of the importance of controlling for endogeneity in this setting using farm-level data on a soil conservation program in El Salvador