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Improving the productivity of the agricultural sector is part of one of theSustainable Development Goals set by the United Nations. To this end, manyinternational organizations have funded training and technology transferprograms that aim to promote productivity and income growth, fight poverty andenhance food security among smallholder farmers in developing countries.Stochastic production frontier analysis can be a useful tool when evaluatingthe effectiveness of these programs. However, accounting for treatmentendogeneity, often intrinsic to these interventions, only recently has receivedany attention in the stochastic frontier literature. In this work, we extendthe classical maximum likelihood estimation of stochastic production frontiermodels by allowing both the production frontier and inefficiency to depend on apotentially endogenous binary treatment. We use instrumental variables todefine an assignment mechanism for the treatment, and we explicitly model thedensity of the first and second-stage composite error terms. We provideempirical evidence of the importance of controlling for endogeneity in thissetting using farm-level data from a soil conservation program in El Salvador.

Binary Endogenous Treatment in Stochastic Frontier Models with an Application to Soil Conservation in El Salvador