Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador
研究了当生产前沿和效率都受二元内生处理影响时，如何用最大似然法估计随机前沿模型，并用萨尔瓦多土壤保持项目数据验证方法有效性。
Summary Numerous programs exist to promote productivity, alleviate poverty, and enhance food security in developing countries. Stochastic frontier analysis can be helpful to assess their effectiveness. However, challenges can arise when accounting for treatment endogeneity, often intrinsic to these interventions. We study maximum likelihood estimation of stochastic frontier models when both the frontier and inefficiency depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism and explicitly model the density of the first and second‐stage error terms. We provide empirical evidence using data from a soil conservation program in El Salvador.