On the Implications of Essential Heterogeneity for Estimating Causal Impacts Using Social Experiments

Abstract The standard model of essential heterogeneity, whereby program take up depends on unobserved costs and benefits of take up, is generalized to allow the source of latent heterogeneity to influence counterfactual outcomes. The standard instrumental variables (IV) estimator is shown to still be preferable to the naïve, ordinary least squares (OLS), estimator for mean impact on the treated. However, under certain conditions, the IV estimate of the overall mean impact will be even more biased than OLS. Examples are given for stylized training, insurance and microcredit schemes.