A "politically robust" experimental design for public policy evaluation, with application to the Mexican universal health insurance program

Abstract

We develop an approach to conducting large-scale randomized public policy experiments intended to be more robust to the political interventions that have ruined some or all parts of many similar previous efforts. Our proposed design is insulated from selection bias in some circumstances even if we lose observations; our inferences can still be unbiased even if politics disrupts any two of the three steps in our analytical procedures; and other empirical checks are available to validate the overall design. We illustrate with a design and empirical validation of an evaluation of the Mexican Seguro Popular de Salud (Universal Health Insurance)program we are conducting. Seguro Popular, which is intended to grow to provide medical care, drugs, preventative services, and financial health protection to the 50 million Mexicans without health insurance, is one of the largest health reforms of any country in the last two decades. The evaluation is also large scale, constituting one of the largest policy experiments to date and what may be the largest randomized health policy experiment ever.