Almost random: Evaluating a large-scale randomized nutrition program in the presence of crossover

Abstract

Large-scale randomized interventions have the potential to uncover the causal effect of programs applying to a large population, thereby improving on the insights gained from currently dominant smaller randomized studies. However, the external validity gained through larger interventions typically risks deviation from the randomization protocol. This paper investigates the impact of the Nutrition Enhancement Program, which aims to improve child nutrition in Senegal. The analysis deals with deviation from the planned treatment and suggests approaches for combining ex-post adjustments such as propensity score matching with the randomized treatment plan. The authors do not detect a strong overall program impact on the outcome measure of weight-for-age based on planned treatment status, but do find an impact on the youngest children. Moreover, the project impact is clearer when the analysis considers treatment crossover using alternative estimators of two-stage least-squares and propensity score matching. The findings underscore the importance of addressing the shortcomings of large-scale randomization interventions in a systematic manner to guide further implementation of such projects, as well as to expose the true causal effect of such programs.