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The development of MUAC-for-age reference data recommended by a WHO Expert Committee

Existing approaches to program evaluation are designed to examine the average treatment effect. In practice, however, we are often interested not just in the mean impact. Indeed, as in the case of child nutrition we are particularly interested in program impacts on the worst off children and locations and in the largest declines in nutritional status. This article has proposed a new method to evaluate program impacts across the entire distribution of outcomes and changes in outcomes. The method does not require experimental data as it applies stochastic dominance estimation to differences-in-differences across subgroups and time. Our empirical results highlight the practical added value of this method. Standard difference-indifference regressions find no statistically significant average treatment effect of additional public expenditures through ALMRP II on child malnutrition levels in Kenya. Our new stochastic dominance difference-in-difference estimation allows us to look beyond the mean impact and tease out program effects that differ across the distribution of nutrition changes. For all MUAC Z-scores summary statistics intervention sublocations had fewer negative changes over time than the control sublocations. While the data do not enable us to identify causality cleanly the results suggest that additional public expenditures under the ALRMP II project were associated with less deterioration in children’s nutritional status among the worst-off children, thus, effectively functioning as a nutritional safety net.