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World Development

The program evaluation literature has evolved separately from the stochastic dominance literature.
Reviews of the state-of-the-art in program evaluation (Todd 2008) and best practice guides (Baker 2000)
do not contain any reference to stochastic dominance. To date Verme (2010) is the only study that has
started to show how stochastic dominance techniques can be used for program evaluation. He uses
simulated income data to show that a program can have no average treatment effect while impacting
the rich and the poor quite differently. Drawing on the analogies between poverty and stochastic
dominance orderings (Foster and Shorrocks 1988) he proposes a simple method for program evaluation
for the case of randomized assignment of treatment. This article extends the method to difference-indifference evaluation to make it applicable to cases where treatment and control populations do not
share the same initial distribution. It also provides the first empirical application of this technique,
highlighting the importance to look beyond average treatment effects.
To illustrate our method we use a unique, large data set from arid and semi-arid Kenya to compare
changes in acute child malnutrition, measured by the Mid-Upper Arm Circumference (MUAC). In
particular, we focus on the differences in changes in nutritional status between areas that have
benefited from additional public expenditures through the second phase of the World Bank-funded Arid
Lands Resource Management Project (ALRMP II) and areas that have not. This article is the first to
evaluate welfare changes over time in a stochastic dominance framework. It is also the first study to use
stochastic dominance analysis for MUAC data.
Acute malnutrition remains pervasive in arid and semi-arid Kenya between 2005 and 2009. Using
standard difference-in-difference regression as a baseline we find no statistically or practically significant
mean impact of ALRMP II expenditures on child malnutrition. In contrast, our stochastic dominance
estimations reveal that project expenditures have had different impacts on different parts of the
distribution. In particular they are correlated with a positive impact on child nutritional status at the
lower end of the distribution. They may have prevented the nutritional status of the worst-off children
from worsening and, thus, may have functioned as a nutritional safety net.