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investment data

Over 602,000 individual child MUAC measurements were taken in 128 sublocations in 10 arid and semiarid ALRMP II districts between June 2005 and August 2009. Table 1 presents the sample sizes and
selected MUAC Z-score statistics for intervention and control sublocations for the two years used in the
analysis. We classified sublocations into intervention and control groups according to the cumulative
ALRMP II provided by the ALRMP district data managers.3 The distribution of project
investments suggests a natural cut-off point with sublocations without any sublocation specific
investment forming the control locations and sublocations with some investment the intervention
Table 1 Sample size and selected MUAC Z-score statistics for intervention and control sublocations
year Sample size Median MUAC Zscore
25th percentile
MUAC Z-score
10th percentile
MUAC Z-score
intervention control intervention control intervention control intervention control
2005/06 71,315 63,863 -1.22 -1.12 -1.80 -1.67 -2.31 -2.14
2008/09 30,046 25,570 -1.15 -1.07 -1.70 -1.64 -2.16 -2.12
Overall levels of nutrition are very low. All summary statistics lie below minus one standard deviations,
indicating mild malnutrition on average in this population. The severity of malnutrition is particularly
evident when looking at the 25th and 10th percentile of the distribution. For example, more than 10
percent of children fall below the threshold of severe malnutrition of minus two standard deviations.
Children in intervention sublocations are worse off than those in control sites. This indicates a
placement effect of the ALRMP interventions. Our stochastic dominance difference-in-difference helps
control for this non-randomness and avoids the negative bias of simple evaluation methods that fail to
control for initial differences between control and treatment groups. These summary statistics suggest
that child nutrition improved from 2005/06 to 2008/09 with larger improvements in intervention
communities and among the least well-off.
To estimate changes over time and compare them between intervention and control sublocations more
rigorously we need to construct a panel. Our child-level observations are unsuitable for this for three
reasons. First, individual child identifiers are not consistent across time in the data set. Second, MUAC
data are not available for all children in all months. Third, and most importantly, the sample of children
will necessarily change over time