To preview the main results, we begin by plotting the full distribution of endline HAZ and WHZ for all measured children by their mNutrition treatment group. Figure 6.1 shows the distribution of HAZ scores while Figure 6.2 does the same for WHZ scores. In both figures we also plot a vertical line at the cut-off for stunting (in the HAZ case) or wasting (in the WHZ case).
As was the case at baseline, the HAZ distributions for children in treatment villages and children in control villages track one another closely. The peak of the density for treatment children occurs slightly before the peak among control children, which could indicate a slight height disadvantage for treatment children around the stunting cut-off (HAZ < -2); however, the distribution for treatment children crosses below the control distribution around -1, suggesting—if anything—a slight
advantage for treatment children beginning at a HAZ score of -1. Thus, at most there are small differences in the HAZ distribution across treatment groups, and the differences do not consistently favour one group.
Similarly, the WHZ distributions for treatment and control children are difficult to distinguish from one another, with at most small and inconsistently signed differences appearing. Control children are slightly advantaged relative to treatment children around the wasting threshold, but this ordering is reversed between -2. Few children are overweight (WHZ > 2), and there are no apparent differences in the density of treatment and control children above this point.
16 PPI scores map households in relation to the likelihood that they will fall below different national and international
poverty lines. We use the likelihood that households will fall below 150% of the national poverty line in Tanzania as our primary PPI score.
The raw HAZ and WHZ densities therefore suggest few, and at most minor, differences between treatment and control children. To explore potential impacts on anthropometry in a more
parametric but systematic fashion, we next turn to the regression-based results described above.
Figure 6.1: Distribution of children's HAZ by mNutrition treatment group
Source: Authors’ own
Source: Authors’ own
Table 6.1 presents the main impact estimates for the child anthropometry outcomes. Consistent with the lack of observable differences in the HAZ and WHZ distributions across treatment groups, we find no evidence that the mNutrition service had any effect on HAZ or WHZ. The point estimate in both the basic and extended models for HAZ is negatively signed but small in magnitude (-0.06) and not statistically distinguishable from zero at the 10% level. The point estimate for WHZ is positive but even smaller than the HAZ coefficient (0.029) and similarly not distinguishable from zero. WAZ is likewise not affected by the offer of access to the mNutrition content. Unsurprisingly, given that the random assignment of mNutrition ensured balance in age and sex across the treatment and control groups, height (in cm) and weight (in kg), unadjusted for the age and sex of the child using the reference distribution of children, are also not impacted by the mNutrition service.
Table 6.1 additionally shows impact estimates for the likelihood that children are stunted, wasted, underweight (WAZ < -2), and overweight or obese (WHZ > 2). For none of these four outcomes are we able to reject the null hypothesis that there was no impact of access to the mNutrition service. The point estimates for stunting and wasting are positive but small in magnitude. At the 5% level, we can rule out any impact outside the 95% confidence interval, which includes all impacts larger than a 6.9 percentage point increase or a 1.2 percentage point decrease in stunting. By the same logic, we can rule out any impact on wasting larger than a 2.1 percentage point increase or a 0.2 percentage point decrease. Only 3.6% of the control group children are classified as overweight or obese, and, as mentioned, this is unaffected by access to the mNutrition
messaging.
Across all the child anthropometry outcomes, we find no evidence that providing households with access to the mNutrition service had an impact—positive or negative—on any of the outcomes. In Annex C we show the simple difference analogues of the main ANCOVA estimates. The point estimates are nearly identical and in no cases are any of the conclusions different when using the two methods.
Table 6.1: Impact estimates of impact of mNutrition on child anthropometry, ANCOVA
Control mean Impact estimates, basic controls Impact estimates, extended controls N Child height (cm) 79.593 -0.153 -0.075 2803 (0.210) (0.187) Child weight (kg) 10.549 -0.009 -0.009 2803 (0.062) (0.062) Child HAZ -1.976 -0.060 -0.039 2794 (0.055) (0.051) Child WAZ -0.933 -0.018 -0.022 2802 (0.043) (0.042) Child WHZ 0.182 0.029 0.005 2792 (0.052) (0.044)
Anderson Index: HAZ and WHZ combined
0.006 -0.010 -0.013 2800
(0.031) (0.030)
Child stunted 0.501 0.029 0.023 2794
Notes: Estimates from the mNutrition Tanzania endline survey sample. Standard errors are in parentheses and clustered at the village level. Impact estimates report the coefficient on the treatment from an OLS regression of the outcome of interest on the treatment variable, controlling for baseline household classification, whether child is the focus child, child’s age, child’s gender, and value of the respective outcome at baseline. The latter is replaced with 0 for children who were not measured at baseline; indicators for missing measurements are included as controls. Extended controls are covariates from baseline: household size, whether household head is female, whether household head is literate in Swahili, whether primary female owns a mobile phone, and PPI score. Control mean is comparison group's mean at endline. * p<0.10 **p<0.05 ***p<0.01
Source: Authors’ own