Appendix Table 2.1. Key Study Variables
Variable Chapter 2 Chapter 3 Definition
Treatment exposure exposure binary
Simulated transfer share exposure exposure continuous
High share exposure exposure binary
Low share exposure exposure binary
Poorest 50% of households at baseline moderator moderator binary
4 or fewer household members moderator moderator binary
Distance to market moderator instrumental variable binary
Caregiver health knowledge score moderator moderator binary
Worried not enough food outcome binary
Per capita real annual food expenditures outcome continuous
Per adult equivalent (AE-L) real annual food
expenditures input continuous
Food share outcome input continuous
More than 1 meal/day outcome binary
Kcal per capita outcome continuous
Food energy deficient outcome binary
Depth of hunger outcome continuous
HDDS outcome count
Per capita real annual expenditures on 5 food
groups* outcome continuous
Share of total food expenditures devoted to 5 food
groups* outcome input continuous
Kcal per capita per day for 5 food groups* outcome continuous
Kcal per adult equivalent per day for 5 food groups* input continuous
Share of total Kcals for 5 food groups* outcome continuous
Health status outcome binary
Health improvement outcome binary
Diarrhea outcome binary
Fever outcome binary
Cough outcome binary
Any illness outcome binary
Height outcome continuous
HAZ outcome continuous
Stunted outcome binary
WHZ outcome continuous
Wasted outcome binary
WAZ outcome continuous
Underweight outcome binary
Health passport input binary
Under-5 service input binary
Any health expenditures input binary
Solid food > 1/day input binary
Nutrition program input binary
Vitamin A past day input binary
* 5 food groups include: (1) cereals, roots, tubers (2) fruits and vegetables (3) meat, fish, eggs, dairy (4) legumes, nuts, pulses (5) oils, sweets, condiments, beverages
173 Health Knowledge Score
The health knowledge score was created from a series of eight questions the caregiver responded to about young child nutrition, diarrhea, malaria, and tuberculosis. The questions had multiple correct answers, so the score for each question was the sum of correct responses given for that question. The total sum of correct answers ranged from one to 19. We decided not to use the sum of the items as the score as suggested by Classical Test Theory, which implicitly assumes that all questions are equally important in contributing to the score; here, the score – or the latent construct – is “health knowledge”. Rather, we employed polychoric factor analysis 112 to reduce the eight potentially collinear items (Bartlet’s test of sphericity chi-square = 4,427.65, df(28), p = 0.00; Kaiser- Meyer-Olkin measure of sampling adequacy = 0.73). We retained the first factor, which had an Eigenvalue of 3.26 and explained 40.80 percent of the total covariance. The health knowledge score was calculated as the household’s predicted value of the first factor. We sorted the score in
increasing order, and those households scoring in the top 66.67 percent of health knowledge scores receive a value of one for the health knowledge variable. It is important to note that the health knowledge questions were only asked during the midline follow-up survey. As the SCTP does not contain an educational component, we consider health knowledge as time-invariant between the survey rounds and find no differential health knowledge scores between treatment and control groups (p = 0.32).
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Appendix Table 2.2. Health Knowledge Score
Module 4B: Child Health Knowledge Questions
22. At what age should a baby be fed other foods and liquids (other than maternal milk)? 23. There is a nutrient found in food called ‘iron’ which helps children ‘accumulate’ blood
(nutrient that makes them strong). Can you tell me some foods that are a good source of iron? Anything else?
24. Vitamin A is a nutrient that helps children grow. Can you tell me some of the foods that are rich in Vitamin A? Anything else?
25. What needs to be done when a child has diarrhea? Anything else?
26. What signs/symptoms would lead you to think that a person has malaria? Anything else? 27. What do you think is the cause of malaria? Anything else?
28. How can someone protect themselves against malaria? Anything else? 29. Have you ever heard of an illness called tuberculosis or TB?
30. How does tuberculosis spread from one person to another? Anything else?
Data Cleaning – Children Under-Five
The child panel data were cleaned prior to deriving the analytical sample and calculating the anthropometric indicators. We first identified the panel children, and then the change in the child’s age in months between the baseline and midline surveys was reviewed to check for children getting younger or aging by implausible amounts (i.e., more than 24 months or less than 10 months). There were a total of 282 panel children with flagged ages; to reconcile the age variable we first looked at the child’s reported age in years and months in the Child Health survey module for both rounds and compared it to the child’s age in years as reported in the Household Roster. We then compared the baseline and midline ages with ages for the child reported at endline (where available) to triangulate which two out of three ages were most consistent. The last metric we considered was the time lapse between the baseline and midline surveys, which averaged to 17 months. We were able to correct ages for all but 25 children. In the case where a child was recorded as a different sex at baseline and midline, we deferred to midline data as enumerators using tablet-based CAPI (computer-assisted personal interviewing) were made aware of the discrepancy and instructed to verify the response in real-time. The last component of cleaning the anthropometric data among panel children was to
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investigate changes in height over time. Intuitively it makes sense to drop observations for children whose height decreased over time. However, we cannot be certain about the direction of
measurement error, so dropping all observations with negative height gains without some way of also correcting for height increases due to positive measurement errors or attenuated height increases due to negative measurement errors can introduce bias into the sample. We decided to retain all panel children whose change in height between the midline and baseline surveys was within +/- three standard deviations of the mean height change among all panel children; fortunately all of the children with negative height changes (18 total) were within this range and so were eligible for study inclusion.
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