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At the time of midline data collection households had received between five and six bi- monthly payments and so had been in the program for approximately one year; as such, results should be interpreted as one year impacts.

Table 2.3 presents the main program impact results estimated from Equation (1). The first three columns present marginal effect from an unadjusted model controlling only for time,

treatment, and the difference-in-differences dummy variables. The remaining columns are estimated from models that adjust for the full vector of control variables in addition to the DD specification.

We did not find strong impacts of the SCTP on households’ current economic vulnerability to food insecurity. Beneficiary households reduced their food share by two percentage points (p =

0.10), and while not statistically significant, program impacts on the probability of worrying about

having enough to eat and on total food spending were in the expected direction.

The program had strong protective effects against the generally negative trends among the diet quantity indicators. On average, program households were 11 percentage points more likely to consume more than one meal per day (p = 0.001). Members of treatment households increased their apparent calorie consumption by 267.49 Kcal per person per day (p = 0.05) relative to control households, which represents 14 percent of baseline household caloric availability. The program impact on the probability that a household was food energy deficient was -0.10 (p = 0.05), and the

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mean caloric deficit was 111.11 Kcal lower among the treatment group compared to the mean hunger gap in control households (p = 0.05).

There is weak evidence that the program had an impact on diet quality. The DD estimate is positive but not significant for the household diet diversity score. The program significantly

increased spending on three food groups: cereals, meat, and other, although program impacts on meat expenditures and the meat food share were only marginally significant (p = 0.10).

Full results for the adjusted models are presented in Appendix 4. Although all study households are poor, households from the bottom half of the baseline consumption distribution fared worse than those from the top on every FNS outcome. The poorest households were five percentage points more likely to worry about not having enough food (p = 0.01), were 10 percentage points less likely to eat multiple meals per day (p = 0.001), and were 24 percentage points more likely to be food energy deficient (p = 0.001). The poorest households also had lower total food

expenditures, reduced caloric availability, and a larger hunger gap, and consumed on an average of one fewer food groups. Households experiencing unusually high prices for food also fared worse than those households that did not suffer food shocks. They spent less on food, had lower apparent caloric consumption, and were more likely to be food-energy deficient with a larger depth of hunger. Households experiencing a food shock at midline were 15 percentage points more likely to worry about not having enough food (p = 0.001).

2.6.3. Heterogeneous Impacts

Marginal effects from the heterogeneous impact models are presented in Tables 2.4 – 2.7. We find little evidence that program impacts differ in meaningful ways by poverty level, household size, distance to the nearest food market, or the caregiver’s health knowledge score. The only differential program impact among the poorest households relative to beneficiary households in the top half of the baseline consumption distribution is an increase of 775.60 MWK spent on

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consumption items in the ‘other’ category (p = 0.10). Program recipients from the poorest

households spent on average 2,599.64 MWK per capita annually less on cereals and 749.86 MWK less on ‘other’ foods compared to beneficiary households at the top of the consumption distribution (p = 0.05). Lastly, there was a positive differential program impact of 0.03 (p = 0.05) on the food share between households where the caregiver scored in the top third of the health knowledge score distribution and households with scores in the bottom two-thirds.

2.6.4. Transfer Share

We also examined whether program impacts varied by the level of the household’s transfer share (Table 2.8). When modeled as a continuous percentage, a one percentage point increase in the value of the transfer share was associated, on average, with a 13 percentage point increase in the likelihood that a household consumed more than one meal on a typical day during the past week among beneficiary households (p = 0.01).

Next, we considered the effects of the SCTP based on a binary indicator of whether the predicted transfer share was greater than or equal to 20 percent of the household’s pre-program consumption. We found no significant program impacts on indicators of current economic

vulnerability, weak evidence of protective program impacts on diet quantity, and no impacts on diet quality other than a three percentage point decrease in the legume food expenditure share (p = 0.05) among treatment households with low predicted transfer shares relative to control households with low predicted transfer shares. There are, however, very strong program impacts on household food and nutrition security indicators among households with transfer shares of at least 20 percent. For example, relative to control households with high predicted transfer shares, program households with high transfer shares spend, on average, MWK 5,527.92 (p = 0.001) more on food – including MWK 2,850.27 (p = 0.001) on cereals, MWK 1,533.91 (p = 0.01) on meat, and MWK 1,597.13 (p =

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equality of program impacts between high and low share beneficiary households reveal that the differential impacts are significant for total food expenditures (p = 0.05), HDDS (p = 0.05), and expenditures on the ‘other’ group (p = 0.01).

Program impacts based on a categorical representation of the transfer share are presented in the last four columns of Table 2.8. Beneficiary households with expected transfer shares greater than 20 percent but less than or equal to 30 percent experienced the strongest program impacts,

especially among indicators of caloric availability, the hunger gap, and HDDS (no other transfer share group experienced significant impacts on HDDS at the five percent significance level or better). We conclude from Wald tests that none of the impacts on current economic vulnerability, diet quantity, or diet quality differed significantly between beneficiary households in the two highest share categories. Program impacts on HDDS and per capita expenditures on meat are larger for households with shares between 20 and 30 percent compared to shares between 15 and 20 percent

(p = 0.10), and impacts on expenditures for the other food group are larger among households with

shares between 15 and 20 percent compared to households with shares less than or equal to 15 percent (p = 0.05).