Total Impact of the HSCT Program

We utilize the panel sample of households to conduct a difference-in-differences (D-in- D) analysis to estimate the impact of the program on life satisfaction. Since the SWLS is only asked to the main respondent of the household, the unit of analysis for this model is the main respondent, and we control for both main respondent (individual) and household characteristics.

Equation (1):

𝑌_ℎ𝑗𝑡 = β₀+ β₁Post_𝑡+ β₂Transfer_𝑗+ β₃(Transfer ∗ Post)_𝑗𝑡

+ β4HHDemographicsℎ+ β5HHMainRespℎ+ β6Strata𝑗+ β7Prices𝑗𝑡 + β₈Week_𝑡+ ε_ℎ𝑗𝑡

87 where

Yhjt is the score on Satisfaction with Life Scale (SWLS) measured in log scale for

main respondent for household h in Ward j at time t;

Postt is an indicator that equals 1 if the time period is 2014 (12 month follow-up)

Transferj is an indicator that equals 1 if the household is in a treatment Ward

HHDemographics refers to log of household size, and the number of people below 5, between 6-17, between 18-60, and those above 60

HHMainResp refers to the household’s Main Respondent characteristics, which include indicators for if the household main respondent is female, widowed, divorced/separated, has attended school, currently attends school, and linear variables for the highest grade attained and age of the household main respondent Strata are indicators of the strata used in selecting Wards. It includes two

dummies to indicate if the household was located in Mashonaland East or Masvingo. The reference strata is Mtabeleland North.

Pricesjt refer to a vector of cluster level prices of eight staple items.

Weekt is the week in which the household is interviewed.

β₃ represents the impact estimator, or the effect of being a cash transfer beneficiary

We run ordinary least squares (OLS) regressions, clustering standard errors at the ward level. To increase statistical power (McKenzie, 2012), we control for baseline values for main respondent characteristics and household demographics except for prices, which we maintain as

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exogenous and allow to vary by time period. The program has had no inflationary effect in treatment wards.

As described earlier, the study design is a ward level longitudinal matched design where households in both comparison and treatment districts went through official program targeting. Participation in the program is not demand-driven: the program eligibility identification process determines eligibility, and there were no refusals to participate in the program among eligible households, i.e., take up is universal among the eligible. Therefore, there is no self-selection into the treatment group.

The identifying assumption of the difference-in-differences model is of ‘parallel trends’, i.e., the trajectory of the dependent variable over the study time period would be the same across treatment and comparison wards in absence of the program. As described in the Study Design section, comparison wards were ‘matched’ to treatment wards by a scoring system based on five variables, which cover level of development and agro-ecological characteristics, to try to

maintain the validity of this assumption. Trends in household consumption and production are expected to depend on these five indicators. In addition, baseline balance tests indicate that households across the treatment and comparison samples are balanced on a number of key demographic and socioeconomic characteristics (see Table 3.4). This is as expected since all households are eligible for the HSCT, having been selected according to the same program eligibility criteria. We do not have multiple pre-baseline data points to confirm parallel trends and must therefore maintain this as an identifying assumption. The fact that households themselves are balanced on key characteristics, and that comparison Wards are both

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geographically adjacent and are matched on characteristics that would determine trends in consumption, wellbeing and production suggest that this assumption is plausible.

The D-in-D model does not control for differences between the treatment and comparison groups on account of household or individual unobserved characteristics. Our impact estimate (β3 in the above equation) may be biased if there are unobserved characteristics influencing both

the program and our outcome measure. A fixed effects model at the household level can address the issue of unobserved characteristics that are fixed over time as a source for endogeneity. Note though, that the threat unobservable characteristics impose to the validity of our model is

minimal because, as mentioned above, households in both arms are selected according to program rules and take up is universal among the eligible, so there is no self-selection into the treatment group. There is a second reason, however, why employing the fixed effects model is warranted for estimating the impact on the SWL score. Subjective measures such as the SWL scale can lead to responder bias since some element of their predisposition or attitudinal characteristics will enter into the responses they give for the set of five questions that comprise the SWL. If respondents interpret and answer these questions relative to their personal frame of reference and this heterogeneity is correlated with other covariates, then our coefficient estimates may be biased. It is, therefore, important to have panel data, where we follow the same

respondent from one year to the next to control for this type of responder bias. We estimate Equation (2) using only the subsample of households where the main respondent has not changed from baseline to follow-up. This is our preferred model:

Equation (2):

90 where

Yhjt is log of the Satisfaction with Life score of the main respondent in

household h in Ward j at time t

αh (h=1….H) is the intercept for each household (h household-specific

intercepts)

Post, Prices, and Week are as described in Equation (1)

β2 represents the impact estimate and νhjt is the time-varying error term

Standard errors are clustered at the ward level.

Out of the 2,630 households that comprise our panel sample, over 76 percent (2,007 households) has the same main respondent across the two time periods. Table B.2 of Appendix B shows the difference in household characteristics between households where the main

respondent remained the same to those where it changed at follow-up. We find significant differences between the two groups. On average, household where the main respondent had changed tend to be larger, have lower per capita total expenditure, and belong to the male gender. However, baseline characteristics between treatment and comparison households in this ‘same respondent’ panel continue to be balanced (Appendix B Table B.3).

Together with the log of the Satisfaction with Life score, we run estimations on three other outcomes. First, we construct a binary variable that takes the value of one, if the main respondent’s Satisfaction with Life score is greater than the average for the sample, and zero otherwise. The mean Satisfaction with Life score, across both treatment and comparison groups at baseline in our sample is about ten. We, therefore, use the value of 10 as a cutoff. This binary

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variable acts as a threshold level and helps us understand what percentage of the beneficiaries experience a jump across the baseline mean score of 10. We also utilize two other questions from the survey which asked the respondent to rate “I feel positive about the future” and “I feel happy most of the time” to create two subjective wellbeing indicators. We code responses on the five- point Likert scale as a binary variable indicating individuals who had agreed or strongly agreed with these affirmative statements. These additional questions address different but related domains of the subjective wellbeing construct. Conceptually, movement along these indicators should be in the direction as that of satisfaction with life.