# **4.4: Estimation Strategy**

Given random assignment to treatment, we construct intent-to-treat estimates for treatment impacts from the following core OLS specification<sup>19</sup> : _yi =_ β _0 +_ β _1Ti +_ β _2 Prei_ + **_X’_** β _+_ ε _it_ Where _i_ indexes households and _t_ indexes the survey round. _yi_ are target outcomes, _Ti_ is treatment assignment, **_X’_** is a vector of time-invariant demographic characteristics and _Prei_ denotes outcomes at baseline (20 months prior), when available (i.e. only for households who were surveyed at both baseline and midline). When considering an individual-level outcome, as in the case of child development measures, _i_ instead indexes individuals. We report results from three specifications: a parsimonious specification (I) with no additional controls; a specification (II) with demographic controls including household size, gender of household head, age of household head, education of household head, distance of the household from the nearest school, weeks since the last payment and age of the youngest child, and finally a specification (III), which includes both demographic controls and controls for values at baseline where available. As explained earlier, we have a sub-sample of the population who were sampled at both baseline and midline of 3,883 households. However, one of the key control variables (weeks since last payment) is missing for a portion of the population, so the sample in specifications II and III are lower than the full sample (maximum sample of 4,061 in specification II, and 2,762 in specification III). As a robustness check, we have run all analyses without the inclusion of this variable and see no major effect on the results. For these purposes, we will focus results from Specifications I and II when discussing results.

Our results compare outcomes for the three ‘behaviorally enhanced’ arms to outcomes for the cash-only group, in order to test for additionality from the enhancements. Standard errors are clustered at the village (fokontany) level. We judged that no correction for multiple-hypothesis testing was required, despite the number of hypotheses tested, given the heterogeneity of constructs measured and our pre-specification of these analyses.<sup>20</sup> All standard errors are clustered at the level of the unit of randomization (‘fokontany’ or village).

# **Section 5: Baseline Results and Randomization Check**

# **5.1: Key Metrics at Baseline**

The baseline survey provided important insights into the living conditions and nutrition, dietary diversity, and parenting practices of the beneficiaries and control group before the HDCT launch. Food accounts for approximately 74% of monthly expenditure (of which rice accounted for, on average, 50% of expenditure).<sup>2122</sup> Grains and starches combined comprise approximately two thirds of average household consumption of food. Less than 7% is spent on fresh fruits and vegetables. Households on average spend approximately 2,361 ariary (~0.75 USD) a day on food expenditures, or approximately 70840 ariary (~22.65 USD) a month. 41.2% responded that they were never able to “properly” diversify their food intake, while only 7.9% reported always doing so. The data on parenting practices display a great deal of variation. Only 4.5% of the sample reported telling stories to children during the past week at baseline, and less than half took children out for a walk, though other activities such as singing (63.6%) and playing (73.1%) were more common. Overall, of 12 included activities, the households reported pursuing only 2.6 on average with their two youngest children in the past week.

# **5.2 Balance across Arms**

Table 1a describes the sample and provides full balance statistics, while Table 1b provides balance statistics for the population sampled for the MDAT (test of socio-cognitive development). Note the generally balanced means across the evaluation groups, indicating that random assignment was successful.

> 19 We have used Ordinary Least Squares (OLS) estimates in all cases, preferring a linear probability model in the case of binary outcomes for simplicity of interpretation.

> 20 Pre-analysis plan registered at the American Economic Association and available at

> <u>https://www.socialscienceregistry.org/trials/957/history/6111.</u> 21 Rice consumption and storage is a salient feature of rural Malagasy society, one reason for the difficulty of changing nutrition practices (Michel & Randriamanampisoa 2017).

> 22 In addition, in most households agricultural production is consumed by the household rather than sold. People generally tend to consume all items of production (on average 3 items) but only sell surplus for 1 item on average.

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