# **III. Empirical Strategy and Data**

## **A. Empirical Strategy**

Given the randomized phase in of the program, this evaluation relies on a phased in randomized control trial methodology to establish the causal impacts of the CfW program. The advantage of such a phased in trial design is that a simple comparison of the outcomes among the two randomly created groups yields an unbiased estimate of the impact of the CfW program.

The overall evaluation sample is provided by 276 communities: 143 treatment communities and 133 control communities (see above). The study population includes 17,608 beneficiary households, 8,944 in the treatment group and 8,664 in the control group. However, because control communities received the program approximately four months after the treatment communities, the evaluation is able to examine the impacts of the program only in the short run, that is, over approximately three or four months.

In the analysis of program impacts, two sets of analyses were undertaken. First, because all the surveyed households had been selected to participate in the program, intent‐to‐treat (ITT) analysis has been conducted. The impacts of the program on various household‐level outcomes of interest are estimated using ordinary least squares regression (equation 1). The regression used to obtain the ITT estimates is as follows: where _Treat_ is a dummy for assignment to the treatment group, that is, it equals 1 if a household belongs to a community that was randomly assigned to the treatment group and 0 if a household was in a community randomly assigned to the control group.

Despite careful efforts to ensure adherence to randomized assignment, there is a possibility of noncompliance; in other words, households that may have initially signed up for the program may not have participated, or some of the control beneficiaries may have participated. To ensure robustness, estimates have been produced of the effect of the treatment on those households with a treatment status affected by the random assignment, that is, the compliers; this effect is known as the local average treatment effect (LATE).<sup>12</sup> These estimates are the instrumental variable (IV estimates) of _β_ in the equation above, using the dummy for random assignment as an instrument for treatment. However, the results are similar to the ITT estimates because of the low levels of noncompliance: the household surveys indicate that only around 1.8 percent of the treatment households reported they did not participate, and 7.4 percent of the control households reported they participated. Therefore, only a subset of the LATE estimates—the overall estimates—are presented in this paper.

Outlier corrections and log transformations have been conducted for variables in leone values. To reduce the influence of outliers, variables in leone values that were identified with outliers have been winsorized at the 1st and 99th percentiles.<sup>13</sup> The outlier‐corrected estimates and the log‐transformed estimates are presented in Annex 2.

> 12 See Angrist and Imbens (1995); Angrist, Imbens, and Rubin (1996).

> 13 Outliers were identified using the blocked adaptive computationally efficient outlier nominators algorithm proposed by Billor, Hadi, and Velleman (2000), using the 15th percentile of the chi‐squared distribution as a threshold to separate outliers from

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