# **6 Empirical Strategy**

## **6.1 Survey Data Analysis**

### **Adolescent Outcomes**

Our main intention-to-treat (ITT) impact estimations for adolescent outcomes use the following analysis of covariance (ANCOVA) specification:<sup>37</sup> Where _Yi,t_ =1 _,_ 2 is the outcome variable for adolescent _i_ measured at midline ( _t_ = 1) and endline ( _t_ = 2), and _Yi,t_ =0 is the baseline value of the outcome variable.<sup>38</sup> _CHOICESi_ is a dummy variable that equals 1 if adolescent _i_ was randomly assigned to receive the _Choices_ training program. That is, this indicator equals 1 if adolescent _i_ was assigned to either _Choices only_ or the combined _Choices_ and _EMB_ treatment arms and 0 for the control group. _EMBi_ is a dummy variable that equals 1 if adolescent _i_ also had at least one adult in their household assigned to receive the _EMB_ training sessions. _Xi,t_ =0 is a vector of baseline control variables, _λd_ are district-level fixed effects, and _εit_ is the error term. Standard errors are clustered at the community cluster level.

In Equation 1 the coefficient _β_ 1 measures the impact of the _Choices_ training relative to the control group, and _β_ 2 measures the marginal impact of having your parents/guardians being trained in _EMB_ . We do not estimate a separate treatment effect for the treatment arm that combines the two interventions, instead, we are more interested in showing the marginal effect of involving parents directly in the program. Throughout the analysis, we control for whether there were multiple adolescents eligible for the program in the household, and the age of the adolescent. We do not pool the two follow-up survey rounds as we are interested in estimating the short-run effects (midline) and longer-run effects (endline) separately. We run the regressions on both the full sample of adolescents as well as conduct heterogeneity analysis by respondents’ sex.

Our survey instruments include several questions about individual behaviors or beliefs that are closely related. To account for multiple hypothesis testing, we create indices for outcome families following the method used by Kling et al. (2007). More precisely, we aggregate primary outcome variables into an index or composite variable to be the equally weighted average of z-scores, where z-scores are calculated by first subtracting the control group mean of the variable and then dividing by the control group standard deviation before taking the average of these normalized variables. We also present sharpened False Discover Rate q-values based on a simple method proposed by Benjamini et al. (2006) to calculate the smallest level of significance at which the null hypothesis would be rejected as described in Anderson (2008). These sharpened two-stage q-values are presented in our main regression tables in square parentheses below the standard errors.

> 37The ANCOVA estimator has been shown to have more statistical power than a difference-in-differences estimator when outcomes are not strongly autocorrelated (McKenzie, 2012).

> 38For those outcome variables that were only collected during follow-up surveys, we estimate equation 1 without controlling for the baseline value of the outcome ( _Yi,t_ =0).

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