where the outcome y and the controls X refer to network member j, but exposure to Shuga is indirect, only through j’s friend i. In the presence of spillovers, the estimated coeffi cient β<sup>�</sup> S should have the same sign as β<sup>�</sup> in (11). In other words, if Shuga positively affected i’s outcome and i talked about it with his/her friend j, then j’s outcome would also respond positively (and vice versa).

We also estimate a variant of (14) that includes an interaction between Tilc0 and a dummy for whether i and j have opposite sex. The idea is that discussion around the issues covered in Shuga may be different among people of same vs. opposite sex, and that the latter may be a coarse proxy for whether the pair is potentially a couple. Some behaviors, e.g., condom use, may be easier to adopt if both partners have been exposed to Shuga.

Reporting results. Since we have a large number of outcome variables, we present results in two formats. First, to address the issue of multiple hypothesis testing, we group our original outcomes into indexes. This reduces the number of hypotheses actually tested and increases statistical power by reducing errors due to random variation at the level of the individual variables. We describe the construction of the indexes in section 4.2. Aggregation only partially solves the multiplicity problem, as we still have several hypotheses being tested jointly. To correct for this, we adjust p-values according to the free step-down resampling method (Westfall and Young, 1993) so that they can be used to control the family-wise error rate (FWER), defined as the probability of rejecting at least one true null hypothesis.

Our second way of reporting results focuses on individual outcomes that can be regarded as important on their own. These are selected within the broader list of variables from which we compute the indexes, and we single them out because they are key to the overall message of Shuga (e.g., “you should wear a condom”; “having concurrent partners can be risky”; etc.) or they capture specific messages that are strongly emphasized in certain episodes (e.g., “a young boy should be allowed to play football”; “you have to take a second HIV test after 3 months”; etc.). For our coeffi cients of interest, we report both ‘naive’standard errors corrected for clustering at the location level, and FWER-adjusted p-values —that adjust for multiple hypothesis testing—based on 10,000 replications.

# 4.2 Data and descriptive statistics

## Sample

In total we interviewed 5166 main study participants at baseline and 4986 at follow up.<sup>13</sup> Since our conditional specification includes the lag of the dependent variable, our working sample consists of the individuals for whom we have both rounds of data, i.e., 4986 observations.

> 13 We performed power calculations using the Nigeria DHS 2008 and determined that a sample of 64 individuals per location, or 5120 individuals in total, half male and half female, would enable us to detect a change of between 0.15 − 0.20 standard deviations in our main outcomes of interest. Updated power calculations using our baseline data showed an improved minimum detectable effect of 0.12 − 0.17 standard deviations.

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