population among the controls. Columns (1-3) report the estimates from a linear model using the _log_ number of cases, while columns (4-6) report the results from a Poisson regression. In both cases, the results are closely aligned with the evidence presented so far, indicating that the campaign had beneficial effects in urban localities only. For example, the _log_ specification estimates a 30% reduction in urban incidence, and the effect is even larger in the Poisson model (both estimates are significant at the 95% confidence level).

# **6 Mechanisms: Limited Effectiveness or Limited Reach?**

The analysis of survey and health facility data indicates that the MNM campaign benefited households residing in solid dwellings and in urban settlements. However, it did not benefit households that are most susceptible to malaria. What mechanism underlies these differential effects? There are two potential explanations for our results. First, the ad content itself may not have been effective for the sub-population residing in non-solid dwellings and rural areas. This could indicate shortcomings in the identification and ad design for these type of personas. Second, the ad campaign might not have sufficiently reached this key sub-population. This underscores the importance of accounting for algorithmic targeting (Lambrecht and Tucker 2019; Sapiezynski et al. 2022) when aiming to engage with underrepresented, geographically dispersed (and therefore more expensive to reach) populations on social media platforms. To disentangle these potential mechanisms, we employed two additional analyses. First, in a post-campaign survey we included ad-recall questions to assess how well participants remembered the campaign messages. Second, we implemented a _feed_ experiment where we ensured exposure to the campaign for both household types. The results of these analyses are detailed below.

## **6.1 Post-Campaign Recall**

Our post-campaign survey measured ad recall for the MNM campaign. If the ads reached both solid and non-solid dwellers at similar frequencies, then one would expect both audiences to equally recall the Facebook advertising or the advertiser itself. To measure this, in the last wave of the longitudinal sample (completed between mid-December 2020 and the end of January 2021), we explicitly asked respondents if they remembered having seen an ad from Malaria No More on Facebook since August 2020. Table 6 shows the results. Columns (1-2) show a 3 p.p. increase in ad recall across all treated respondents, corresponding to a 27% lift with respect to the control group. However, columns (3-6) show that the effect is driven by individuals residing in solid dwellings, where ad recall increased by 5 p.p., a 50% lift with respect to the control. For non-solid dwellers, the coefficient is close to zero and statistically insignificant. Estimates from a logistic model reported in Annex Table D20 confirm these results, with marginal effects being identical to the OLS coefficients.

The analysis of treatment heterogeneity for the recall question provides initial evidence that the ad campaign did not reach vulnerable populations. Annex Table D21 explores how ad recall varies among younger respondents and those with higher concern about malaria. These groups were 28