feed of our study participants. Since the respondents took our survey on Messenger, we could use anonymous identifiers to create a “Custom Audience” for the treatment group.<sup>37</sup> Subsequently, we handed over this audience to MNM, which then remarketed ads from their own Facebook page for two weeks, mirroring the nationwide campaign.<sup>38</sup> The remarketing campaign reached 2,119 individuals (about 90% of the total audience) at least once in two weeks, for an average frequency of 4.9 ads per person. These ads were displayed in the users’ feeds, alongside with other competing ads and organic content from various sources. Crucially, participants were unaware that the ads were targeted at them based on their survey participation. Consequently, the remarketing campaign closely resembled the nationwide campaign, lending ecological validity to the individual-level study.

Two weeks after the end of the remarketing campaign, we invited respondents to fill out a followup survey. Those who completed it are included in the individual study sample (N=1,542). Annex Table D22 shows the distribution of individuals’ and households’ characteristics in the treatment and control conditions. The sample is strongly balanced across most covariates such as age, gender, education and bed net ownership, with a few small imbalances on household size and unemployment rate. We include a battery of control variables in the regressions to show that our results are robust to these small imbalances.

We focus on two primary behavioral outcomes where we anticipated effects within our two-week window: bed net usage and seeking medical attention in case of a high fever. To facilitate the comparison, the questions were similar to those used in the longitudinal panel survey (see Annex B). To mitigate priming and survey effects, we asked these questions only once, at follow-up. Our workhorse model is a Linear Probability Model, which is estimated using Ordinary Least Squares. It is represented in the following form: where _i_ is an individual residing in district _d_ , _Individual T_ is the individual-level random treatment indicator for the remarketing campaign, which varies across individuals. _T_ is the district-level random treatment indicator, which varies across districts and captures the potential long-lasting effects of the nationwide campaign. **x** represents a vector of individual-level control covariates, which are described in Annex Table D22. We adjust the standard errors to account for heteroskedasticity.

# **6.3 Targeting Matters**

Table 7 reports effects on bed net usage. We find a positive and significant effect of the individual level treatment on the probability that a respondent has slept under a mosquito net the previous

> 37Remarketing is a tool offered by the major ad platforms by which an ad campaign can be run to a group of individuals who have previously interacted with the advertiser in some way. This implies that the individual has either come directly from an ad to a platform (website, app, chatbot, etc.) controlled by the advertiser, has directly connected their account (i.e., via “Login with Facebook”), or has shared personally identifying data and given the advertiser permission to use that data to market to or communicate with them.

> 38This campaign featured the highest-performing ads from the earlier nationwide campaign, which had received the bulk of the budget. See Annex Figure for a selection of these ads A7).

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