risk, but on the population-level effect of mass behavior change on the risk of an average member from the population. A cluster design allows us to estimate such population-level effects.

# **4.2 Stratified Recruitment**

We used Facebook advertising to recruit survey respondents from the previously mentioned circles in both treatment and control districts, incentivizing participation with mobile credit ranging from 2.6 to 3.6 USD. Respondents were asked to complete a series of surveys conducted via chatbot on Facebook Messenger and delivered by a page that we created called “Global Insights”.<sup>18</sup> The main benefit of employing chatbot surveys on Messenger lies in their seamless integration into the ad platform itself. Moreover, the chatbot streamlines follow-up with respondents, as inviting them to additional surveys can be as simple as sending a new chat message.

Before conducting the study, we carried out a formative research phase to guide our sampling design. Specifically, in July 2020, we started recruiting respondents asking them several demographic and socio-economic questions, as well as whether they or anyone in their household had had malaria in the past 5 years and in the previous two weeks. These surveys indicated that one of the original study states, Odisha, exhibited low malaria rates among our target population. Consequently, we opted to exclude it from our trial due to statistical power constraints.

Importantly, the analysis of our formative research data in the other three study states revealed that dwelling type was the most robust household-level predictor of past and current malaria incidence.<sup>19</sup> Its predictive capacity, both in terms of Pearson pairwise correlation and OLS regression coefficients, surpassed that of other factors such as proximity to health facilities, membership to a backward caste, family size, religion, and the educational background of the respondent (Annex Tables D1 and D2). Specifically, past and current malaria incidence rates in households living in solid dwellings (i.e., those with walls and roof made of bricks, stones and cement) were 21% and 43% lower compared to those living in non-solid and semi-solid houses (i.e., made of mud, tin and straw), respectively.<sup>20</sup> For our analysis, we combined the latter two dwelling categories.

Understanding the most important risk factors enables us to estimate conditional treatment effects for the most vulnerable populations, addressing the research question of _whether social media campaigns reach and impact these high-risk groups_ . In our initial recruitment, however, people in non-solid dwellings were significantly less likely to be reached and to respond to our survey than those in other dwelling types. In fact, the vast majority of respondents lived in solid dwellings. For this reason, we decided to stratify our recruitment by district and dwelling type (160 strata in total, 80 for the districts and 2 for the dwellings). Besides being a strong predictor of malaria risk, dwelling type captures the dimensions of poverty and rurality (this variable is correlated with education,

> 18Annex Figure C2 shows examples of ads used to recruit survey participants.

> 19Following the India DHS survey, respondents were asked which kind of house they lived in among three options: non-solid houses (made of mud, tin, straw); solid houses (built with durable materials like stones, bricks, cement, or concrete, encompassing the entirety of the structure, including the floor, roof, and outer walls); semi-solid houses, which occupy an intermediate position between the previous two housing types.

> 20This is in line with previous work in India (Yadav et al., 2014) and elsewhere (Wanzirah et al., 2015).

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