million reactions on Facebook and Instagram across 22 Indian states),<sup>3</sup> our study focuses on its offline behavioral impacts: “Did the campaign work?” and “Did it work for those most at risk?” We adopt a multi-stage methodology. We start by recruiting survey respondents stratifying across 80 districts in the three study states: Uttar Pradesh, Jharkhand, and Chhattisgarh. Specifically, we use ads on Facebook and Instagram, where we offer mobile credit as an incentive to fill out a baseline survey using a chatbot integrated with Facebook Messenger. During the initial phase of recruitment, we conduct formative research to identify households most at risk of contracting malaria and ensure their inclusion in the study sample. After considering various demographic and socio-economic characteristics, we find that dwelling type is the most robust household-level predictor of malaria incidence.

Respondents living in solid houses (i.e., dwellings made of stones, bricks, cement, and concrete) are 43% less likely to report recent malaria cases compared to those living in semi-solid or non-solid dwellings (i.e., made of mud, straw, and tin).<sup>4</sup> Beyond being a strong predictor of malaria risk, dwelling type also provides a convenient mapping into administrative data on malaria incidence, which are disaggregated across urban and rural health facilities. In India, non-solid and semi-solid dwellings make up approximately 15% of housing in urban areas and around 50% in rural areas (India IIPS, 2022).

During the initial recruitment phase, respondents living in solid dwellings were more likely to join our survey. To correct for this imbalance, we stratify our recruitment by dwelling type. Since Facebook does not offer the option to micro-target ads based on housing characteristics, we use the Facebook Lookalike Audience tool to target individuals living in non-solid dwellings. We show that this technique works to increase the number of respondents living in non-solid houses.

We apply this technique to recruit survey respondents into a longitudinal sample (N=5,205) and a cross-sectional sample (N=3,052), totaling 8,257 individuals in the three study states, balancing across 80 districts and the two dwelling types. To estimate the population-level effects of the malaria-prevention campaign, we work with the MNM advertising team and conduct a cluster randomized controlled trial, where we expose half of the 80 districts to the same ad content and delivery strategy used in the nationwide campaign. It is important to note that MNM employed conventional targeting techniques (i.e., based on socio-demographic characteristics available on the ad platform), optimizing ad delivery for engagement. Survey respondents in treatment districts were not necessarily exposed to the campaign, hence our results estimate intent-to-treat (ITT) effects.

We rely on the longitudinal sample to assess the impact of the campaign on a range of selfreported attitudes, behaviors, and malaria-related outcomes _during_ the campaign. This process was accomplished via an “experience” sampling method that gathered data across several survey rounds, for a maximum of 10 waves. We find an overall moderate impact of the intervention on bed net use among mosquito net owners. Overall, the likelihood of sleeping under a bed net both at the individual and household level increased by 3 percentage points (p.p.), a 4.5% increase compared

> 3The campaign cost 202,000 USD and reached users an average of 3.6 times over a 6-month period.

> 4In the context of India, houses constructed with durable materials are referred to as _pucca_ dwellings, while those made from temporary or non-permanent materials are known as _kutcha_ dwellings.

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