Table 1: Baseline cluster characteristics by experimental condition

||Treatment<br>_Mean_<br>(1)|Control<br>_Mean_<br>(2)|Normalized<br>Diference<br>(3)|_T-test_<br>(_p-value)_<br>(4)|
|---|---|---|---|---|
|**Cluster covariates**|||||
|N. of clusters|39|40|||
|Population (Million)|2.756|2.629|0.077|0.629|
|Share living in solid houses|0.502|0.494|0.078|0.623|
|Share with university degree|0.598|0.593|0.063|0.693|
|Share unemployed|0.439|0.446|-0.103|0.519|
|Share with malaria in last 2 weeks|0.017|0.016|0.072|0.652|
|Share with malaria in last 5 years|0.190|0.184|0.051|0.750|
|Average click-through rate (%)|1.830|1.847|-0.048|0.764|
|Average cost per mille (USD)|0.764|0.758|0.050|0.756|
|Average ad cost per survey started (USD)|0.252|0.241|0.072|0.650|
|Average ad cost per survey completed (USD)|0.820|0.858|-0.052|0.744|

Notes: The table reports clusters’ characteristics measured at baseline, by experimental condition. Population numbers come from census data. The normalized difference is the difference between the two means divided by the square root of the sum of the variances of the two variables.

distance from the nearest medical center and caste (Column 3 of Annex Table D2) and provides a convenient mapping into administrative data on malaria incidence, which are disaggregated across urban and rural locations. In India, non-solid and semi-solid dwellings make up approximately 15% in urban areas and around 50% in rural areas (India DHS).<sup>21</sup> To overcome the challenge that Facebook does not offer the option to target ads by dwelling type, we created Lookalike Audiences in Facebook and dynamically increased budget to them in districts where non-solid dwellers were underrepresented.<sup>22</sup> The Lookalike Audience tool allows advertisers to connect with fresh users who mirror their current customers or desired target audience. Drawing from our formative research surveys, we provided Facebook with the “seed audience” of individuals that lived in non-solid dwellings. Leveraging its rich data and algorithms, Facebook then assembles a Lookalike Audience by extrapolating patterns identified within the seed audience.

We used this procedure to ensure that in each district we recruited a sufficient sample size for measuring the effects of the campaign on those most at risk of the disease. The benefits of this stratification can be seen in Annex Figure C3, which shows an improvement in the share of non-solid dwellers per district and a reduction in its variance across districts as a result of the optimization. Table 1 shows that, eventually, the stratified recruitment and cluster-randomization resulted in a sample with two important features: (1) baseline cluster characteristics are balanced across treatment and control districts, a condition for internal validity; (2) the average share of cluster population living in solid houses is around 50%, which allows the estimation of conditional treatment effects for both types of dwellings. The table also provides insights into the main metrics of our recruitment campaign, pointing out an average ad cost of 85 cents per final survey respondent.

> 21According to the India DHS, the distribution of housing types in urban areas is as follows: 85.8% solid, 13.3% semi-solid, and 0.9% non-solid houses. In rural areas, these percentages change to 48.7% solid, 44.8% semi-solid, and 6.5% non-solid houses, respectively.

> 22By leveraging real-time response data, Virtual Lab optimized the budget allocation across ad sets to get all the strata filled as quickly as possible.

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