night: the estimated marginal effect is around 7.5 and 8 p.p., and implies an increase in the probability of sleeping under a mosquito net from 66% in the control group to 73% in the treatment group, a 11% increase. This effect maintains its effect size and statistical significance across all specifications and is robust to the inclusion of controls (columns 1-3). Note that the impact of the remarketing campaign is larger compared to the estimated treatment effect of the nationwide MNM campaign described in Section 5.2. A plausible explanation is that the coefficients in the cluster design represent intent-to-treat effects, meaning only a subset of respondents (whose exact number is unknown to us) in our longitudinal sample were exposed to the treatment. In contrast, the individual-level design ensures that the majority of the individuals in the treated group (approximately 90%) saw the ads at least once. Therefore, the effects observed at the individual level are larger and more aligned with treatment-on-the-treated estimates.

Table 7: Sleeping under bed net (individual-level study) _Y=1 if respondent has slept under bed net the_ _<u>previous</u> night_

||Any|dwelling ty|pe|Solid house|Non-Solid house|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Individual T|0.075<sup>_∗∗∗_</sup>|0.079<sup>_∗∗∗_</sup>|0.079<sup>_∗∗_</sup>|0.076<sup>_∗_</sup>|0.081<sup>_∗∗_</sup>|
||(0.029)|(0.029)|(0.038)|(0.043)|(0.039)|
|Individual T _×_ Solid house|||0.000<br>(0.057)|||
|Solid house|-0.115<sup>_∗∗∗_</sup>|-0.080<sup>_∗∗_</sup>|-0.080<sup>_∗_</sup>|||
||(0.029)|(0.031)|(0.044)|||
|Treatment|0.019|0.021|0.021|0.058|-0.015|
||(0.029)|(0.029)|(0.029)|(0.043)|(0.039)|
|Individual controls||✓|✓|✓|✓|
|Observations|1043|1043|1043|513|530|
|Adj. R-squared|0.018|0.033|0.032|0.026|0.030|
|Mean in control|0.654|0.654|0.654|0.595|0.706|
|SD in control|0.476|0.476|0.476|0.492|0.456|

Each observation is an individual. Indivitual T is the individual treatment indicator for the remarketing campaign. T is the indicator of the district-level treatment assignment. Solid house is a binary indicator equal to 1 if the individual lives in a dwelling made of stones, bricks, cement or concrete. Individual controls include gender, age, household size, caste, education, distance to the medical center, employment and student status, religion, presence of pregnant woman in the house and ownership of an air conditioner. Heteroskedasticity-robust standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Columns (3-5) of Table 7 provide a direct test for our mechanisms and support the hypothesis that the MNM campaign did not sufficiently reach those living in non-solid dwellings. In fact, unlike in the cluster-level treatment, column (3) exhibits no heterogeneous treatment effects of the remarketing campaign across dwelling types, suggesting that - _conditional on receiving the ads- the campaign is similarly effective for both types of dwellers._ This is further supported by columns (4-5), which show a positive and similar impact of the individual treatment on bed net use for both solid and non-solid dwellers (around 8 p.p.). Given the baseline differences in the dependent variable across dwelling types, the estimated effect is of 13% in solid-houses and of 11.5% in non-solid dwellings. Estimates from a logistic model yield identical marginal effects (Annex Table D23).

Note that in all the regressions we also include the cluster-level treatment indicator. Although statistically insignificant, the magnitude and sign of the estimated coefficients are different across 31