dwelling types. In particular, among respondents living in solid houses, the coefficient (5.8 p.p.) is very similar to the treatment effect of the MNM campaign estimated in the cluster design (5.5 p.p.). This point out persistent effects of advertising, although we are unable to precisely estimate them due to limited power. Since half of the respondents in the individual study sample lived in treatment districts, their potential exposure to the nationwide campaign might reduce the impact of the individual treatment. In Annex Table D25 we replicate the above regressions on the subsamples of individuals assigned to treatment and control districts during the MNM campaign. The results show that the individual treatment produces higher effects among respondents who were unlikely to be previously exposed to the ad content (i.e., those residing in control districts). For them, the coefficient estimates are around 10-11 p.p., indicating a lift of 18% in solid houses and 16% in non-solid dwellings.

Table 8: Timely treatment seeking in case of high fever (individual-level study) _Y=1 if respondent would seek treatment within 24 hours of the onset of a high_ _<u>fever</u>_

||Any|dwelling t|ype|Solid house|Non-Solid house|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Individual T|0.026<sup>_∗∗_</sup>|0.025<sup>_∗∗_</sup>|0.023|0.026|0.023|
||(0.013)|(0.013)|(0.016)|(0.019)|(0.016)|
|Individual T _×_ Solid house|||0.004|||
||||(0.025)|||
|Solid house|-0.030<sup>_∗∗_</sup>|-0.030<sup>_∗∗_</sup>|-0.031|||
||(0.013)|(0.014)|(0.020)|||
|T|0.006|0.007|0.007|0.001|0.011|
||(0.013)|(0.013)|(0.013)|(0.019)|(0.016)|
|Individual controls||✓|✓|✓|✓|
|Observations|1542|1542|1542|807|735|
|Adj. R-squared|0.004|0.003|0.003|-0.004|0.003|
|Mean in control|0.921|0.921|0.921|0.905|0.937|
|SD in control|0.270|0.270|0.270|0.294|0.243|

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, ownership of a bednet and 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 Table 8 reports effects on individuals’ intentions to seek treatment in the event of a high fever (100.4°F / 38°C or above). Our dependent variable is equal to 1 if the respondent would seek treatment within 24 hours of the onset of a fever. The table shows that the ads were effective at encouraging individuals at seeking treatment promptly. Across all dwelling types (columns 1-2), the estimated coefficients suggest a 2.5 p.p. increase in the share of respondents who would timely seek treatment, a 2.7% lift compared to the control group. Columns (3-5) all seem to suggest the absence of heterogeneous effects across dwelling type. Although not significant because of limited power, the individual treatment effect in each subsample is similar to the overall effect, suggesting that the ad content was effective for both dwellers. Identical results are obtained when we use logistic regression (Annex Table D24).

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