and control subdistricts should have been the same. This assumption is plausible due to the clusterlevel randomization, and we provide evidence to support it. By grouping observations in different time periods, we can depict the differences in malaria incidence between treated and control localities over time. The result, shown in Figure 5, allow us to detect potentially diverging trends before the campaign (i.e., between April and August 2020). Importantly, the figure supports the parallel trend assumption, as trends in incidence rates across treated and control subdistricts are comparable before the campaign, providing reassurance on the identification strategy underlying the analysis of administrative data. The figure also confirms that the campaign significantly impacted urban malaria incidence while having negligible effects on rural cases.

# **5.4 Placebo Regressions and Robustness Checks**

In this section, we present a series of placebo regressions and robustness checks for our results. As demonstrated, our findings remain consistent and reliable across these various checks.

For our survey-based findings, we conducted the following specification checks and placebo exercises. First, we assessed the sensitivity of our results to the inclusion of individual- and districtlevel covariates, and reported them in the tables above (and in the Appendix). The coefficients showed minimal changes, and the effects remained stable. Second, we estimated logistic regressions for all the binary outcomes presented above. The results are shown in Annex Tables D12-D14. The estimated marginal effects for the treatment indicator reported at the bottom of the tables are identical to the OLS estimates presented so far, validating the use of a Linear Probability Model in this context. Third, since we measured bed net usage at baseline (i.e., before the campaign started), we can conduct placebo regressions to further validate the randomization. Annex Table D15 shows the results of this exercise, where the dependent variable measured in July and August 2020 is regressed on the treatment indicator. The table provides strong evidence in favor of a correct randomization: all coefficients are small and statistically insignificant. Moreover, there is no evidence of heterogeneous treatment effects across dwelling type. Fourth, we measured individuals’ concerns about getting COVID-19 in the next year and used this as a placebo outcome, since the campaign should not directly affect it. Consistently, Annex Table D16 shows no impact on risk perceptions about getting COVID-19. This alleviates the potential concerns that our results are driven by reporting bias.

For our health facility data, we test the robustness of our results to different models, samples, and outcome definitions. First, as shown in Table 5, the coefficients of our impact estimators showed minimal changes with or without district- and subdistrict-level controls. Second, Annex Table D17 shows the sensitivity of our estimates to five choices of the population thresholds used to identify subdistricts to include in our sample. The impact of the ad campaign on urban incidence is stable and mostly significant across all subsamples, with estimated reductions in cases ranging between 20% and 30% of the baseline average incidence rate. Conversely, the sign of the coefficient on rural incidence is inconsistent and remains statistically insignificant. Third, in Annex Table D18 we consider the absolute number of malaria cases as dependent variable, and include the subdistrict 27