# **5.3 Evidence from Administrative Data**

Table 5 shows the campaign’s impact on malaria incidence, expressed as the number of diagnosed monthly cases per million people recorded by health facilities for each sub-district-month. We report the interaction term (Treatment _×_ Post) as the analysis of the administrative data is based on a difference-in-difference specification (see equation 2). We show a parsimonious specification with no controls, as well as with the results with the inclusion of district-level variables, and additionally controlling for subdistrict-level characteristics.<sup>35</sup> Table 5: Malaria monthly incidence rates <u>(N.</u> of monthly cases/1M <u>people)</u>

||Over|all incid|ence|Ur|ban incide|nce||Rural inci|dence|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Treated _×_ Post|-3.12|-5.66|-4.68|-6.10<sup>_∗_</sup>|-6.96<sup>_∗∗_</sup>|-6.21<sup>_∗∗_</sup>|0.99|-1.47|-2.24|
||(6.37)|(6.25)|(5.37)|(3.41)|(2.92)|(2.86)|(6.02)|(6.37)|(5.79)|
|Subdistrict FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Month and year FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|District controls||✓|✓||✓|✓||✓|✓|
|Subdistrict controls|||✓|||✓|||✓|
|Observations|1498|1498|1498|791|791|791|1338|1338|1338|
|Adj. R-squared|0.189|0.188|0.187|0.451|0.455|0.461|0.151|0.149|0.148|
|Baseline mean incidence|20.89|20.89|20.89|20.89|20.89|20.89|20.89|20.89|20.89|

Notes: Each observation is a subdistrict-month-year. The panel spans from April 2020 to May 2021. T takes value 1 if the subdistrict belongs to a treated district. Post takes value 1 after August 2020, i.e. when the Facebook campaign started. Subdistrict controls include: subdistrict area, share of urban population, elevation, terrain ruggedness and distance to the state capital, all interacted with Post. District controls include total population from the Census as well as the shares of baseline survey respondents living in kutcha dwellings, with university degree, sleeping under mosquito nets, and reporting any malaria cases in the last 5 years and 2 weeks, all interacted with Post. All regressions include state fixed-effects interacted with Post. Standard errors are clustered at the district level. Data come from the Indian Health Management Information System.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Columns (1-3) examine overall incidence across both urban and rural health facilities. Although the coefficients are negative, they are not statistically significant. We observe that the campaign was effective in urban settings but not in rural ones. Column (4) shows that in treated subdistricts the number of monthly urban malaria cases per million people decreased by more than 6 cases in the months during and after the campaign, compared to the previous period. The coefficient is significant at the 90% confidence level and its magnitude and significance are robust to controlling for district-level controls (column 5) and subdistrict-level variables (column 6). Specifically, in the fully-saturated specification, the coefficient is significant at the 95% confidence level and suggests a decrease of 6.2 cases in the number of monthly urban cases per million people, which corresponds to about 30% of the overall monthly malaria incidence rate in the pre-campaign period. Finally, we do not find significant effects of the campaign on rural incidence. Columns (8-9) suggest a potential reduction, but the magnitudes are only a quarter of those observed in the urban sample, and the coefficients are not statistically significant.

> 35District-level controls include total population from the census as well as the shares of baseline survey respondents living in non-solid or kutcha dwellings, with university degree, sleeping under mosquito nets, and reporting any malaria cases in the last 5 years and 2 weeks, all interacted with Post. Subdistrict controls include subdistrict area, share of urban population, elevation, terrain ruggedness and distance to the state capital, all interacted with Post.

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