# **4.4 Empirical Specifications**

## **4.4.1 Analysis of Survey Data**

In the survey analysis, each observation is an individual. Respondents in districts where ads were shown are considered “treated” and those in districts where ads were not shown are considered the “control” group. Because survey respondents in treatment districts were not necessarily exposed to the MNM campaign, our results estimate intent-to-treat (ITT) effects. Our workhorse model, estimated via Ordinary Least Squares, takes the following form: where _i_ is an individual residing in district _d_ of state _z_ . _T_ is the treatment indicator, which is randomized across districts. _β_ is our coefficient of interest, which captures the effect of being assigned to the treatment on the outcome _Y_ . Note that when the outcome is binary, the equation describes a Linear Probability Model. **x** and **w** represent, respectively, vectors of individual-level and districtlevel control covariates measured at baseline and potentially correlated with our outcomes. Since the treatment was randomly assigned, incorporating these control variables should not influence our estimates of _β_ . However, their inclusion enhances the efficiency of the estimator. Lastly, _θ_ denotes state fixed-effects, which account for unobserved state-level factors that could impact our outcomes of interest, given the potential influence of such factors in the Indian context.<sup>28</sup> We cluster the standard errors at the district level to account for intra-cluster correlations in the error term _ε_ . We also investigate the robustness of our results to different model specifications (e.g., adding controls and interaction terms) and estimators. For the latter, we estimate a logistic regression for all binary outcomes.

## **4.4.2 Analysis of Administrative Data**

Administrative data were gathered at the subdistrict level, covering a total of 395 subdistricts across the 79 districts included in the analysis. Focusing on smaller areas rather than the entire district reduces measurement error and increases the precision of our estimates. Moreover, the subdistrictlevel analysis also alleviates concerns about health facility records in control districts potentially including outcomes for individuals exposed to the campaign. The smaller scale of subdistricts allows for a more precise matching with our targeted areas.<sup>29</sup> In practice, we use GIS tools to identify

> 28State governments in India play a significant role in the administration of health services, including hospitals, public health programs, and healthcare infrastructure within their territories.

> 29Recall that the implementation of our cluster-level design did not rely on the boundaries of the districts but on a series of circles around their major cities. These circles were used in the control group to identify areas within a district to be excluded from the campaign (see Section 4.1). This implies that some parts of the districts in the control group received ads. While this is not a problem for the survey-based analysis -in fact, respondents were precisely sampled within the circles, it creates potential concerns when analyzing administrative records. In the control group, districtlevel data would reflect records from individuals who were potentially exposed to the campaign, which would confound and downward-bias our estimates. Focusing on subdistricts alleviates this concern, as their smaller dimension allows us to consider those that better coincide with our circles.

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