# III – Methods

In the following section, the differences in reporting risk reduction behaviors begins with simple pooled ordinary least squares (OLS) estimates of the association between reporting SMS messages and the outcomes of interest. These descriptive regressions demonstrate average differences in reporting between those who report receiving (or relying on) messages, and those who do not. Controls for urban location, log income per capita, and month of interview are cumulatively added.

Then, the direct impact of messages on reported behavior is identified by means of an individual fixed effect estimator. By removing individual and household characteristics that are either almost entirely time-invariant (such as location and gender) or nearly time invariant (such as household type, living space, and connections to services) this approach more clearly establishes the link between SMS messages and relevant time-varying behavioral indicators. The approach accounts for both “observed” characteristics that are enumerated in the survey, as well as “unobserved” sources of heterogeneity. This also accounts for time-invariant dimensions issues relating to locations, such as unobserved amenities. The goal is to estimate a regression equation of the form: <mark>BEHit = αi + βSMSit + βIit + εit Eq. 1</mark> Where BEHit is a measure of household _i_ ’s reported risk mitigation behavior at time _t_ , _αi_ is the timeinvariant household-level component, SMSit is a dummy variable equal to one when a respondent reports SMS messages, and zero otherwise, for household _i_ at time _t_ , and _I_ is time-varying per capita income (included as a control variable where appropriate). Deriving estimates of the terms _<mark>β</mark>_ <mark>for the</mark> SMSi <mark>t dummy variable is the objective of the procedure. Positive values suggest a positive relationship between having received a message in the preceding week (or relying on messages as a primary source of information on COVID-19) and reporting risk mitigation behaviors.</mark> <mark>In the results reported below, th</mark> e robust option in Stata is used throughout to control for potential heteroskedasticity. This precludes accounting for the survey’s clustered sampling design using the SVY package in Stata (which does not allow “robust” option for standard errors). However, I perform a sensitivity check of the results by re-estimating identical models using the SVY prefix (and without the robust option). The results are nearly identical, and the choice does not materially affect any result reported below.

Finally, to establish the appropriateness of the proposed fixed effects approach, I also conduct a Hausman test for each specification, where the null hypothesis is that the preferred model is random effects vs. the proposed fixed effects alternative. The results clarify that the fixed effects approach is preferred in all relevant cases.

# IV – Results

The key results of the study are described in tables 5 and 6. Table 5 reports a series of regression specifications in which the dependent variable measures the number of risk reduction behaviors reported (ranging from 0-9) and the independent variable of interest is a measure of the respondent listing SMS as a primary source of information. A total of 10,963 observations are available for this 9