Vaccine increase per county.
REGRESSIONS, REGRESSION
Regression results. Sample size is 151,945 county-date observations. All regressions include fixed effects at the county and date levels. In columns 1 and 2, the ITT effect corresponds to the OLS-estimated coefficient on the interaction of a treatment assignment dummy (Treat) with a dummy for dates after October 14 (Post), the start of the campaign. Column 1 also includes the interaction of Post with county population. Column 2 replaces this with interactions of county population with (i) flexible dummies for each date within 2 weeks before to 2 weeks after the campaign (omitting the date before the campaign started), (ii) a dummy variable for 2 weeks or more before, and (iii) a dummy variable for 2 weeks or more after. Columns 3 and 4 report the IV-estimated coefficient on the interaction of the number of ads the county received (in 1000s) with Post, with this interaction instrumented for by Treat × Post. Column 3 mimics column 1 in controlling for differential trends by population, and column 4 mimics column 2. “***,” “**,” and “*” indicate significance (from a one-tailed test) at the 0.01, 0.05, and 0.10 levels. Standard errors, reported in parentheses below each estimate, are clustered at the county level. Randomization inference Each column in As we discuss in Materials and Methods, these confidence bounds are constructed under our most conservative clustering approach (county-level clustering). We obtain smaller standard errors (and hence smaller We discuss here implications of the point estimates, keeping in mind that, based on the standard errors, we cannot reject much smaller effects. The estimate in column 1 implies that, among the 1014 treatment counties, the average county saw a causal increase of 102.6 vaccines due to the campaign. Aggregating across all treatment counties, the total effect was an increase of 104,036 vaccines. Because our total ad budget spent in these counties was $96,408.56, the estimated vaccines per dollar spent was 1.08 (standard error of 0.828), with similar results in column 2. In either case, this implies that $1 in advertising spending yielded one more shot in the arm. It bears noting that, at the lower 90% confidence bound, the estimated effect is quite small: Only 0.017 vaccines resulted from $1 in ads, or $58.8 dollars per vaccine. At our 90% lower confidence bound, this is still more cost effective than the point estimates of $68 and $82 in Estimates from Barro (Columns 3 and 4 of As described in Materials and Methods, 1000 ad impressions cost us $8.55 on average (the cost per mille, or “CPM” in advertising lingo). These numbers therefore imply an alternative estimate of the number of vaccines per dollar, at 1.01. The estimate in column 4 implies a similar estimate of cost effectiveness, at 0.99 vaccines per dollar spent. Thus, regardless of whether we rely on the estimated ITT effect or the ACR, our point estimates imply that $1 in advertising spending persuaded one (or slightly more than one) viewer to get the vaccine. Supplementary Materials (SM) Section A presents various robustness checks on the results in this section.So far, we have conceptualized our outcome measure as a level: the number of people in a county who are vaccinated. An alternative approach would be to conceptualize the dependent measure as a rate: the percent of residents in a county who are vaccinated. We report results from this analysis in 