Vaccine increase per county: Heterogeneous effects and causal responses.
Table reports the difference in the estimated effect in low versus high counties based on a given county characteristic. This characteristic is the 2016 Trump vote share in columns 1 and 2, the fraction of county residents with a college degree in columns 3 and 4, and the fraction of county residents who are white in columns 5 and 6. High refers to counties that are above the median level for that characteristic, and low refers to below the median, where the median is computed across counties in our sample. In odd columns, the effect is the ITT effect and in even columns it is the ACR. “***,” “**,” and “*” indicate significance (from a two-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 We also repeat our heterogeneous treatment effect analysis using instead the vaccine rate within each county as the dependent variable. We again find differential effects in terms of Trump vote share: In counties that are below the median, the ITT effect is 1.775 percentage points larger than in counties with above-median Trump share. The ACR analysis suggests that an increase of 1 more ad per 100 county residents increases the percent vaccinated by 0.09 percentage points more in low-Trump-share counties than in high-Trump-share counties. Each of these effects is significant at the 0.05 level in a two-tailed test. The results are reported in table S7. Effects separated by race or education again show no statistically significant differences, suggesting that the strongest heterogeneous effects of those we examine are the Trump share results.