Table 6.
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
(7) |
|
---|---|---|---|---|---|---|---|
Log COVID-19 Deaths | |||||||
Baseline Model | Limiting to Comparable Counties with 50%–70% Essential Workers | Limited to Counties with Population Density <2000 in the Treatment Group | Dropping States Near the Mexican Border With Possible Smoke Exposure | Limiting to Counties in States with Both Treated and Control Counties | Limiting to Only Essential TRIs | Limiting to Only TRIs Emitting Air Pollution | |
Treated Counties After the Rollback | 0.1055∗∗∗ (0.0272) |
0.1371∗∗∗ (0.0377) |
0.0429∗ (0.0238) |
0.1098∗∗∗ (0.0298) |
0.0891∗∗∗ (0.0270) |
0.2307∗∗∗ (0.0293) |
0.1208∗∗∗ (0.0244) |
With County, Month, and Day of Week Fixed Effects | X | X | X | X | X | X | X |
Observations | 84126 | 45236 | 79162 | 73155 | 82452 | 84126 | 84126 |
Notes: Columns 1–7 present the results for being in a county with 6 or more TRI sites after the EPA’s rollback with the log of COVID-19 deaths as the outcome. Column 1 replicates our results from Table 3. Column 2 presents the results when limiting to counties with similar percentages of essential workers. Column 3 presents estimates in which we drop treated counties with population densities of more than 2000. Column 4 presents results when we drop counties with potential seasonal smoke exposure. Column 5 presents results when the sample is limited to states with both treated and control counties. The results in column 6 limit the analysis to essential TRI sites. Column 7 limits the sample to include only TRI sites that emit air pollution. All models control for being after the rollback, social distancing, stay at home orders, re-openings, mask mandates, days since the first COVID death, total tests administered, daily number of confirmed COVID-19 cases, weather, and day of the week, county and month fixed effects. Standard errors are clustered at the county level and are in parenthesis. Coefficients labeled as ∗∗∗, ∗∗, and ∗ are statistically significant at the 1, 5, and 10 percent levels, respectively.