Table A10.
Stringency index and air pollution – Principal Component Analysis.
| Bandwidth | Air pollution: NO2 |
Air pollution: PM2.5 |
||||
|---|---|---|---|---|---|---|
| Optimal bandwidth | Optimal bandwidth +10 days | Optimal bandwidth −10 days | Optimal bandwidth | Optimal bandwidth +10 days | Optimal bandwidth −10 days | |
| Lockdown = 1 | −0.331∗∗ (0.160) |
−0.601∗∗∗ (0.146) |
−0.088 (0.178) |
−2.570∗∗∗ (0.462) |
−3.115∗∗∗ (0.429) |
−1.401∗∗∗ (0.486) |
| Means before lockdowns | 23.281 | 23.281 | 23.281 | 64.824 | 64.824 | 64.824 |
| Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Country and time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 260,241 | 300,811 | 218,404 | 79,623 | 89,384 | 69,921 |
Notes: ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1. Results of RDD using the optimal bandwidths based on Imbens and Kalyanaraman (2012). Clustered standard errors in parentheses are robust to within-day and within-country serial correlation. Control variables are daily temperature and rainfall (humidity for station-based data). Stringency index is constructed using Principal Component Analysis. For all dimensions of stringency index, see Table B2 (Appendix B).