Table 1.
Regression discontinuity estimates of changes in residential electricity usage (log) due to COVID-19 mandates: global polynomial results
| (1) |
(2) |
(3) |
(4) |
|
|---|---|---|---|---|
| Arizona (Global) |
Illinois (Global) |
Arizona (Local) |
Illinois (Local) |
|
| School closure | School closure | School closure | School closure | |
| COVID-19 Mandates | ||||
| 0.053 ∗∗∗ | 0.044 ∗∗∗ | 0.013 ∗∗∗ | 0.031 ∗∗∗ | |
| (0.004) | (0.002) | (0.003) | (0.002) | |
| Weather-related variables | Yes | Yes | Yes | Yes |
| Month FE | Yes | No | Yes | No |
| Day-of-week FE | Yes | Yes | Yes | Yes |
| Holiday FE | Yes | No | Yes | No |
| Hourly FE | Yes | Yes | Yes | Yes |
| Account FE | Yes | Yes | Yes | Yes |
| Observations | 19,998,526 | 30,283,001 | 4,962,531 | 8,793,256 |
| Number of households | 7,004 | 40,771 | 7,004 | 40,771 |
Notes: 1. Weather-related control variables include temperature (in a restricted cubic spline format), precipitation (linear and quadratic format), air pressure, relative humidity, and wind speed.
Standard errors, clustered by account id, are in parentheses.
2. Columns (3) and (4) are local linear approaches to validate the results from the polynomial approach. In this setting, a narrower bandwidth of 15 days before and after the policy effective dates (for Arizona) and 4 days (for Illinois) are adopted. We adopted different bandwidths for Arizona and Illinois because of the different periods between the school closure day and stay-at-home order date (AZ – 15 days, IL – 4 days). We use different bandwidths for the local linear regression because we do not want to include the impact of the stay-at-home order in the evaluation of the impact of the school closure date. Additionally, we conducted robustness checks with different bandwidths of the local RD models in Table S11.
∗∗∗ Significant at the 1% level. ∗∗ Significant at the 5% level. ∗ Significant at the 10% level.