Table 1.
States sorted by their residuals from the regression model described in the main text
| State | Residuals | State | Residuals | State | Residuals |
|---|---|---|---|---|---|
| AZ | − 0.1482 | ME | − 0.0329 | NE | 0.0267 |
| MD | − 0.0820 | NM | − 0.0220 | OR | 0.0399 |
| LA | − 0.0792 | NH | − 0.0158 | SC | 0.0401 |
| OH | − 0.0747 | WA | − 0.0122 | WI | 0.0419 |
| VA | − 0.0747 | NJ | − 0.0043 | CT | 0.0426 |
| UT | − 0.0632 | CA | − 0.0036 | MA | 0.0437 |
| TX | − 0.0623 | IA | − 0.0022 | MS | 0.0458 |
| NC | − 0.0551 | AL | 0.0027 | OK | 0.0527 |
| IL | − 0.0538 | HI | 0.0137 | MN | 0.0563 |
| TN | − 0.0503 | GA | 0.0181 | FL | 0.0568 |
| PA | − 0.0475 | KY | 0.0203 | KS | 0.0603 |
| WV | − 0.0466 | ID | 0.0217 | NV | 0.0628 |
| RI | − 0.0458 | MO | 0.0239 | IN | 0.0813 |
| CO | − 0.0386 | AR | 0.0244 | MI | 0.1047 |
| NY | 0.1345 |
A state’s residual can be interpreted as how gerrymandered the state is after taking into account the number of districts, with negative residuals indicating greater gerrymandering. Of course, there could be other important covariates in addition to population size