Republican share of the two-party vote, 2016 and Poverty rate (model-based)
Where Republican share of the two-party vote, 2016 is higher, Poverty rate (model-based) tends to be lower. The link is moderate: -0.42 across 3,094 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 39 states, the middle value of the link is -0.11.
Holds after adjusting
Every place, plotted
One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.
Loading every place…
What each measure is
Republican share of the two-party vote, 2016
The Republican candidate's share of the votes for the two main parties in the 2016 presidential election. Third-party votes are left out, so they do not push it up or down.
MIT Election Data and Science Lab · MIT county presidential returns
Poverty rate (model-based)
The percentage of people in poverty, as an estimate from a statistical model, not a direct count. The Census Bureau combines survey answers with tax and food-assistance records, to get figures that small counties' survey answers cannot give on their own.
Census SAIPE · Census SAIPE SAEPOVRTALL_PT
Highest and lowest
Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.
Counties
By republican share of the two-party vote, 2016
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Highest | ||
| King County, TX | 96.8 | 9.7% |
| Roberts County, TX | 96.3 | 9% |
| Garfield County, MT | 95.1 | 16.9% |
| Banner County, NE | 94.9 | 11.6% |
| McPherson County, NE | 94.8 | 12% |
| Lowest | ||
| District of Columbia, DC | 4.3 | 15.2% |
| Prince George's County, MD | 8.71 | 11% |
| Oglala Lakota County, SD | 8.76 | 37.1% |
| Bronx County, NY | 9.66 | 27.7% |
| San Francisco County, CA | 9.85 | 11.9% |
By poverty rate (model-based)
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Highest | ||
| Issaquena County, MS | 43 | 49.6% |
| Martin County, KY | 90.6 | 48.1% |
| East Carroll Parish, LA | 36.6 | 46.5% |
| Ziebach County, SD | 51 | 46.2% |
| Bolivar County, MS | 33.7 | 38.7% |
| Lowest | ||
| Lincoln County, SD | 65.7 | 3.3% |
| Douglas County, CO | 59.9 | 3.7% |
| Los Alamos County, NM | 37.7 | 3.8% |
| Loudoun County, VA | 41 | 4.1% |
| Morgan County, UT | 84.7 | 4.3% |
States — Republican share of the two-party vote, 2016 is not published for states
By poverty rate (model-based)
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Highest | ||
| Louisiana | — | 18.9% |
| Mississippi | — | 18.3% |
| New Mexico | — | 16.9% |
| West Virginia | — | 16.4% |
| Kentucky | — | 16.1% |
| Lowest | ||
| New Hampshire | — | 7.3% |
| Utah | — | 9% |
| Minnesota | — | 9.2% |
| Colorado | — | 9.4% |
| New Jersey | — | 9.7% |
Metro areas
By republican share of the two-party vote, 2016
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Highest | ||
| Gillette, WY | 92.3 | 8.5% |
| Vernal, UT | 90.8 | 11.1% |
| Pampa, TX | 90.7 | 17% |
| Cullman, AL | 89.7 | 15.5% |
| Borger, TX | 89.2 | 14.3% |
| Lowest | ||
| San Francisco-Oakland-Fremont, CA | 17.8 | 9.22% |
| Santa Cruz-Watsonville, CA | 19.1 | 12.6% |
| Rio Grande City-Roma, TX | 19.3 | 28.8% |
| Taos, NM | 20.4 | 18.5% |
| Eagle Pass, TX | 21.3 | 22.8% |
By poverty rate (model-based)
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Highest | ||
| Cleveland, MS | 33.7 | 38.7% |
| Greenville, MS | 31.5 | 35.5% |
| Forrest City, AR | 44.2 | 34.3% |
| Gallup, NM | 27.3 | 34.3% |
| Selma, AL | 31.1 | 31.4% |
| Lowest | ||
| Los Alamos, NM | 37.7 | 3.8% |
| Heber, UT | 50.6 | 5.05% |
| Appleton, WI | 57.7 | 5.93% |
| Jackson, WY-ID | 39.2 | 6.34% |
| Nantucket, MA | 31.3 | 6.5% |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,113 counties: the five with far more poverty rate (model-based) than their republican share of the two-party vote, 2016 would suggest, and the five with far less.
| Republican share of the two-party vote, 2016 | Poverty rate (model-based) | |
|---|---|---|
| Above the line | ||
| Martin County, KY | 90.6 | 48.1% |
| Issaquena County, MS | 43 | 49.6% |
| Ziebach County, SD | 51 | 46.2% |
| East Carroll Parish, LA | 36.6 | 46.5% |
| Clay County, KY | 88.6 | 37.2% |
| Below the line | ||
| Los Alamos County, NM | 37.7 | 3.8% |
| Falls Church city, VA | 18.5 | 5.1% |
| Loudoun County, VA | 41 | 4.1% |
| Lincoln County, SD | 65.7 | 3.3% |
| Douglas County, CO | 59.9 | 3.7% |
Two series, one county
Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.
Loading…
Every version of the link
Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.
| Before adjusting for anything | -0.129 | 3,094 counties reporting both |
| After adjusting for density, income and education | -0.425 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.425 | size taken out as well as density |
| Also comparing only within each state | -0.469 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | -0.471 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 286 equally weighted counties |
| Middle value inside single states | -0.106 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.257 | uses each county’s rank, not its value, so a few extreme counties cannot create the link |
How much to believe it
Could it be chance?
The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 5.9e-136. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 3.7e-9. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 5.6e-135.
Do nearby places look alike?
Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.41 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.
Did they change together?
Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.
Related pairs
- Returns claiming the earned income credit and Poverty rate (model-based) — +0.71
- Housing insecurity and Republican share of the two-party vote, 2016 — -0.62
- Lack of reliable transportation and Republican share of the two-party vote, 2016 — -0.61
- Share White, not Hispanic and Republican share of the two-party vote, 2016 — +0.61
- Food insecurity and Republican share of the two-party vote, 2016 — -0.60
- Received food stamps and Republican share of the two-party vote, 2016 — -0.58
- Share never married and Republican share of the two-party vote, 2016 — -0.58
- Share now married and Republican share of the two-party vote, 2016 — +0.57
Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Republican share of the two-party vote, 2016 and Poverty rate (model-based)