Per capita personal income and Mobility disability
Across 2,882 counties, Per capita personal income and Mobility disability have a link of -0.52, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and what is left could easily be chance. Most of the first figure came from both measures following how urban, rich and educated a place is, not from a link with each other.
Mostly gone 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.
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What each measure is
Per capita personal income
Personal income divided by the number of residents, children included.
BEA Regional Accounts · BEA CAINC1 line 3
Mobility disability
The estimated percentage of adults who report serious difficulty walking or climbing stairs. It is adjusted so places with older or younger populations can be compared fairly. It is an estimate from a statistical model, not a direct count from a local survey.
CDC PLACES · PLACES MOBILITY, age-adjusted
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 per capita personal income
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| Teton County, WY | $500,560 | 7.9% |
| Summit County, UT | $268,220 | 8% |
| Pitkin County, CO | $261,950 | 7.5% |
| New York County, NY | $207,570 | 10.5% |
| Blaine County, ID | $193,220 | 10.6% |
| Lowest | ||
| Wheeler County, GA | $24,985 | 17.6% |
| Crowley County, CO | $26,114 | 14.1% |
| Madison County, ID | $28,260 | 15.9% |
| Union County, FL | $28,509 | 16.5% |
| Lake County, TN | $32,847 | 23% |
By mobility disability
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | $36,693 | 28.8% |
| Todd County, SD | $38,427 | 27.2% |
| East Carroll Parish, LA | $36,438 | 26.3% |
| Humphreys County, MS | $44,359 | 25.4% |
| Dimmit County, TX | $58,167 | 25.2% |
| Lowest | ||
| Los Alamos County, NM | $92,765 | 7% |
| Douglas County, CO | $103,590 | 7.1% |
| King County, WA | $122,670 | 7.2% |
| Morris County, NJ | $114,980 | 7.4% |
| Pitkin County, CO | $261,950 | 7.5% |
States — Mobility disability is not published for states
By per capita personal income
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| District of Columbia | $106,640 | — |
| Connecticut | $90,235 | — |
| Massachusetts | $89,947 | — |
| Wyoming | $83,038 | — |
| New York | $81,932 | — |
| Lowest | ||
| Mississippi | $49,661 | — |
| West Virginia | $52,963 | — |
| Alabama | $54,612 | — |
| New Mexico | $55,457 | — |
| Kentucky | $55,667 | — |
Metro areas
By per capita personal income
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| Jackson, WY-ID | $349,320 | 8.88% |
| Heber, UT | $186,820 | 8.45% |
| Hailey, ID | $163,400 | 11.4% |
| San Jose-Sunnyvale-Santa Clara, CA | $146,490 | 9.42% |
| Naples-Marco Island, FL | $143,710 | 10.6% |
| Lowest | ||
| Rexburg, ID | $32,063 | 15.4% |
| Rio Grande City-Roma, TX | $33,211 | 24.1% |
| McAllen-Edinburg-Mission, TX | $34,393 | 19.7% |
| Zapata, TX | $34,712 | 23.5% |
| Arcadia, FL | $35,254 | 19% |
By mobility disability
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | $33,211 | 24.1% |
| Clarksdale, MS | $48,095 | 23.6% |
| Zapata, TX | $34,712 | 23.5% |
| Gallup, NM | $35,838 | 21.9% |
| Greenville, MS | $48,488 | 21.9% |
| Lowest | ||
| Los Alamos, NM | $92,765 | 7% |
| Boulder, CO | $101,120 | 8% |
| Nantucket, MA | $122,580 | 8.1% |
| Barre, VT | $74,726 | 8.3% |
| Burlington-South Burlington, VT | $72,870 | 8.36% |
Cities and towns — Per capita personal income is not published for cities and towns
By mobility disability
| Per capita personal income | Mobility disability | |
|---|---|---|
| Highest | ||
| East Bakersfield CDP, CA | — | 28.9% |
| Lamont CDP, CA | — | 28.5% |
| Doffing CDP, TX | — | 28.2% |
| Tallulah city, LA | — | 28% |
| Benton Harbor city, MI | — | 27.5% |
| Lowest | ||
| Short Hills CDP, NJ | — | 4.9% |
| Old Greenwich CDP, CT | — | 5.3% |
| Sammamish city, WA | — | 5.3% |
| Glen Ridge borough, NJ | — | 5.3% |
| Riverside CDP, CT | — | 5.4% |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,903 counties: the five with far more mobility disability than their per capita personal income would suggest, and the five with far less.
| Per capita personal income | Mobility disability | |
|---|---|---|
| Above the line | ||
| Teton County, WY | $500,560 | 7.9% |
| Oglala Lakota County, SD | $36,693 | 28.8% |
| Todd County, SD | $38,427 | 27.2% |
| Dimmit County, TX | $58,167 | 25.2% |
| Summit County, UT | $268,220 | 8% |
| Below the line | ||
| Harding County, SD | $50,490 | 9.3% |
| Hinsdale County, CO | $73,076 | 7.6% |
| San Juan County, CO | $54,168 | 9.2% |
| Addison County, VT | $65,352 | 8.3% |
| Lake County, CO | $54,433 | 9.2% |
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.
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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 | -0.522 | 2,882 counties reporting both |
| After adjusting for density, income and education | +0.036 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.046 | size taken out as well as density |
| Also comparing only within each state | +0.054 | 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 | +0.129 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 259 equally weighted counties |
| Middle value inside single states | -0.614 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.179 | 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 = 0.053. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.440. 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 0.067.
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 +0.58 (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
- Mobility disability and Material security score — -0.80
- Mobility disability and Returns claiming the earned income credit — +0.78
- Per capita personal income and Mean adjusted gross income per return — +0.76
- Mobility disability and Homicide rate, annual — +0.66
- Mobility disability and Homicide rate — +0.59
- Mobility disability and Average test score — -0.58
- Mobility disability and Premature mortality — +0.57
- Mobility disability and Social connection score — -0.56
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 Per capita personal income and Mobility disability