Per capita income and Motor vehicle crash deaths
Across 2,489 counties, Per capita income and Motor vehicle crash deaths have a link of -0.59, 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.
Loading every place…
What each measure is
Per capita income
All the money income in the area divided by everyone who lives there, children included. It is an average, so a few high earners pull it up, unlike the middle household income.
Census ACS 5-year · ACS table B19301_001
Motor vehicle crash deaths
Deaths in car and other motor vehicle crashes per 100,000 people.
County Health Rankings · County Health Rankings v039
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 income
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| New York County, NY | $94,922 | 2.71 |
| Marin County, CA | $90,408 | 5.57 |
| San Francisco County, CA | $90,285 | 4.56 |
| Arlington County, VA | $88,024 | 2.73 |
| San Mateo County, CA | $81,980 | 5.53 |
| Lowest | ||
| Oglala Lakota County, SD | $11,650 | 86.4 |
| Todd County, SD | $13,849 | 80.6 |
| Wolfe County, KY | $15,253 | 41 |
| McDowell County, WV | $16,599 | 27.7 |
| Perry County, AL | $16,960 | 30.6 |
By motor vehicle crash deaths
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| Big Horn County, MT | $22,453 | 90 |
| Oglala Lakota County, SD | $11,650 | 86.4 |
| Todd County, SD | $13,849 | 80.6 |
| Jackson County, SD | $17,120 | 76.3 |
| Dewey County, SD | $21,940 | 68.3 |
| Lowest | ||
| New York County, NY | $94,922 | 2.71 |
| Arlington County, VA | $88,024 | 2.73 |
| Loudoun County, VA | $70,133 | 3.46 |
| Kings County, NY | $46,057 | 3.66 |
| Richmond County, NY | $44,368 | 3.83 |
States
By per capita income
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| District of Columbia | $75,253 | 6.39 |
| Massachusetts | $56,284 | 6.01 |
| Connecticut | $54,409 | 8.54 |
| New Jersey | $53,118 | 6.98 |
| Maryland | $51,689 | 9.5 |
| Lowest | ||
| Mississippi | $30,529 | 25.5 |
| West Virginia | $32,949 | 15.9 |
| Arkansas | $33,147 | 19.8 |
| Louisiana | $34,211 | 18 |
| New Mexico | $34,823 | 19.8 |
By motor vehicle crash deaths
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| Mississippi | $30,529 | 25.5 |
| South Carolina | $37,993 | 20.8 |
| Alabama | $34,835 | 20.7 |
| Arkansas | $33,147 | 19.8 |
| New Mexico | $34,823 | 19.8 |
| Lowest | ||
| New York | $49,520 | 5.9 |
| Massachusetts | $56,284 | 6.01 |
| District of Columbia | $75,253 | 6.39 |
| Rhode Island | $45,919 | 6.89 |
| New Jersey | $53,118 | 6.98 |
Metro areas
By per capita income
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| San Jose-Sunnyvale-Santa Clara, CA | $75,895 | 7.35 |
| San Francisco-Oakland-Fremont, CA | $72,306 | 6.65 |
| Bridgeport-Stamford-Danbury, CT | $69,642 | 5.09 |
| Jackson, WY-ID | $65,790 | 24 |
| Heber, UT | $65,333 | 8.92 |
| Lowest | ||
| Rio Grande City-Roma, TX | $18,063 | 13.2 |
| Gallup, NM | $19,580 | 42.3 |
| Middlesborough, KY | $20,463 | 29 |
| Deming, NM | $21,871 | 29.8 |
| Eagle Pass, TX | $21,952 | 13.3 |
By motor vehicle crash deaths
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| Palatka, FL | $26,908 | 45.5 |
| Gallup, NM | $19,580 | 42.3 |
| Lumberton, NC | $22,925 | 41.4 |
| Show Low, AZ | $24,347 | 40.2 |
| Selma, AL | $24,368 | 39.2 |
| Lowest | ||
| Amherst Town-Northampton, MA | $45,006 | 5.04 |
| New York-Newark-Jersey City, NY-NJ | $54,510 | 5.04 |
| Bridgeport-Stamford-Danbury, CT | $69,642 | 5.09 |
| Boston-Cambridge-Newton, MA-NH | $61,389 | 5.14 |
| Pullman, WA | $31,884 | 5.54 |
Cities and towns — Motor vehicle crash deaths is not published for cities and towns
By per capita income
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| Palm Beach town, FL | $211,810 | — |
| Atherton town, CA | $203,140 | — |
| Highland Park town, TX | $187,200 | — |
| Scarsdale village, NY | $181,920 | — |
| Cherry Hills Village city, CO | $176,850 | — |
| Lowest | ||
| Binghamton University CDP, NY | $2,240 | — |
| Clemson University CDP, SC | $4,362 | — |
| California Polytechnic State University CDP, CA | $5,540 | — |
| Stony Brook University CDP, NY | $6,166 | — |
| Notre Dame CDP, IN | $7,117 | — |
ZIP codes — Motor vehicle crash deaths is not published for ZIP codes
By per capita income
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Highest | ||
| ZIP 10007 (New York city, NY) | $209,000 | — |
| ZIP 10282 (New York city, NY) | $207,030 | — |
| ZIP 94027 (Atherton town, CA) | $203,240 | — |
| ZIP 94105 (San Francisco city, CA) | $196,160 | — |
| ZIP 33480 (Palm Beach town, FL) | $194,910 | — |
| Lowest | ||
| ZIP 32816 (University CDP, FL) | $1,570 | — |
| ZIP 13902 (Binghamton University CDP, NY) | $2,240 | — |
| ZIP 33620 (Tampa city, FL) | $2,713 | — |
| ZIP 79406 (Lubbock city, TX) | $2,722 | — |
| ZIP 32612 (Gainesville city, FL) | $2,878 | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,491 counties: the five with far more motor vehicle crash deaths than their per capita income would suggest, and the five with far less.
| Per capita income | Motor vehicle crash deaths | |
|---|---|---|
| Above the line | ||
| Big Horn County, MT | $22,453 | 90 |
| Oglala Lakota County, SD | $11,650 | 86.4 |
| Meade County, KS | $44,359 | 62.8 |
| Todd County, SD | $13,849 | 80.6 |
| Jackson County, SD | $17,120 | 76.3 |
| Below the line | ||
| Madison County, ID | $23,024 | 5.22 |
| Bronx County, NY | $26,956 | 4.29 |
| Hidalgo County, TX | $22,005 | 9.43 |
| Harrisonburg city, VA | $27,957 | 5.94 |
| Starr County, TX | $18,063 | 13.2 |
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.
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 | -0.588 | 2,489 counties reporting both |
| After adjusting for density, income and education | -0.011 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.027 | size taken out as well as density |
| Also comparing only within each state | +0.058 | 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 | +0.122 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 280 equally weighted counties |
| Middle value inside single states | -0.534 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.035 | 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 = 0.600. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.810. 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 0.630.
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 +0.34 (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?
The figures above compare counties at one moment. This asks a different question: where per capita income rose most, did motor vehicle crash deathsmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 2,380 | +0.06 | +0.05 | 0.243 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 2,410 | +0.05 | +0.02 | 0.625 |
Measured over the nearest years published: Motor vehicle crash deaths: 2013–2019 for the 2014–2019 row; Motor vehicle crash deaths: 2019–2022 for the 2019–2023 row.
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Median household income and Per capita income — +0.77
- Motor vehicle crash deaths and Years of potential life lost — +0.56
- Motor vehicle crash deaths and Premature mortality — +0.54
- Per capita income and Share with a bachelor's degree or higher — +0.54
- Child mortality and Motor vehicle crash deaths — +0.53
- Motor vehicle crash deaths and Life expectancy — -0.51
- Motor vehicle crash deaths and Health score — -0.50
- Motor vehicle crash deaths and Homicide rate, annual — +0.50
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 income and Motor vehicle crash deaths