Share with a bachelor's degree or higher and Premature mortality
Across 3,066 counties, Share with a bachelor's degree or higher and Premature mortality have a link of -0.60, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a weak link of -0.28 remains. 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
Share with a bachelor's degree or higher
The percentage of adults aged 25 and over who have at least a bachelor's degree. It counts degrees people already have, not people who are in college now.
Census ACS 5-year · ACS table B15003_022…B15003_025 (4 cells), divided by population aged 25 and over
Premature mortality
Deaths before age 75 per 100,000 people. It is adjusted so places with older or younger populations can be compared fairly.
County Health Rankings · County Health Rankings v127
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 share with a bachelor's degree or higher
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| Falls Church city, VA | 79.7% | 281 |
| Arlington County, VA | 77.1% | 195 |
| Los Alamos County, NM | 68.2% | 178 |
| Alexandria city, VA | 65.8% | 243 |
| San Miguel County, CO | 65.2% | 188 |
| Lowest | ||
| McDowell County, WV | 5.75% | 1,106 |
| Elliott County, KY | 6.22% | 490 |
| Newton County, TX | 6.23% | 680 |
| Lee County, AR | 6.87% | 844 |
| Reeves County, TX | 6.87% | 564 |
By premature mortality
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| Buffalo County, SD | 6.99% | 1,949 |
| Dewey County, SD | 17.3% | 1,606 |
| Oglala Lakota County, SD | 9.78% | 1,585 |
| Corson County, SD | 19.2% | 1,445 |
| Sioux County, ND | 11.2% | 1,416 |
| Lowest | ||
| Pitkin County, CO | 64% | 140 |
| Teton County, WY | 61.2% | 152 |
| Summit County, CO | 52.5% | 156 |
| Eagle County, CO | 50.3% | 167 |
| San Juan County, WA | 53.4% | 168 |
States
By share with a bachelor's degree or higher
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| District of Columbia | 63.6% | 486 |
| Massachusetts | 46.6% | 303 |
| Colorado | 44.7% | 344 |
| New Jersey | 42.9% | 334 |
| Maryland | 42.7% | 383 |
| Lowest | ||
| West Virginia | 23.3% | 625 |
| Mississippi | 24.2% | 629 |
| Arkansas | 25.1% | 552 |
| Louisiana | 26.6% | 563 |
| Kentucky | 27% | 565 |
By premature mortality
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| Mississippi | 24.2% | 629 |
| West Virginia | 23.3% | 625 |
| Alabama | 27.8% | 569 |
| Kentucky | 27% | 565 |
| Louisiana | 26.6% | 563 |
| Lowest | ||
| Massachusetts | 46.6% | 303 |
| Hawaii | 35.5% | 310 |
| Minnesota | 38.8% | 315 |
| Utah | 36.9% | 317 |
| Connecticut | 41.9% | 323 |
Metro areas
By share with a bachelor's degree or higher
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| Los Alamos, NM | 68.2% | 178 |
| Boulder, CO | 63.9% | 224 |
| Ithaca, NY | 59.3% | 270 |
| Nantucket, MA | 58.4% | 203 |
| Ann Arbor, MI | 58.1% | 280 |
| Lowest | ||
| Town of Pecos, TX | 6.87% | 564 |
| Summerville, GA | 10.7% | 704 |
| Arcadia, FL | 11% | 549 |
| Eufaula, AL-GA | 11.3% | 634 |
| Clewiston, FL | 11.3% | 506 |
By premature mortality
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| Gallup, NM | 12.5% | 1,079 |
| Clarksdale, MS | 19% | 1,070 |
| Middlesborough, KY | 13.9% | 929 |
| Cleveland, MS | 26.6% | 927 |
| Greenwood, MS | 19.5% | 891 |
| Lowest | ||
| Edwards, CO | 50.3% | 167 |
| Los Alamos, NM | 68.2% | 178 |
| Jackson, WY-ID | 57.1% | 184 |
| Breckenridge, CO | 51.3% | 189 |
| Heber, UT | 53.6% | 202 |
Cities and towns — Premature mortality is not published for cities and towns
By share with a bachelor's degree or higher
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| University at Buffalo CDP, NY | 97.3% | — |
| Stanford CDP, CA | 91.1% | — |
| Scarsdale village, NY | 90.7% | — |
| West University Place city, TX | 90.5% | — |
| University Park city, TX | 90.4% | — |
| Lowest | ||
| Binghamton University CDP, NY | 0% | — |
| Corcoran city, CA | 3.16% | — |
| McFarland city, CA | 4.11% | — |
| Mecca CDP, CA | 4.26% | — |
| Good Hope CDP, CA | 4.28% | — |
ZIP codes — Premature mortality is not published for ZIP codes
By share with a bachelor's degree or higher
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Highest | ||
| ZIP 19383 (Chester County, PA) | 98.6% | — |
| ZIP 02210 (Boston city, MA) | 93.1% | — |
| ZIP 53726 (Madison city, WI) | 91.8% | — |
| ZIP 60654 (Chicago city, IL) | 91.8% | — |
| ZIP 10069 (New York city, NY) | 91% | — |
| Lowest | ||
| ZIP 36849 (Auburn city, AL) | 0% | — |
| ZIP 13902 (Binghamton University CDP, NY) | 0% | — |
| ZIP 92254 (Riverside County, CA) | 3.03% | — |
| ZIP 89070 (Clark County, NV) | 3.45% | — |
| ZIP 73108 (Oklahoma City city, OK) | 3.61% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,072 counties: the five with far more premature mortality than their share with a bachelor's degree or higher would suggest, and the five with far less.
| Share with a bachelor's degree or higher | Premature mortality | |
|---|---|---|
| Above the line | ||
| Buffalo County, SD | 6.99% | 1,949 |
| Dewey County, SD | 17.3% | 1,606 |
| Oglala Lakota County, SD | 9.78% | 1,585 |
| Corson County, SD | 19.2% | 1,445 |
| Todd County, SD | 16.6% | 1,413 |
| Below the line | ||
| Aleutians East Borough, AK | 18.1% | 198 |
| Colfax County, NE | 14.1% | 246 |
| Aleutians West Census Area, AK | 17.5% | 229 |
| Clark County, SD | 16.2% | 243 |
| Lyon County, IA | 20.8% | 224 |
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 | -0.599 | 3,066 counties reporting both |
| After adjusting for density, income and education | -0.275 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.268 | size taken out as well as density |
| Also comparing only within each state | -0.277 | 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 | -0.286 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 282 equally weighted counties |
| Middle value inside single states | -0.623 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.383 | 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 = 3.6e-54. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 3.5e-13. 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 2.1e-53.
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 +0.35 (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 share with a bachelor's degree or higher rose most, did premature mortalitymove 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 | 3,043 | -0.08 | -0.08 | 0.002 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,056 | -0.14 | -0.13 | 5.1e-5 |
Measured over the nearest years published: Premature mortality: 2013–2019 for the 2014–2019 row; Premature mortality: 2019–2022 for the 2019–2023 row.
In counties where one changed more, the other tended to move the opposite way, compared with other counties in the same state (r = -0.08). The same holds in the second period. How change is measured.
Related pairs
- Premature mortality and Safety score — -0.67
- Premature mortality and Homicide rate, annual — +0.67
- Median household income and Share with a bachelor's degree or higher — +0.64
- Share American Indian or Alaska Native, not Hispanic and Premature mortality — +0.63
- Independent-living disability and Premature mortality — +0.60
- Threat of utility shut-off and Premature mortality — +0.58
- Premature mortality and Returns claiming the earned income credit — +0.58
- Premature mortality and Disability-free score — -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 Share with a bachelor's degree or higher and Premature mortality