placestats

Share with a bachelor's degree or higher and Public-good pages per 1,000 users

Where Share with a bachelor's degree or higher is higher, Public-good pages per 1,000 users tends to be higher. The link is weak: +0.40 across 3,076 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 40 states, the middle value of the link is +0.10.

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.

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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

Public-good pages per 1,000 users

Facebook pages for community groups and public-good efforts, per thousand Facebook users. It is per user, not per resident.

Social Capital Atlas · Opportunity Insights civic_organizations_county

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 higherPublic-good pages per 1,000 users
Highest
Arlington County, VA77.1%0.014
Los Alamos County, NM68.2%0.0314
Alexandria city, VA65.8%0.0256
San Miguel County, CO65.2%0.0384
Howard County, MD64.5%0.017
Lowest
McDowell County, WV5.75%0.0127
Elliott County, KY6.22%0.00916
Newton County, TX6.23%0.0168
Lee County, AR6.87%0.0244
Reeves County, TX6.87%0.00942

By public-good pages per 1,000 users

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
Highest
Douglas County, MO15.5%0.195
San Juan County, CO47.2%0.112
Lewis County, ID20%0.106
Alpine County, CA42.1%0.0828
Cheyenne County, CO24.4%0.0822
Lowest
Humphreys County, MS19.4%0.00349
Winkler County, TX10%0.00361
Jefferson County, MS19.9%0.00375
Maverick County, TX15.3%0.00381
Starr County, TX13.3%0.00414
States — Public-good pages per 1,000 users is not published for states

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
Highest
District of Columbia63.6%—
Massachusetts46.6%—
Colorado44.7%—
New Jersey42.9%—
Maryland42.7%—
Lowest
West Virginia23.3%—
Mississippi24.2%—
Arkansas25.1%—
Louisiana26.6%—
Kentucky27%—
Metro areas

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
Highest
Los Alamos, NM68.2%0.0314
Boulder, CO63.9%0.0233
Ithaca, NY59.3%0.0406
Nantucket, MA58.4%0.0312
Ann Arbor, MI58.1%0.0246
Lowest
Town of Pecos, TX6.87%0.00942
Summerville, GA10.7%0.0119
Arcadia, FL11%0.00944
Eufaula, AL-GA11.3%0.0156
Clewiston, FL11.3%0.0107

By public-good pages per 1,000 users

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
Highest
Vermillion, SD46%0.0451
Port Townsend, WA43.3%0.0442
Vineyard Haven, MA49%0.0418
Barre, VT47%0.0411
Ithaca, NY59.3%0.0406
Lowest
Eagle Pass, TX15.3%0.00381
Rio Grande City-Roma, TX13.3%0.00414
Laredo, TX20.9%0.00422
Provo-Orem-Lehi, UT43.4%0.00431
Zapata, TX13.3%0.00436
Cities and towns — Public-good pages per 1,000 users is not published for cities and towns

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
Highest
University at Buffalo CDP, NY97.3%—
Stanford CDP, CA91.1%—
Scarsdale village, NY90.7%—
West University Place city, TX90.5%—
University Park city, TX90.4%—
Lowest
Binghamton University CDP, NY0%—
Corcoran city, CA3.16%—
McFarland city, CA4.11%—
Mecca CDP, CA4.26%—
Good Hope CDP, CA4.28%—
ZIP codes — Public-good pages per 1,000 users is not published for ZIP codes

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherPublic-good pages per 1,000 users
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 3,083 counties: the five with far more public-good pages per 1,000 users 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 higherPublic-good pages per 1,000 users
Above the line
Douglas County, MO15.5%0.195
San Juan County, CO47.2%0.112
Lewis County, ID20%0.106
Cheyenne County, CO24.4%0.0822
Custer County, CO35.3%0.0819
Below the line
Utah County, UT43.8%0.0042
Hudson County, NJ47.9%0.00651
Queens County, NY35.3%0.00469
Albemarle County, VA60.6%0.00856
Kings County, NY41.3%0.00603

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+0.1573,076 counties reporting both
After adjusting for density, income and education+0.395the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.429size taken out as well as density
Also comparing only within each state+0.394each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.468the 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+0.096the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting+0.401uses 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 = 1.8e-115. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 3.0e-19. 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 1.5e-114.

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 +0.18 (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

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 Public-good pages per 1,000 users