placestats

Share with a bachelor's degree or higher and Friending clustering

Across 3,076 counties, Share with a bachelor's degree or higher and Friending clustering have a link of -0.54, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a very weak link of -0.17 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

Friending clustering

How often a person's friends are also friends with each other. It shows how tight-knit the local network is, which is separate from how far it reaches across income groups.

Social Capital Atlas · Opportunity Insights clustering_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 higherFriending clustering
Highest
Arlington County, VA77.1%0.0891
Los Alamos County, NM68.2%0.0969
Alexandria city, VA65.8%0.0816
San Miguel County, CO65.2%0.0941
Howard County, MD64.5%0.09
Lowest
McDowell County, WV5.75%0.151
Elliott County, KY6.22%0.155
Newton County, TX6.23%0.124
Lee County, AR6.87%0.157
Reeves County, TX6.87%0.115

By friending clustering

Share with a bachelor's degree or higherFriending clustering
Highest
Nome Census Area, AK17.5%0.235
Northwest Arctic Borough, AK12.4%0.228
East Carroll Parish, LA11%0.222
Bethel Census Area, AK10.4%0.221
Sharkey County, MS15.4%0.215
Lowest
Bronx County, NY22%0.0716
Osceola County, FL28.3%0.0734
Utah County, UT43.8%0.0758
Kings County, NY41.3%0.0759
Bernalillo County, NM37.4%0.0761
States — Friending clustering is not published for states

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherFriending clustering
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 higherFriending clustering
Highest
Los Alamos, NM68.2%0.0969
Boulder, CO63.9%0.0862
Ithaca, NY59.3%0.0971
Nantucket, MA58.4%0.12
Ann Arbor, MI58.1%0.0994
Lowest
Town of Pecos, TX6.87%0.115
Summerville, GA10.7%0.123
Arcadia, FL11%0.114
Eufaula, AL-GA11.3%0.147
Clewiston, FL11.3%0.116

By friending clustering

Share with a bachelor's degree or higherFriending clustering
Highest
Zapata, TX13.3%0.182
Natchez, MS-LA18.8%0.176
Greenwood, MS19.5%0.166
Cleveland, MS26.6%0.165
Clarksdale, MS19%0.163
Lowest
Las Vegas-Henderson-North Las Vegas, NV27.3%0.0763
Provo-Orem-Lehi, UT43.4%0.077
El Paso, TX25.3%0.0772
Colorado Springs, CO41%0.0784
Orlando-Kissimmee-Sanford, FL35.8%0.0786
Cities and towns — Friending clustering is not published for cities and towns

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherFriending clustering
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 — Friending clustering is not published for ZIP codes

By share with a bachelor's degree or higher

Share with a bachelor's degree or higherFriending clustering
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 friending clustering 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 higherFriending clustering
Above the line
Nome Census Area, AK17.5%0.235
Northwest Arctic Borough, AK12.4%0.228
Dillingham Census Area, AK19.4%0.214
Humphreys County, MS19.4%0.214
East Carroll Parish, LA11%0.222
Below the line
Bronx County, NY22%0.0716
McKinley County, NM12.5%0.0823
Ector County, TX16.6%0.0799
San Juan County, NM16.6%0.0826
Apache County, AZ15.3%0.0859

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-0.5423,076 counties reporting both
After adjusting for density, income and education-0.175the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.137size taken out as well as density
Also comparing only within each state-0.080each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.001the 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-0.553the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting-0.270uses 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 = 1.5e-22. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. 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 5.1e-22.

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 +0.46 (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 Friending clustering