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Median household income (model-based) and Economic connectedness

Across 3,011 counties, Median household income (model-based) and Economic connectedness 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.

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What each measure is

Median household income (model-based)

The income of the middle household. It is an estimate from a statistical model that combines survey answers with tax and government-program records.

Census SAIPE · Census SAIPE SAEMHI_PT

Economic connectedness

How many of a low-income person's friends are high-income, compared with an even mix. One means their friends match the local mix of rich and poor. Below one means their friendships are more divided by income than that.

Social Capital Atlas · Opportunity Insights ec_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 median household income (model-based)

Median household income (model-based)Economic connectedness
Highest
Loudoun County, VA$173,6601.22
Santa Clara County, CA$154,5701.16
San Mateo County, CA$151,0001.16
Los Alamos County, NM$146,2101.12
Douglas County, CO$144,8101.35
Lowest
Claiborne County, MS$28,5790.432
Holmes County, MS$30,5420.46
Humphreys County, MS$31,5380.43
Leflore County, MS$33,9450.485
Wilcox County, AL$34,4260.422

By economic connectedness

Median household income (model-based)Economic connectedness
Highest
Rockingham County, NH$109,6601.36
Douglas County, CO$144,8101.35
San Francisco County, CA$125,4601.31
Morgan County, UT$122,6701.29
Madison County, ID$58,2361.28
Lowest
Todd County, SD$50,2680.295
Oglala Lakota County, SD$42,7910.33
Terrell County, GA$45,6160.347
Bullock County, AL$36,2040.355
Allendale County, SC$35,4770.361
States — Economic connectedness is not published for states

By median household income (model-based)

Median household income (model-based)Economic connectedness
Highest
District of Columbia$104,640—
Massachusetts$99,750—
New Jersey$99,716—
Maryland$98,568—
New Hampshire$97,031—
Lowest
Mississippi$54,386—
West Virginia$55,875—
Louisiana$58,273—
Arkansas$58,748—
Kentucky$61,099—
Metro areas — Median household income (model-based) is not published for metro areas

By economic connectedness

Median household income (model-based)Economic connectedness
Highest
Provo-Orem-Lehi, UT—1.22
Alexandria, MN—1.18
Laramie, WY—1.17
Hutchinson, MN—1.16
Ames, IA—1.15
Lowest
Lumberton, NC—0.363
Cordele, GA—0.388
Laurinburg, NC—0.396
Eufaula, AL-GA—0.414
Gallup, NM—0.416

Counties furthest from the pattern

Compared with the straight line that best fits all 3,020 counties: the five with far more economic connectedness than their median household income (model-based) would suggest, and the five with far less.

Median household income (model-based)Economic connectedness
Above the line
Madison County, ID$58,2361.28
Garfield County, NE$51,1351.17
Golden Valley County, ND$59,4641.19
McPherson County, SD$55,7041.16
Faulk County, SD$66,8101.22
Below the line
Marin County, CA$139,2000.812
Western Connecticut Planning Region, CT$121,2800.717
Mercer County, NJ$95,5020.567
Todd County, SD$50,2680.295
Nantucket County, MA$108,6700.694

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+0.5903,011 counties reporting both
After adjusting for density, income and education+0.092the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.111size taken out as well as density
Also comparing only within each state+0.143each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight-0.124the 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+0.656the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting+0.177uses 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 = 4.5e-7. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.013. 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 8.7e-7.

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 +0.55 (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 Median household income (model-based) and Economic connectedness