placestats

Per capita income and Child mortality

Across 1,734 counties, Per capita income and Child mortality have a link of -0.56, 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

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

Child mortality

Deaths of children under 18 per 100,000 children.

County Health Rankings · County Health Rankings v128

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 incomeChild mortality
Highest
New York County, NY$94,92231.8
Marin County, CA$90,40825.2
San Francisco County, CA$90,28528.8
Arlington County, VA$88,02435
San Mateo County, CA$81,98026.1
Lowest
Oglala Lakota County, SD$11,650208
Todd County, SD$13,849153
Kusilvak Census Area, AK$16,657274
Telfair County, GA$17,226151
Starr County, TX$18,06346.2

By child mortality

Per capita incomeChild mortality
Highest
Kusilvak Census Area, AK$16,657274
Oglala Lakota County, SD$11,650208
Northwest Arctic Borough, AK$28,467202
Bethel Census Area, AK$24,976200
Holmes County, MS$22,044192
Lowest
Putnam County, NY$54,91419
Benton County, OR$42,11219.1
Kendall County, IL$43,12719.3
Washington County, RI$53,22620.8
Norfolk County, MA$69,50821.2
States

By per capita income

Per capita incomeChild mortality
Highest
District of Columbia$75,25365.3
Massachusetts$56,28430.4
Connecticut$54,40936.8
New Jersey$53,11833.9
Maryland$51,68951.5
Lowest
Mississippi$30,52986.5
West Virginia$32,94960.7
Arkansas$33,14773
Louisiana$34,21178.6
New Mexico$34,82359.3

By child mortality

Per capita incomeChild mortality
Highest
Mississippi$30,52986.5
Louisiana$34,21178.6
Arkansas$33,14773
Alaska$44,92872.9
Alabama$34,83572.7
Lowest
Massachusetts$56,28430.4
New Jersey$53,11833.9
New Hampshire$50,86734.6
Rhode Island$45,91934.7
Vermont$44,36536.3
Metro areas

By per capita income

Per capita incomeChild mortality
Highest
San Jose-Sunnyvale-Santa Clara, CA$75,89528.3
San Francisco-Oakland-Fremont, CA$72,30630.9
Bridgeport-Stamford-Danbury, CT$69,64229.2
Heber, UT$65,33336.1
Edwards, CO$62,60735.4
Lowest
Rio Grande City-Roma, TX$18,06346.2
Gallup, NM$19,58065.4
Middlesborough, KY$20,46391.1
Deming, NM$21,87177.2
Eagle Pass, TX$21,95255.3

By child mortality

Per capita incomeChild mortality
Highest
Greenville, MS$24,162143
Uvalde, TX$26,437139
Vernon, TX$28,997137
Rockingham, NC$24,752137
Minden, LA$24,115135
Lowest
Corvallis, OR$42,11219.1
Blacksburg-Christiansburg-Radford, VA$34,81621.2
Putnam, CT$44,39321.6
Amherst Town-Northampton, MA$45,00622.8
Santa Cruz-Watsonville, CA$55,71727.5
Cities and towns — Child mortality is not published for cities and towns

By per capita income

Per capita incomeChild mortality
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 — Child mortality is not published for ZIP codes

By per capita income

Per capita incomeChild mortality
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 1,735 counties: the five with far more child mortality than their per capita income would suggest, and the five with far less.

Per capita incomeChild mortality
Above the line
Kusilvak Census Area, AK$16,657274
Northwest Arctic Borough, AK$28,467202
Bethel Census Area, AK$24,976200
Oglala Lakota County, SD$11,650208
Holmes County, MS$22,044192
Below the line
Val Verde County, TX$27,11428.2
Imperial County, CA$22,70236
Madison County, ID$23,02437
Starr County, TX$18,06346.2
Hidalgo County, TX$22,00541.3

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-0.5601,734 counties reporting both
After adjusting for density, income and education+0.007the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.005size taken out as well as density
Also comparing only within each state+0.073each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.070the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 260 equally weighted counties
Middle value inside single states-0.491the link worked out inside each of 31 states with at least 20 counties reporting
Using ranks, after adjusting+0.019uses 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 = 0.780. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.880. 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 0.800.

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 +0.26 (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 child 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic1,436+0.07+0.050.365
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years1,484-0.01-0.020.596

Measured over the nearest years published: Child mortality: 2013–2019 for the 2014–2019 row; Child mortality: 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

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 Child mortality