placestats

Child mortality and Incarceration of children raised poor here

Where Child mortality is higher, Incarceration of children raised poor here tends to be higher. The link is weak: +0.35 across 1,631 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 30 states, the middle value of the link is +0.18.

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

Child mortality

Deaths of children under 18 per 100,000 children.

County Health Rankings · County Health Rankings v128

Incarceration of children raised poor here

The share of those same children, who grew up here with poor parents, who were in prison on census day in 2010.

Opportunity Atlas · Opportunity Insights jail_pooled_pooled_p25

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 child mortality

Child mortalityIncarceration of children raised poor here
Highest
Oglala Lakota County, SD2080.0889
Bethel Census Area, AK2000.0305
Holmes County, MS1920.0207
Humphreys County, MS1740.0267
Petersburg city, VA1680.0594
Lowest
Putnam County, NY190.011
Benton County, OR19.10.0146
Kendall County, IL19.30.0143
Washington County, RI20.80.00337
Norfolk County, MA21.20.00783

By incarceration of children raised poor here

Child mortalityIncarceration of children raised poor here
Highest
Oglala Lakota County, SD2080.0889
Charlottesville city, VA43.10.0751
Portsmouth city, VA1130.0658
Franklin city, VA1640.0649
King George County, VA68.50.0642
Lowest
Kent County, RI31.90.0033
Washington County, RI20.80.00337
Providence County, RI39.50.0047
Stark County, ND75.90.00571
Carver County, MN25.60.00576
States — Incarceration of children raised poor here is not published for states

By child mortality

Child mortalityIncarceration of children raised poor here
Highest
Mississippi86.5—
Louisiana78.6—
Arkansas73—
Alaska72.9—
Alabama72.7—
Lowest
Massachusetts30.4—
New Jersey33.9—
New Hampshire34.6—
Rhode Island34.7—
Vermont36.3—
Metro areas

By child mortality

Child mortalityIncarceration of children raised poor here
Highest
Greenville, MS1430.0334
Uvalde, TX1390.0247
Vernon, TX1370.028
Rockingham, NC1370.0177
Minden, LA1350.0497
Lowest
Corvallis, OR19.10.0146
Blacksburg-Christiansburg-Radford, VA21.20.0348
Putnam, CT21.60.0263
Amherst Town-Northampton, MA22.80.00949
Santa Cruz-Watsonville, CA27.50.0148

By incarceration of children raised poor here

Child mortalityIncarceration of children raised poor here
Highest
Summerville, GA66.10.0555
Waco, TX63.30.0552
Sherman-Denison, TX54.10.0546
Savannah, GA69.60.0541
Arcadia, FL830.0533
Lowest
Dickinson, ND75.90.00571
Oneonta, NY37.10.00584
Houghton, MI36.20.00631
Wheeling, WV-OH57.70.00639
Providence-Warwick, RI-MA35.70.00684

Counties furthest from the pattern

Compared with the straight line that best fits all 1,631 counties: the five with far more incarceration of children raised poor here than their child mortality would suggest, and the five with far less.

Child mortalityIncarceration of children raised poor here
Above the line
Charlottesville city, VA43.10.0751
Oglala Lakota County, SD2080.0889
King George County, VA68.50.0642
Dauphin County, PA59.30.0608
McLennan County, TX59.30.0592
Below the line
Swain County, NC1090.00614
Holmes County, MS1920.0207
Howell County, MO94.90.00742
Pottawatomie County, KS82.10.0058
Stark County, ND75.90.00571

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+0.3341,631 counties reporting both
After adjusting for density, income and education+0.355the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.343size taken out as well as density
Also comparing only within each state+0.244each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.531the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 255 equally weighted counties
Middle value inside single states+0.177the link worked out inside each of 30 states with at least 20 counties reporting
Using ranks, after adjusting+0.400uses 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 = 1.9e-49. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.1e-7. 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 1.0e-48.

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 +0.34 (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 Child mortality and Incarceration of children raised poor here