placestats

Returns claiming the earned income credit and Disability-free score

Where Returns claiming the earned income credit is higher, Disability-free score tends to be lower. The link is strong: -0.73 across 2,934 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 38 states, the middle value of the link is -0.85.

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

Returns claiming the earned income credit

The percentage of tax returns claiming the earned income tax credit, which goes to working households with low incomes. It is a count from tax records of the working poor, not a survey estimate.

IRS Statistics of Income · IRS SOI N59660 / N1

Disability-free score

How common disability is, across seven kinds, flipped so higher means less disability. It is kept out of the health score on purpose. Disability goes with age and with how people report it as much as with anything a county does. Adding it would make an older county look sicker just for being older.

· derived from 13 measures (BRFSS, CDC)

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 returns claiming the earned income credit

Returns claiming the earned income creditDisability-free score
Highest
Kusilvak Census Area, AK48.7%-4.04
Humphreys County, MS47.6%-3.02
Tunica County, MS46.3%-2.74
Holmes County, MS46%-2.78
Oglala Lakota County, SD45.5%-5.31
Lowest
Mineral County, CO0%1.15
Yakutat City and Borough, AK0%-0.44
Skagway Municipality, AK0%0.819
Daggett County, UT0%0.175
Roberts County, TX0%0.923

By disability-free score

Returns claiming the earned income creditDisability-free score
Highest
Falls Church city, VA4.04%2.05
Los Alamos County, NM3.7%1.95
Somerset County, NJ7%1.94
Howard County, MD7.88%1.93
Morris County, NJ5.61%1.92
Lowest
Oglala Lakota County, SD45.5%-5.31
Todd County, SD40.7%-4.83
Kusilvak Census Area, AK48.7%-4.04
Sioux County, ND36.1%-3.8
East Carroll Parish, LA42.2%-3.68
States

By returns claiming the earned income credit

Returns claiming the earned income creditDisability-free score
Highest
Mississippi26.7%-1.8
Louisiana24%-1.7
Georgia20.6%-0.251
Arkansas20.5%-2.14
Alabama20.3%-1.41
Lowest
New Hampshire7.91%0.329
Massachusetts9.56%0.25
Washington9.82%0.385
Minnesota9.98%0.446
Vermont10.1%0.575

By disability-free score

Returns claiming the earned income creditDisability-free score
Highest
New Jersey12%0.837
Connecticut11.1%0.779
Maryland12.3%0.742
Hawaii12.1%0.63
North Dakota10.5%0.628
Lowest
West Virginia16.3%-2.47
Arkansas20.5%-2.14
Mississippi26.7%-1.8
Tennessee17%-1.72
Louisiana24%-1.7
Metro areas

By returns claiming the earned income credit

Returns claiming the earned income creditDisability-free score
Highest
Zapata, TX45.4%-3.68
Rio Grande City-Roma, TX44.4%-3.85
Clarksdale, MS42%-3.09
Greenville, MS41.2%-2.6
Greenwood, MS38.7%-2.52
Lowest
Los Alamos, NM3.7%2.35
Jackson, WY-ID6.19%1.58
Heber, UT6.24%1.66
Edwards, CO6.39%1.35
Nantucket, MA6.55%2.01

By disability-free score

Returns claiming the earned income creditDisability-free score
Highest
Los Alamos, NM3.7%2.35
Nantucket, MA6.55%2.01
San Jose-Sunnyvale-Santa Clara, CA7.02%1.83
Boulder, CO7.06%1.71
Heber, UT6.24%1.66
Lowest
Gallup, NM35.1%-4.13
Rio Grande City-Roma, TX44.4%-3.85
Zapata, TX45.4%-3.68
Clarksdale, MS42%-3.09
Raymondville, TX37.4%-2.99

Counties furthest from the pattern

Compared with the straight line that best fits all 2,955 counties: the five with far more disability-free score than their returns claiming the earned income credit would suggest, and the five with far less.

Returns claiming the earned income creditDisability-free score
Above the line
Miami-Dade County, FL28.6%0.178
Clayton County, GA35.1%-0.601
Henry County, GA23.1%0.591
DeKalb County, GA21.1%0.798
Fort Bend County, TX16.1%1.29
Below the line
Kenedy County, TX0%-1.19
Esmeralda County, NV0%-1.05
Clark County, ID0%-1
McMullen County, TX0%-0.782
Harding County, NM0%-0.757

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-0.8532,934 counties reporting both
After adjusting for density, income and education-0.727the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.723size taken out as well as density
Also comparing only within each state-0.710each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight-0.679the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 264 equally weighted counties
Middle value inside single states-0.846the link worked out inside each of 38 states with at least 20 counties reporting
Using ranks, after adjusting-0.645uses 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 = 0.000. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 4.9e-14. 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 0.000.

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 +0.42 (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.

At three sizes of place

The same pair measured across counties, metro areas and states. A missing size means the measure cannot be combined up to that level.

LevelPlacesUnadjustedAdjustedNote
county2,934-0.85-0.73
state47-0.73-0.22no same-state comparison at state level: the units do not sit inside states one to one
cbsa872-0.88-0.83not adjusted for median household income: not available at this level; no same-state comparison at cbsa level: the units do not sit inside states one to one

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 Returns claiming the earned income credit and Disability-free score