Threat of utility shut-off and Returns claiming the earned income credit
Where Threat of utility shut-off is higher, Returns claiming the earned income credit tends to be higher. The link is strong: +0.80 across 2,287 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 +0.89.
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
Threat of utility shut-off
The estimated percentage of adults who received a notice threatening to shut off a utility in the past year. It is adjusted so places with older or younger populations can be compared fairly. It is an estimate from a statistical model, not a direct count from a local survey.
CDC PLACES · PLACES SHUTUTILITY, age-adjusted
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
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 threat of utility shut-off
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| East Carroll Parish, LA | 27.8% | 42.2% |
| Madison Parish, LA | 25.5% | 42.9% |
| Greene County, AL | 25.5% | 42.7% |
| McKinley County, NM | 25% | 35.1% |
| Kusilvak Census Area, AK | 24.9% | 48.7% |
| Lowest | ||
| Falls Church city, VA | 3.7% | 4.04% |
| Ozaukee County, WI | 4.1% | 4.98% |
| Waukesha County, WI | 4.2% | 4.93% |
| Arlington County, VA | 4.3% | 6.14% |
| Loudoun County, VA | 4.4% | 6.36% |
By returns claiming the earned income credit
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 24.9% | 48.7% |
| Humphreys County, MS | 24.8% | 47.6% |
| Tunica County, MS | 23.8% | 46.3% |
| Holmes County, MS | 24.8% | 46% |
| Quitman County, MS | 23% | 44.9% |
| Lowest | ||
| Yakutat City and Borough, AK | 10.8% | 0% |
| Skagway Municipality, AK | 6.8% | 0% |
| Daggett County, UT | 7.5% | 0% |
| Slope County, ND | 6.9% | 0% |
| Oliver County, ND | 5.4% | 0% |
States — Threat of utility shut-off is not published for states
By returns claiming the earned income credit
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| Mississippi | — | 26.7% |
| Louisiana | — | 24% |
| Georgia | — | 20.6% |
| Arkansas | — | 20.5% |
| Alabama | — | 20.3% |
| Lowest | ||
| New Hampshire | — | 7.91% |
| Massachusetts | — | 9.56% |
| Washington | — | 9.82% |
| Minnesota | — | 9.98% |
| Vermont | — | 10.1% |
Metro areas
By threat of utility shut-off
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| Gallup, NM | 25% | 35.1% |
| Clarksdale, MS | 22.5% | 42% |
| Greenville, MS | 20.4% | 41.2% |
| Selma, AL | 20.4% | 38.6% |
| Forrest City, AR | 19.9% | 36.7% |
| Lowest | ||
| Los Alamos, NM | 4.8% | 3.7% |
| San Jose-Sunnyvale-Santa Clara, CA | 4.81% | 7.02% |
| Madison, WI | 4.9% | 7.35% |
| Heber, UT | 4.97% | 6.24% |
| Appleton, WI | 5.06% | 8.31% |
By returns claiming the earned income credit
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| Clarksdale, MS | 22.5% | 42% |
| Greenville, MS | 20.4% | 41.2% |
| Greenwood, MS | 19% | 38.7% |
| Selma, AL | 20.4% | 38.6% |
| Forrest City, AR | 19.9% | 36.7% |
| Lowest | ||
| Los Alamos, NM | 4.8% | 3.7% |
| Jackson, WY-ID | 5.9% | 6.19% |
| Heber, UT | 4.97% | 6.24% |
| Nantucket, MA | 5.9% | 6.55% |
| Bozeman, MT | 5.5% | 6.6% |
Cities and towns — Returns claiming the earned income credit is not published for cities and towns
By threat of utility shut-off
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Highest | ||
| Benton Harbor city, MI | 34.6% | — |
| Glasgow Village CDP, MO | 33.3% | — |
| Buena Vista CDP, MI | 33.2% | — |
| Tallulah city, LA | 32.3% | — |
| Jennings city, MO | 31.8% | — |
| Lowest | ||
| San Marino city, CA | 2.6% | — |
| Cupertino city, CA | 2.7% | — |
| Saratoga city, CA | 2.8% | — |
| Los Altos city, CA | 2.8% | — |
| Palos Verdes Estates city, CA | 2.9% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,298 counties: the five with far more returns claiming the earned income credit than their threat of utility shut-off would suggest, and the five with far less.
| Threat of utility shut-off | Returns claiming the earned income credit | |
|---|---|---|
| Above the line | ||
| Echols County, GA | 9.3% | 27.7% |
| Menominee County, WI | 13.4% | 35.6% |
| Murray County, GA | 8.5% | 25.3% |
| Webster County, MS | 8.6% | 25.2% |
| Prentiss County, MS | 9.8% | 27.6% |
| Below the line | ||
| Harding County, NM | 13.6% | 0% |
| Esmeralda County, NV | 12.9% | 0% |
| Yakutat City and Borough, AK | 10.8% | 0% |
| Clark County, ID | 10.5% | 0% |
| Eureka County, NV | 13.1% | 7.41% |
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 | +0.895 | 2,287 counties reporting both |
| After adjusting for density, income and education | +0.797 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.795 | size taken out as well as density |
| Also comparing only within each state | +0.768 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.754 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 192 equally weighted counties |
| Middle value inside single states | +0.886 | the link worked out inside each of 30 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.694 | uses 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 = 5.8e-21. 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 +0.45 (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.
| Level | Places | Unadjusted | Adjusted | Note |
|---|---|---|---|---|
| county | 2,287 | +0.90 | +0.80 | |
| state | — | — | — | not available at state: not published |
| cbsa | 674 | +0.91 | +0.87 | not 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
- Food insecurity and Returns claiming the earned income credit — +0.84
- Lack of reliable transportation and Returns claiming the earned income credit — +0.84
- Housing insecurity and Returns claiming the earned income credit — +0.83
- Self-care disability and Returns claiming the earned income credit — +0.82
- Vision disability and Returns claiming the earned income credit — +0.81
- Stroke and Returns claiming the earned income credit — +0.78
- Diagnosed diabetes and Returns claiming the earned income credit — +0.78
- Mobility disability and Returns claiming the earned income credit — +0.78
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 Threat of utility shut-off and Returns claiming the earned income credit