Returns claiming the earned income credit and Safety score
Where Returns claiming the earned income credit is higher, Safety score tends to be lower. The link is weak: -0.34 across 2,086 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 33 states, the middle value of the link is -0.56.
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
Safety score
Deaths from violence and injury (guns, crashes and overdoses), flipped so higher is safer. Homicide, suicide, drunk-driving and total injury deaths are left out, since they would count the same deaths twice. Gun deaths already include most homicides and suicides, and injury deaths include all four.
· derived from 4 measures (BRFSS, CHR)
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 credit | Safety score | |
|---|---|---|
| Highest | ||
| Humphreys County, MS | 47.6% | -2.44 |
| Tunica County, MS | 46.3% | -1.59 |
| Holmes County, MS | 46% | -3.82 |
| Oglala Lakota County, SD | 45.5% | -4.61 |
| Claiborne County, MS | 44.8% | -2.79 |
| Lowest | ||
| Douglas County, CO | 4.45% | 1.05 |
| Hunterdon County, NJ | 4.9% | 1.15 |
| Waukesha County, WI | 4.93% | 0.866 |
| Carver County, MN | 4.93% | 1.2 |
| Ozaukee County, WI | 4.98% | 1.07 |
By safety score
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Highest | ||
| Arlington County, VA | 6.14% | 1.4 |
| Loudoun County, VA | 6.36% | 1.39 |
| Madison County, ID | 16.4% | 1.38 |
| Story County, IA | 8.03% | 1.29 |
| Fairfax County, VA | 8.1% | 1.29 |
| Lowest | ||
| Big Horn County, MT | 31.6% | -5.15 |
| Phillips County, AR | 39.2% | -4.96 |
| Todd County, SD | 40.7% | -4.82 |
| Oglala Lakota County, SD | 45.5% | -4.61 |
| Wilcox County, AL | 38.3% | -4.61 |
States
By returns claiming the earned income credit
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Highest | ||
| Mississippi | 26.7% | -1.82 |
| Louisiana | 24% | -0.999 |
| Georgia | 20.6% | -0.0835 |
| Arkansas | 20.5% | -0.779 |
| Alabama | 20.3% | -0.771 |
| Lowest | ||
| New Hampshire | 7.91% | -1.16 |
| Massachusetts | 9.56% | 0.153 |
| Washington | 9.82% | 0.756 |
| Minnesota | 9.98% | 0.747 |
| Vermont | 10.1% | 0.0253 |
By safety score
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Highest | ||
| Hawaii | 12.1% | 0.788 |
| Washington | 9.82% | 0.756 |
| Minnesota | 9.98% | 0.747 |
| California | 13.8% | 0.68 |
| Oregon | 11.2% | 0.628 |
| Lowest | ||
| Mississippi | 26.7% | -1.82 |
| Kentucky | 18% | -1.52 |
| West Virginia | 16.3% | -1.48 |
| Missouri | 14.8% | -1.37 |
| Montana | 11.7% | -1.23 |
Metro areas
By returns claiming the earned income credit
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | 44.4% | 0.831 |
| Clarksdale, MS | 42% | -2.58 |
| Greenville, MS | 41.2% | -3.93 |
| Greenwood, MS | 38.7% | -4.26 |
| Selma, AL | 38.6% | -2.61 |
| Lowest | ||
| Jackson, WY-ID | 6.19% | -0.512 |
| Heber, UT | 6.24% | 1 |
| Edwards, CO | 6.39% | 0.975 |
| Bozeman, MT | 6.6% | 0.789 |
| Juneau, AK | 6.78% | 0.224 |
By safety score
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Highest | ||
| Ames, IA | 8.28% | 1.47 |
| Iowa City, IA | 10.4% | 1.41 |
| State College, PA | 7.85% | 1.38 |
| McAllen-Edinburg-Mission, TX | 36.9% | 1.37 |
| Urban Honolulu, HI | 11.2% | 1.37 |
| Lowest | ||
| Greenwood, MS | 38.7% | -4.26 |
| Greenville, MS | 41.2% | -3.93 |
| Lumberton, NC | 34% | -3.33 |
| Española, NM | 18.2% | -2.61 |
| Selma, AL | 38.6% | -2.61 |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,086 counties: the five with far more safety score than their returns claiming the earned income credit would suggest, and the five with far less.
| Returns claiming the earned income credit | Safety score | |
|---|---|---|
| Above the line | ||
| Starr County, TX | 44.4% | 0.886 |
| Hidalgo County, TX | 36.9% | 1.19 |
| Webb County, TX | 37% | 0.901 |
| Cameron County, TX | 34% | 1.12 |
| Maverick County, TX | 35.9% | 0.94 |
| Below the line | ||
| Big Horn County, MT | 31.6% | -5.15 |
| Rosebud County, MT | 17.8% | -3.84 |
| McDowell County, WV | 24.6% | -4.3 |
| Estill County, KY | 22.7% | -3.94 |
| Baltimore city, MD | 22% | -3.63 |
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 | -0.623 | 2,086 counties reporting both |
| After adjusting for density, income and education | -0.342 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.353 | size taken out as well as density |
| Also comparing only within each state | -0.271 | 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 | -0.094 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 271 equally weighted counties |
| Middle value inside single states | -0.556 | the link worked out inside each of 33 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.270 | 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 = 2.5e-58. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. 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 1.5e-57.
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 +0.36 (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
- 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
- Threat of utility shut-off and Returns claiming the earned income credit — +0.80
- Stroke and Returns claiming the earned income credit — +0.78
- Diagnosed diabetes 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 Returns claiming the earned income credit and Safety score