Gini index of income inequality and Share separated
Where Gini index of income inequality is higher, Share separated tends to be higher. The link is weak: +0.30 across 2,250 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 34 states, the middle value of the link is +0.15.
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
Gini index of income inequality
How unequally household income is shared, on a scale from 0 to 1. Zero would mean every household has the same income. One would mean a single household has all of it. US counties run from about 0.32 to 0.60. It is measured on money income before taxes and government payments, so US figures sit above countries that measure it after taxes and payments.
Census ACS 5-year · ACS table B19083_001
Share separated
The percentage of people aged 15 and over who are separated. This is not a fifth group next to the other four. The Census counts a separated person as married with the spouse away, so this is part of the married share and must never be added to it.
Census ACS 5-year · ACS table B12001_007…B12001_016 (2 cells), divided by population aged 15 and over
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 gini index of income inequality
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| San Juan Municipio, PR | 0.617 | 2.14% |
| Macon County, GA | 0.611 | 4.21% |
| Mayagüez Municipio, PR | 0.61 | 2.5% |
| Holmes County, MS | 0.609 | 6.57% |
| New York County, NY | 0.596 | 2.28% |
| Lowest | ||
| Kalawao County, HI | 0.291 | 0% |
| Aleutians West Census Area, AK | 0.346 | 4.23% |
| Paulding County, GA | 0.354 | 1.54% |
| Kendall County, IL | 0.356 | 1.16% |
| Sitka City and Borough, AK | 0.358 | 1.26% |
By share separated
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Lee County, KY | 0.49 | 6.77% |
| Holmes County, MS | 0.609 | 6.57% |
| Woodruff County, AR | 0.488 | 6.36% |
| Wilcox County, AL | 0.495 | 6.03% |
| Sharkey County, MS | 0.494 | 5.99% |
| Lowest | ||
| San Juan County, CO | 0.405 | 0% |
| Hinsdale County, CO | 0.394 | 0% |
| Columbia County, WA | 0.435 | 0% |
| King County, TX | 0.439 | 0% |
| Kenedy County, TX | 0.399 | 0% |
States
By gini index of income inequality
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Puerto Rico | 0.546 | 2.13% |
| New York | 0.515 | 2.19% |
| District of Columbia | 0.514 | 1.4% |
| Connecticut | 0.498 | 1.22% |
| Louisiana | 0.494 | 2.22% |
| Lowest | ||
| Utah | 0.428 | 1.16% |
| Alaska | 0.433 | 1.68% |
| New Hampshire | 0.441 | 1.01% |
| Wisconsin | 0.443 | 0.869% |
| Idaho | 0.444 | 1.04% |
By share separated
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Mississippi | 0.484 | 2.49% |
| South Carolina | 0.477 | 2.35% |
| Louisiana | 0.494 | 2.22% |
| New York | 0.515 | 2.19% |
| North Carolina | 0.476 | 2.18% |
| Lowest | ||
| Wisconsin | 0.443 | 0.869% |
| North Dakota | 0.455 | 0.875% |
| Minnesota | 0.45 | 0.888% |
| Maine | 0.457 | 0.97% |
| Nebraska | 0.456 | 0.993% |
Metro areas — Gini index of income inequality is not published for metro areas
By share separated
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | — | 5.51% |
| Raymondville, TX | — | 4.62% |
| Orangeburg, SC | — | 4.61% |
| Bainbridge, GA | — | 4.55% |
| Henderson, NC | — | 4.53% |
| Lowest | ||
| Mitchell, SD | — | 0% |
| Dickinson, ND | — | 0% |
| Ottawa, KS | — | 0.332% |
| Marquette, MI | — | 0.355% |
| Alexandria, MN | — | 0.412% |
Cities and towns
By gini index of income inequality
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Hazard city, KY | 0.683 | 9.11% |
| Milledgeville city, GA | 0.676 | 1.37% |
| Dyersburg city, TN | 0.666 | 4% |
| Mayagüez zona urbana, PR | 0.634 | 2.74% |
| San Germán zona urbana, PR | 0.627 | 2.89% |
| Lowest | ||
| Bangor Base CDP, WA | 0.259 | 0% |
| Moorefield CDP, VA | 0.266 | 0% |
| Lakes of the Four Seasons CDP, IN | 0.27 | 0% |
| Helena Valley Northwest CDP, MT | 0.276 | 0% |
| Fort Dix CDP, NJ | 0.286 | 4.38% |
By share separated
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| Westgate CDP, FL | 0.401 | 11% |
| Hazard city, KY | 0.683 | 9.11% |
| Central City city, KY | 0.549 | 6.97% |
| Gonzales city, TX | 0.492 | 6.77% |
| Monticello village, NY | 0.51 | 6.7% |
| Lowest | ||
| Simsbury Center CDP, CT | 0.393 | 0% |
| Old Greenwich CDP, CT | 0.524 | 0% |
| Oakville CDP, CT | 0.45 | 0% |
| Daniels Farm CDP, CT | 0.381 | 0% |
| Cos Cob CDP, CT | 0.491 | 0% |
ZIP codes
By gini index of income inequality
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| ZIP 39095 (Holmes County, MS) | 0.67 | 10.2% |
| ZIP 00682 (Mayagüez zona urbana, PR) | 0.659 | 2.44% |
| ZIP 47406 (Bloomington city, IN) | 0.647 | 0% |
| ZIP 48201 (Detroit city, MI) | 0.646 | 2.43% |
| ZIP 31201 (Macon-Bibb County, GA) | 0.643 | 3.86% |
| Lowest | ||
| ZIP 98315 (Bangor Base CDP, WA) | 0.259 | 0% |
| ZIP 04027 (York County, ME) | 0.282 | 0% |
| ZIP 84128 (West Valley City city, UT) | 0.283 | 3.86% |
| ZIP 08640 (Fort Dix CDP, NJ) | 0.286 | 4.38% |
| ZIP 20136 (Linton Hall CDP, VA) | 0.296 | 1.51% |
By share separated
| Gini index of income inequality | Share separated | |
|---|---|---|
| Highest | ||
| ZIP 39095 (Holmes County, MS) | 0.67 | 10.2% |
| ZIP 79901 (El Paso city, TX) | 0.515 | 9.93% |
| ZIP 63106 (St. Louis city, MO) | 0.532 | 8.33% |
| ZIP 33128 (Miami city, FL) | 0.465 | 8.26% |
| ZIP 36784 (Clarke County, AL) | 0.471 | 8.07% |
| Lowest | ||
| ZIP 08502 (Somerset County, NJ) | 0.379 | 0% |
| ZIP 08230 (Cape May County, NJ) | 0.369 | 0% |
| ZIP 08056 (Gloucester County, NJ) | 0.378 | 0% |
| ZIP 07981 (Whippany CDP, NJ) | 0.421 | 0% |
| ZIP 07757 (Oceanport borough, NJ) | 0.404 | 0% |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,256 counties: the five with far more share separated than their gini index of income inequality would suggest, and the five with far less.
| Gini index of income inequality | Share separated | |
|---|---|---|
| Above the line | ||
| Lee County, KY | 0.49 | 6.77% |
| Woodruff County, AR | 0.488 | 6.36% |
| Forest County, PA | 0.421 | 5.5% |
| Wilcox County, AL | 0.495 | 6.03% |
| Sharkey County, MS | 0.494 | 5.99% |
| Below the line | ||
| Prairie County, MT | 0.522 | 0% |
| Blaine County, NE | 0.521 | 0% |
| Borden County, TX | 0.501 | 0% |
| Wichita County, KS | 0.462 | 0% |
| Glasscock County, TX | 0.452 | 0% |
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 | +0.333 | 2,250 counties reporting both |
| After adjusting for density, income and education | +0.302 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.306 | size taken out as well as density |
| Also comparing only within each state | +0.161 | 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 | +0.357 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 275 equally weighted counties |
| Middle value inside single states | +0.155 | the link worked out inside each of 34 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.221 | 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 = 1.3e-48. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.2e-8. 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 6.9e-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 +0.32 (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 gini index of income inequality rose most, did share separatedmove 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.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 2,336 | +0.03 | +0.03 | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 2,146 | +0.03 | +0.03 | — |
Both come from the American Community Survey, which asks a sample of households. Random error in a county's sample pushes both changes at once. So part of any agreement here is that shared error. It is shown, but not counted as a finding. How change is measured.
Related pairs
- Share separated and Returns claiming the earned income credit — +0.59
- Gini index of income inequality and Median household income — -0.56
- Share separated and Material security score — -0.54
- Share Black, not Hispanic and Share separated — +0.51
- Share separated and Homicide rate, annual — +0.49
- Share separated and Children in single-parent households — +0.47
- Share separated and Share White, not Hispanic — -0.47
- Gini index of income inequality and Per capita income — +0.46
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 Gini index of income inequality and Share separated