Returns reporting unemployment compensation and Unemployment rate
Where Returns reporting unemployment compensation is higher, Unemployment rate tends to be higher. The link is strong: +0.61 across 3,121 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 40 states, the middle value of the link is +0.62.
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 reporting unemployment compensation
The percentage of tax returns reporting unemployment benefits during the year.
IRS Statistics of Income · IRS SOI N02300 / N1
Unemployment rate
Unemployed people as a percentage of the labor force (people working or looking for work), not of the whole population. A place where few people look for work at all can have low unemployment and still have few people working.
BLS LAUS · BLS LAUS county annual averages
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 reporting unemployment compensation
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| Colusa County, CA | 19.7% | 12.4% |
| Imperial County, CA | 17.4% | 17.6% |
| Tulare County, CA | 13% | 9.8% |
| Calhoun County, WV | 12.2% | 6.4% |
| Clearwater County, MN | 11.8% | 6.7% |
| Lowest | ||
| Washington County, CO | 0% | 2.7% |
| Sedgwick County, CO | 0% | 3.4% |
| San Juan County, CO | 0% | 3.6% |
| Phillips County, CO | 0% | 2.5% |
| Mineral County, CO | 0% | 3.2% |
By unemployment rate
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| Imperial County, CA | 17.4% | 17.6% |
| Kusilvak Census Area, AK | 9.39% | 14.8% |
| Yuma County, AZ | 11.5% | 12.4% |
| Colusa County, CA | 19.7% | 12.4% |
| East Carroll Parish, LA | 1.79% | 11.8% |
| Lowest | ||
| Loving County, TX | 0% | 1.3% |
| Brule County, SD | 0% | 1.3% |
| King County, TX | 0% | 1.4% |
| Sanborn County, SD | 0% | 1.4% |
| Hand County, SD | 0% | 1.4% |
States
By returns reporting unemployment compensation
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| Rhode Island | 6.9% | 2.98% |
| Massachusetts | 6.24% | 3.49% |
| Connecticut | 6.02% | 3.2% |
| California | 5.75% | 4.72% |
| Washington | 5.55% | 4.21% |
| Lowest | ||
| Florida | 0.913% | 2.94% |
| North Carolina | 0.958% | 3.49% |
| Alabama | 0.986% | 2.47% |
| Mississippi | 1.17% | 3.12% |
| Nebraska | 1.19% | 2.3% |
By unemployment rate
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| Nevada | 3.19% | 5.13% |
| District of Columbia | 2.92% | 4.86% |
| California | 5.75% | 4.72% |
| Illinois | 3.14% | 4.49% |
| New Jersey | 4.93% | 4.32% |
| Lowest | ||
| South Dakota | 1.2% | 1.82% |
| Vermont | 3.08% | 1.94% |
| North Dakota | 2.18% | 2.05% |
| Maryland | 1.73% | 2.24% |
| New Hampshire | 2.19% | 2.28% |
Metro areas
By returns reporting unemployment compensation
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| El Centro, CA | 17.4% | 17.6% |
| Visalia, CA | 13% | 9.8% |
| Yuma, AZ | 11.5% | 12.4% |
| Merced, CA | 11.5% | 9% |
| Kahului-Wailuku, HI | 10.3% | 4.3% |
| Lowest | ||
| Boone, NC | 0.454% | 3.4% |
| Washington, IN | 0.515% | 2.6% |
| Hays, KS | 0.525% | 2.2% |
| Arcadia, FL | 0.554% | 3.5% |
| Key West-Key Largo, FL | 0.569% | 2% |
By unemployment rate
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Highest | ||
| El Centro, CA | 17.4% | 17.6% |
| Yuma, AZ | 11.5% | 12.4% |
| Deming, NM | 7.73% | 10.3% |
| Visalia, CA | 13% | 9.8% |
| Rio Grande City-Roma, TX | 8.45% | 9.2% |
| Lowest | ||
| Pierre, SD | 0.681% | 1.52% |
| Yankton, SD | 0.797% | 1.6% |
| Los Alamos, NM | 0.701% | 1.6% |
| Burlington-South Burlington, VT | 2.17% | 1.61% |
| Mitchell, SD | 1.03% | 1.63% |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,143 counties: the five with far more unemployment rate than their returns reporting unemployment compensation would suggest, and the five with far less.
| Returns reporting unemployment compensation | Unemployment rate | |
|---|---|---|
| Above the line | ||
| Imperial County, CA | 17.4% | 17.6% |
| Kusilvak Census Area, AK | 9.39% | 14.8% |
| East Carroll Parish, LA | 1.79% | 11.8% |
| Yuma County, AZ | 11.5% | 12.4% |
| Kenedy County, TX | 0% | 7.8% |
| Below the line | ||
| Aleutians East Borough, AK | 6.67% | 2.3% |
| Kent County, RI | 6.77% | 2.7% |
| Bristol County, RI | 5.72% | 2.5% |
| Roseau County, MN | 6.28% | 2.7% |
| Winnebago County, IA | 7.85% | 3.3% |
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 | +0.539 | 3,121 counties reporting both |
| After adjusting for density, income and education | +0.614 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.594 | size taken out as well as density |
| Also comparing only within each state | +0.630 | 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 | +0.706 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 287 equally weighted counties |
| Middle value inside single states | +0.616 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.566 | 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 = 2.3e-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 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 +0.54 (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 returns reporting unemployment compensation rose most, did unemployment ratemove 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 | 3,143 | +0.35 | +0.50 | 4.5e-14 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,143 | +0.32 | +0.56 | 2.2e-17 |
In counties where one changed more, the other tended to move the same way, compared with other counties in the same state (r = +0.50). The same holds in the second period. How change is measured.
Related pairs
- Received food stamps and Unemployment rate — +0.44
- Taking blood pressure medication and Returns reporting unemployment compensation — -0.41
- Share of suicides by firearm and Returns reporting unemployment compensation — -0.39
- Firearm share of suicides, standardised and Returns reporting unemployment compensation — -0.39
- Poverty rate and Unemployment rate — +0.39
- Returns reporting unemployment compensation and Mobility score — +0.38
- Republican share of the two-party vote, 2008 and Returns reporting unemployment compensation — -0.36
- Black-white residential segregation and Returns reporting unemployment compensation — +0.35
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 reporting unemployment compensation and Unemployment rate