Unemployment rate and Medicare enrollees also on Medicaid
Where Unemployment rate is higher, Medicare enrollees also on Medicaid tends to be higher. The link is weak: +0.40 across 3,105 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 +0.46.
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
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
Medicare enrollees also on Medicaid
The percentage of people in traditional Medicare who could also get Medicaid for at least one month of the year. Medicaid is the health program for people with low incomes, so this picks out older and disabled people who are also poor. People on both are often called dual eligible.
CMS Medicare Geographic Variation Public Use File · CMS Medicare Geographic Variation PUF, national/state/county, all ages, BENE_DUAL_PCT
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 unemployment rate
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| Imperial County, CA | 17.6% | 57.6 |
| Kusilvak Census Area, AK | 14.8% | 70.2 |
| Yuma County, AZ | 12.4% | 15.2 |
| Colusa County, CA | 12.4% | 36.3 |
| East Carroll Parish, LA | 11.8% | 33.1 |
| Lowest | ||
| Loving County, TX | 1.3% | 0 |
| Brule County, SD | 1.3% | 16.5 |
| Sanborn County, SD | 1.4% | 9.42 |
| Hand County, SD | 1.4% | 9.51 |
| Bowman County, ND | 1.4% | 8.4 |
By Medicare enrollees also on Medicaid
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 14.8% | 70.2 |
| Bethel Census Area, AK | 9.8% | 63.4 |
| Starr County, TX | 9.2% | 58 |
| Imperial County, CA | 17.6% | 57.6 |
| Northwest Arctic Borough, AK | 9.1% | 57.4 |
| Lowest | ||
| Loving County, TX | 1.3% | 0 |
| Beaufort County, SC | 2.7% | 2.05 |
| Summit County, UT | 2.6% | 2.43 |
| Sumter County, FL | 5.1% | 2.76 |
| Teton County, WY | 2.2% | 2.77 |
States
By unemployment rate
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| Nevada | 5.13% | 15.3 |
| District of Columbia | 4.86% | 37.1 |
| California | 4.72% | 30.6 |
| Illinois | 4.49% | 15.2 |
| New Jersey | 4.32% | 12.4 |
| Lowest | ||
| South Dakota | 1.82% | 12.2 |
| Vermont | 1.94% | 21.9 |
| North Dakota | 2.05% | 13.3 |
| Maryland | 2.24% | 17 |
| New Hampshire | 2.28% | 12.3 |
By Medicare enrollees also on Medicaid
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| District of Columbia | 4.86% | 37.1 |
| California | 4.72% | 30.6 |
| Maine | 2.65% | 26.6 |
| Connecticut | 3.2% | 24.9 |
| Massachusetts | 3.49% | 23.2 |
| Lowest | ||
| South Carolina | 2.99% | 6.69 |
| Arizona | 3.7% | 8.37 |
| Virginia | 2.7% | 8.72 |
| Utah | 2.72% | 8.76 |
| Hawaii | 2.85% | 9.47 |
Metro areas
By unemployment rate
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| El Centro, CA | 17.6% | 57.6 |
| Yuma, AZ | 12.4% | 15.2 |
| Deming, NM | 10.3% | 27 |
| Visalia, CA | 9.8% | 37.2 |
| Rio Grande City-Roma, TX | 9.2% | 58 |
| Lowest | ||
| Pierre, SD | 1.52% | 11.3 |
| Yankton, SD | 1.6% | 14.6 |
| Los Alamos, NM | 1.6% | 3.73 |
| Burlington-South Burlington, VT | 1.61% | 21.9 |
| Mitchell, SD | 1.63% | 14.4 |
By Medicare enrollees also on Medicaid
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | 9.2% | 58 |
| El Centro, CA | 17.6% | 57.6 |
| Eagle Pass, TX | 7.5% | 45.5 |
| Zapata, TX | 6.4% | 45.2 |
| Frankfort, KY | 3.76% | 40.6 |
| Lowest | ||
| Hilton Head Island-Bluffton-Port Royal, SC | 2.71% | 2.36 |
| Wildwood-The Villages, FL | 5.1% | 2.76 |
| Heber, UT | 2.51% | 2.94 |
| Los Alamos, NM | 1.6% | 3.73 |
| Naples-Marco Island, FL | 3% | 4.23 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,123 counties: the five with far more Medicare enrollees also on Medicaid than their unemployment rate would suggest, and the five with far less.
| Unemployment rate | Medicare enrollees also on Medicaid | |
|---|---|---|
| Above the line | ||
| San Francisco County, CA | 3.4% | 50.5 |
| Franklin County, KY | 3.7% | 50.3 |
| Todd County, SD | 3.9% | 50.7 |
| Oglala Lakota County, SD | 4.7% | 52 |
| Wayne County, NY | 3.2% | 43.9 |
| Below the line | ||
| Yuma County, AZ | 12.4% | 15.2 |
| Garfield County, UT | 7.9% | 7.75 |
| Cape May County, NJ | 8% | 10.4 |
| Mackinac County, MI | 9.1% | 14.6 |
| Sumter County, FL | 5.1% | 2.76 |
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 | +0.480 | 3,105 counties reporting both |
| After adjusting for density, income and education | +0.396 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.369 | size taken out as well as density |
| Also comparing only within each state | +0.235 | 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 | +0.408 | 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 | +0.462 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.280 | 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 = 8.3e-117. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.9e-6. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 9.2e-116.
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 +0.52 (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 unemployment rate rose most, did Medicare enrollees also on Medicaidmove 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 | 3,125 | +0.05 | +0.01 | 0.877 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,120 | +0.20 | +0.10 | 0.027 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Returns reporting unemployment compensation and Unemployment rate — +0.61
- Received food stamps and Medicare enrollees also on Medicaid — +0.53
- Medicare enrollees also on Medicaid and Poverty rate (model-based) — +0.49
- Lack of reliable transportation and Medicare enrollees also on Medicaid — +0.49
- Medicare enrollees also on Medicaid and Material security score — -0.47
- Poverty rate and Medicare enrollees also on Medicaid — +0.45
- Lacking social and emotional support and Medicare enrollees also on Medicaid — +0.45
- Received food stamps and Unemployment rate — +0.44
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 Unemployment rate and Medicare enrollees also on Medicaid