placestats

Poverty rate and Unemployment rate

Where Poverty rate is higher, Unemployment rate tends to be higher. The link is weak: +0.39 across 3,139 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 41 states, the middle value of the link is +0.55.

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

Poverty rate

The percentage of people whose family income is below the federal poverty line. The line depends on how many people are in the family and their ages.

Census ACS 5-year · ACS table B17001_002, divided by population for whom poverty status is determined

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 poverty rate

By unemployment rate

Poverty rateUnemployment rate
Highest
Imperial County, CA19.6%17.6%
Kusilvak Census Area, AK31.2%14.8%
Maricao Municipio, PR52.6%13.3%
Maunabo Municipio, PR52.8%13%
Yuma County, AZ16.5%12.4%
Lowest
Brule County, SD10.5%1.3%
Sanborn County, SD11.4%1.4%
Hand County, SD6.61%1.4%
Bowman County, ND9.5%1.4%
Chittenden County, VT10.4%1.5%
States

By poverty rate

Poverty rateUnemployment rate
Highest
Puerto Rico41.6%5.89%
Mississippi19.1%3.12%
Louisiana18.9%3.72%
New Mexico18.1%3.68%
West Virginia16.6%3.86%
Lowest
New Hampshire7.16%2.28%
Utah8.56%2.72%
Minnesota9.2%2.78%
Maryland9.35%2.24%
Colorado9.38%3.28%

By unemployment rate

Poverty rateUnemployment rate
Highest
Puerto Rico41.6%5.89%
Nevada12.6%5.13%
District of Columbia14.5%4.86%
California12%4.72%
Illinois11.7%4.49%
Lowest
South Dakota12%1.82%
Vermont10.3%1.94%
North Dakota10.6%2.05%
Maryland9.35%2.24%
New Hampshire7.16%2.28%
Metro areas

By poverty rate

Poverty rateUnemployment rate
Highest
Lares, PR54.9%8.5%
Utuado, PR53.5%9.1%
Guayama, PR49.8%9.17%
Coamo, PR49.7%7.11%
Coco, PR48%7.6%
Lowest
Nantucket, MA3.03%5.8%
Heber, UT4.82%2.51%
Vineyard Haven, MA5.38%4.4%
Hood River, OR5.49%3.1%
Lexington Park, MD5.89%2.05%

By unemployment rate

Poverty rateUnemployment rate
Highest
El Centro, CA19.6%17.6%
Yuma, AZ16.5%12.4%
Deming, NM26.1%10.3%
Visalia, CA17.8%9.8%
Rio Grande City-Roma, TX33.5%9.2%
Lowest
Pierre, SD9.19%1.52%
Yankton, SD9.36%1.6%
Burlington-South Burlington, VT10.3%1.61%
Mitchell, SD11.4%1.63%
Sioux Falls, SD-MN8.1%1.66%
Cities and towns — Unemployment rate is not published for cities and towns

By poverty rate

Poverty rateUnemployment rate
Highest
Isla Vista CDP, CA69.3%—
University of California-Davis CDP, CA67.7%—
Caño Martin Peña comunidad, PR63%—
Guánica zona urbana, PR62.1%—
Utuado zona urbana, PR62%—
Lowest
Linton Hall CDP, VA1.54%—
Huntington Woods city, MI1.65%—
Trussville city, AL1.65%—
Hugo city, MN1.73%—
Franklin Farm CDP, VA1.8%—
ZIP codes — Unemployment rate is not published for ZIP codes

By poverty rate

Poverty rateUnemployment rate
Highest
ZIP 22807 (Harrisonburg city, VA)100%—
ZIP 33620 (Tampa city, FL)100%—
ZIP 48825 (East Lansing city, MI)100%—
ZIP 55455 (Minneapolis city, MN)100%—
ZIP 80310 (Boulder city, CO)100%—
Lowest
ZIP 92136 (San Diego city, CA)0%—
ZIP 12222 (Albany city, NY)0%—
ZIP 54913 (Outagamie County, WI)1.42%—
ZIP 55038 (Hugo city, MN)1.52%—
ZIP 76226 (Denton County, TX)1.53%—

Counties furthest from the pattern

Compared with the straight line that best fits all 3,149 counties: the five with far more unemployment rate than their poverty rate would suggest, and the five with far less.

Poverty rateUnemployment rate
Above the line
Imperial County, CA19.6%17.6%
Kusilvak Census Area, AK31.2%14.8%
Colusa County, CA10.8%12.4%
Yuma County, AZ16.5%12.4%
East Carroll Parish, LA35.7%11.8%
Below the line
Sioux County, ND42.7%2.4%
Mellette County, SD46.2%2.8%
Vieques Municipio, PR59.5%4.1%
Corson County, SD45.2%3.2%
Faulk County, SD25.6%1.5%

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+0.5523,139 counties reporting both
After adjusting for density, income and education+0.391the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.400size taken out as well as density
Also comparing only within each state+0.306each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.454the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 292 equally weighted counties
Middle value inside single states+0.547the link worked out inside each of 41 states with at least 20 counties reporting
Using ranks, after adjusting+0.202uses 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-115. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.1e-5. 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 7.2e-114.

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 +0.53 (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 poverty rate 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic3,147+0.12+0.080.110
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,123+0.08+0.050.191

Over the main period, any link between the changes could easily be chance. How change is measured.

Related pairs

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 Poverty rate and Unemployment rate