Unemployed
People without a job who looked for one in the past four weeks and could start work.
Reported for3,221areas, in 52 states
Link to density0.84mostly reflects how urban a place is
Nearby places alike0.32Moran’s I: strongly, neighbouring places tend to be similar
Typecountcounts rise and fall together from size alone
How it has moved
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Every state, every year
One row per state and one column per year, sorted by the latest value. It shows states, not counties: nobody can scan 3,222 rows, and the pattern only shows when it all fits on one screen.
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Where it comes from
BLS LAUS · BLS LAUS county annual averages
Combining areas: added up across areas.
What it goes with
The strongest links that have a page of their own. Unadjusted is the link before adjusting for anything, from −1 (opposite) to +1 (in step). Adjusted compares places with the same population density, income and education levels, so those differences can’t explain the link. This list leaves out the 2 pairs built from the same numbers or the same model as this measure, since they are bound to match.
| Measure | Unadjusted | Adjusted |
|---|---|---|
| Unemployed and Share foreign-bornWhere Unemployed is higher, Share foreign-born tends to be higher: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Share foreign-born by US county | +0.46 | +0.37 |
Population makeup
How this measure varies with the racial and ethnic makeup of counties. Every group is shown, whatever its rank. These are links across counties: they describe places, not people. They say nothing about any person’s risk or behaviour based on their race. Counties with a larger share of one group also differ in much else, such as income, segregation, region and history. The adjusted figure only accounts for density, income and education.
| Share of residents | Unadjusted | Adjusted |
|---|---|---|
| Unemployed and Share White, not HispanicWhere Unemployed is higher, Share White, not Hispanic tends to be lower: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Share White, not Hispanic by US county | -0.24 | -0.20 |
| Unemployed and Share Black, not HispanicCould easily be chance once only places with similar density, income and education are compared. Share Black, not Hispanic by US county | +0.02 | +0.02 |
| Unemployed and Share Hispanic or Latino, any raceCould easily be chance once only places with similar density, income and education are compared. Share Hispanic or Latino, any race by US county | +0.13 | +0.12 |
| Unemployed and Share Asian, not HispanicWhere Unemployed is higher, Share Asian, not Hispanic tends to be higher: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Share Asian, not Hispanic by US county | +0.42 | +0.29 |
| Unemployed and Share American Indian or Alaska Native, not HispanicCould easily be chance once only places with similar density, income and education are compared. Share American Indian or Alaska Native, not Hispanic by US county | -0.08 | +0.07 |
Highest and lowest, per 1,000 residents
Of 3,219 counties reporting it. This is a count, so it is ranked per 1,000 residents, not as published. Ranked by raw totals, it would just list the largest counties in the country. Its link to population is 0.84, so the raw order would match almost every other count here. The published totals are in the table on each place page.
Highest
- Imperial County, CA — 70.26 (12,599 total)
- Colusa County, CA — 62.66 (1,372 total)
- Yuma County, AZ — 58.55 (12,160 total)
- Tulare County, CA — 44.71 (21,271 total)
- Mackinac County, MI — 44.55 (484 total)
Lowest
- Bowman County, ND — 6.81 (20 total)
- Bon Homme County, SD — 7.26 (51 total)
- Faulk County, SD — 7.49 (16 total)
- Brule County, SD — 7.79 (41 total)
- Divide County, ND — 7.82 (17 total)
The middle area reports 15.78. The middle half of counties falls between 13.69 and 18.58 (the 25th to 75th percentile).
All measures · Explore its pairs · How the US county data works