Population for whom poverty status is determined and Share Asian, not Hispanic
Where Population for whom poverty status is determined is higher, Share Asian, not Hispanic tends to be higher. The link is weak: +0.30 across 2,057 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 39 states, the middle value of the link is +0.64.
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
Population for whom poverty status is determined
The people whose poverty status can be worked out, used as the base for the poverty rate. It leaves out people in institutions, military barracks and college dorms, so it is a little smaller than the population.
Census ACS 5-year · ACS table B17001_001
Share Asian, not Hispanic
The percentage of residents who are Asian alone and not Hispanic.
Census ACS 5-year · ACS table B03002_006, divided by total population
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 population for whom poverty status is determined
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Quitman County, GA | 1000.00 | 0% |
| Schley County, GA | 1000.00 | 0% |
| Camas County, ID | 1000.00 | 0% |
| Golden Valley County, MT | 1000.00 | 0% |
| Arthur County, NE | 1000.00 | 0% |
| Lowest | ||
| Chattahoochee County, GA | 520.10 | 2.97% |
| Lexington city, VA | 562.53 | 3.21% |
| Williamsburg city, VA | 691.08 | 6.75% |
| East Carroll Parish, LA | 755.80 | 0% |
| Bee County, TX | 767.68 | 0.584% |
By share Asian, not Hispanic
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Honolulu County, HI | 969.05 | 42% |
| Aleutians West Census Area, AK | 959.83 | 40.7% |
| Santa Clara County, CA | 983.29 | 39.3% |
| San Francisco County, CA | 984.50 | 34.7% |
| Alameda County, CA | 983.14 | 32% |
| Lowest | ||
| Yuma County, CO | 966.54 | 0% |
| San Juan County, CO | 1000.00 | 0% |
| Lincoln County, CO | 777.74 | 0% |
| Kiowa County, CO | 980.09 | 0% |
| Jackson County, CO | 995.78 | 0% |
States
By population for whom poverty status is determined
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Puerto Rico | 991.22 | 0.082% |
| Nevada | 987.07 | 8.54% |
| Utah | 984.09 | 2.32% |
| Washington | 982.60 | 9.31% |
| New Jersey | 982.12 | 9.83% |
| Lowest | ||
| Rhode Island | 961.78 | 3.42% |
| District of Columbia | 963.98 | 4.01% |
| Vermont | 964.57 | 1.63% |
| North Dakota | 965.56 | 1.54% |
| Mississippi | 966.24 | 0.965% |
By share Asian, not Hispanic
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Hawaii | 975.00 | 36.7% |
| California | 981.81 | 15.1% |
| New Jersey | 982.12 | 9.83% |
| Washington | 982.60 | 9.31% |
| New York | 975.83 | 8.83% |
| Lowest | ||
| Puerto Rico | 991.22 | 0.082% |
| West Virginia | 968.70 | 0.747% |
| Wyoming | 976.13 | 0.798% |
| Montana | 976.59 | 0.809% |
| Mississippi | 966.24 | 0.965% |
Metro areas
By population for whom poverty status is determined
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Selma, AL | 1024.78 | 0.602% |
| Gallup, NM | 1023.13 | 1.3% |
| Greenville, MS | 1020.67 | 0.801% |
| Morgan City, LA | 1015.96 | 1.31% |
| Edwards, CO | 1012.04 | 1.27% |
| Lowest | ||
| Huntsville, TX | 762.27 | 1.08% |
| Beeville, TX | 771.41 | 0.584% |
| Lewisburg, PA | 809.81 | 1.65% |
| Palestine, TX | 809.95 | 0.571% |
| Fort Leonard Wood, MO | 814.87 | 2.06% |
By share Asian, not Hispanic
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Urban Honolulu, HI | 983.02 | 42% |
| San Jose-Sunnyvale-Santa Clara, CA | 995.54 | 38.1% |
| Kapaa, HI | 986.42 | 29% |
| Kahului-Wailuku, HI | 992.49 | 27.8% |
| San Francisco-Oakland-Fremont, CA | 1005.53 | 27.4% |
| Lowest | ||
| Raymondville, TX | 930.68 | 0% |
| Mitchell, SD | 968.91 | 0% |
| Las Vegas, NM | 976.39 | 0% |
| Iron Mountain, MI-WI | 982.52 | 0% |
| Hailey, ID | 971.47 | 0% |
Cities and towns
By population for whom poverty status is determined
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| North Star CDP, DE | 1000.00 | 11.6% |
| Apollo Beach CDP, FL | 1000.00 | 2.29% |
| Bay Harbor Islands town, FL | 1000.00 | 0% |
| Fort Pierce North CDP, FL | 1000.00 | 0% |
| Gladeview CDP, FL | 1000.00 | 0% |
| Lowest | ||
| Stony Brook University CDP, NY | 0.00 | 47.1% |
| Binghamton University CDP, NY | 0.00 | 11.9% |
| Rutgers University-Busch Campus CDP, NJ | 36.19 | 17.5% |
| Notre Dame CDP, IN | 90.91 | 8.28% |
| University of California-Santa Barbara CDP, CA | 141.74 | 30.7% |
By share Asian, not Hispanic
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| Cupertino city, CA | 994.01 | 71.7% |
| Milpitas city, CA | 975.71 | 71.5% |
| South San Gabriel CDP, CA | 974.27 | 69.5% |
| San Marino city, CA | 1000.00 | 68.9% |
| Waipahu CDP, HI | 986.38 | 67.1% |
| Lowest | ||
| Terryville CDP, CT | 999.80 | 0% |
| Woodland Park city, CO | 998.74 | 0% |
| Lochbuie town, CO | 997.91 | 0% |
| Eagle town, CO | 990.51 | 0% |
| Mecca CDP, CA | 991.37 | 0% |
ZIP codes
By population for whom poverty status is determined
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| ZIP 10005 (New York city, NY) | 1000.00 | 14.2% |
| ZIP 10069 (New York city, NY) | 1000.00 | 29.2% |
| ZIP 10280 (New York city, NY) | 1000.00 | 27.8% |
| ZIP 10282 (New York city, NY) | 1000.00 | 15.2% |
| ZIP 10514 (Westchester County, NY) | 1000.00 | 12.7% |
| Lowest | ||
| ZIP 93410 (California Polytechnic State University CDP, CA) | 0.00 | 16.8% |
| ZIP 92055 (San Diego County, CA) | 0.00 | 5.16% |
| ZIP 80913 (Fort Carson CDP, CO) | 0.00 | 3.01% |
| ZIP 77843 (College Station city, TX) | 0.00 | 11.3% |
| ZIP 32816 (University CDP, FL) | 0.00 | 3.97% |
By share Asian, not Hispanic
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Highest | ||
| ZIP 94539 (Fremont city, CA) | 994.56 | 78.4% |
| ZIP 11355 (New York city, NY) | 995.44 | 74.5% |
| ZIP 94555 (Fremont city, CA) | 998.37 | 73.2% |
| ZIP 95035 (Milpitas city, CA) | 975.79 | 71.3% |
| ZIP 95014 (Santa Clara County, CA) | 994.21 | 71.3% |
| Lowest | ||
| ZIP 08734 (Ocean County, NJ) | 1000.00 | 0% |
| ZIP 08533 (Ocean County, NJ) | 1000.00 | 0% |
| ZIP 08059 (Mount Ephraim borough, NJ) | 995.37 | 0% |
| ZIP 07524 (Paterson city, NJ) | 1000.00 | 0% |
| ZIP 06331 (Windham County, CT) | 1000.00 | 0% |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,069 counties: the five with far more share Asian, not Hispanic than their population for whom poverty status is determined would suggest, and the five with far less.
| Population for whom poverty status is determined, per 1,000 residents | Share Asian, not Hispanic | |
|---|---|---|
| Above the line | ||
| Honolulu County, HI | 969.05 | 42% |
| Aleutians West Census Area, AK | 959.83 | 40.7% |
| Santa Clara County, CA | 983.29 | 39.3% |
| San Francisco County, CA | 984.50 | 34.7% |
| Alameda County, CA | 983.14 | 32% |
| Below the line | ||
| San Juan County, CO | 1000.00 | 0% |
| Hinsdale County, CO | 1000.00 | 0% |
| Dolores County, CO | 1000.00 | 0% |
| Vieques Municipio, PR | 1000.00 | 0% |
| Terrell County, TX | 1000.00 | 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 | +0.456 | 2,057 counties reporting both |
| After adjusting for density, income and education | +0.304 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.272 | size taken out as well as density |
| Also comparing only within each state | +0.255 | 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 | +0.280 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 261 equally weighted counties |
| Middle value inside single states | +0.636 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.357 | 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 = 3.4e-45. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.5e-10. 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 1.8e-44.
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 +0.33 (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 population for whom poverty status is determined rose most, did share asian, not hispanicmove 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 | 1,647 | +0.15 | +0.16 | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 1,731 | +0.11 | +0.12 | — |
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
- Homeownership rate and Share Asian, not Hispanic — -0.40
- Population for whom poverty status is determined and Share foreign-born — +0.39
- Median gross rent and Share Asian, not Hispanic — +0.37
- Share Asian, not Hispanic and Personal income — +0.36
- Population with a bachelor's degree or higher and Share Asian, not Hispanic — +0.35
- Share Asian, not Hispanic and Local income tax collected — +0.35
- Foreign-born population and Share Asian, not Hispanic — +0.34
- Share Asian, not Hispanic and Gross domestic product — +0.34
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 Population for whom poverty status is determined and Share Asian, not Hispanic