placestats

Share Asian, not Hispanic and Presidential votes cast, 2000

A county is not a voter. Read either way, this says one thing and no more. Counties that voted more Republican also tend to differ on the other measure, and counties that voted less Republican tend to differ the other way. It does not follow that the people who voted that way are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. It is the most common misuse of exactly this data. How a county votes also goes so closely with its density, age and education that much of what looks political here is really those. That is what the adjusted figures are for. The vote shares run from 2000 to 2024, and the links here are measured in 2023.

Where Share Asian, not Hispanic is higher, Presidential votes cast, 2000 tends to be higher. The link is weak: +0.31 across 1,995 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 38 states, the middle value of the link is +0.60.

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

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

Presidential votes cast, 2000

The total number of presidential votes cast in 2000.

MIT Election Data and Science Lab · MIT county presidential returns

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 share Asian, not Hispanic

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Honolulu County, HI42%255.05
Santa Clara County, CA39.3%287.99
San Francisco County, CA34.7%382.38
Alameda County, CA32%299.25
San Mateo County, CA30.5%348.14
Lowest
Yuma County, CO0%439.27
San Juan County, CO0%631.88
Lincoln County, CO0%395.43
Kiowa County, CO0%713.86
Jackson County, CO0%650.49

By presidential votes cast, 2000

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Blaine County, NE0%906.49
Garfield County, MT0%793.18
Harding County, NM0%792.78
Sharkey County, MS0%754.42
Kent County, TX0%737.28
Lowest
Pinal County, AZ1.57%91.92
Hidalgo County, TX0.993%114.71
Webb County, TX0.456%117.92
Chattahoochee County, GA2.97%131.47
Hays County, TX1.82%133.83
States — Presidential votes cast, 2000 is not published for states

By share Asian, not Hispanic

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Hawaii36.7%—
California15.1%—
New Jersey9.83%—
Washington9.31%—
New York8.83%—
Lowest
Puerto Rico0.082%—
West Virginia0.747%—
Wyoming0.798%—
Montana0.809%—
Mississippi0.965%—
Metro areas

By share Asian, not Hispanic

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Urban Honolulu, HI42%258.73
San Jose-Sunnyvale-Santa Clara, CA38.1%290.35
Kapaa, HI29%294.82
Kahului-Wailuku, HI27.8%238.93
San Francisco-Oakland-Fremont, CA27.4%345.57
Lowest
Raymondville, TX0%252.63
Mitchell, SD0%396.13
Las Vegas, NM0%368.68
Iron Mountain, MI-WI0%496.90
Hailey, ID0%315.83

By presidential votes cast, 2000

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Albert Lea, MN3.07%528.89
Fairmont, MN0.564%527.60
Mason City, IA1.21%526.45
Los Alamos, NM4.98%525.51
Burlington, IA-IL1.14%523.92
Lowest
McAllen-Edinburg-Mission, TX0.993%112.47
Laredo, TX0.456%117.30
Jefferson, GA2.08%131.30
Yuma, AZ1.08%134.38
Hinesville, GA1.75%138.35
Cities and towns — Presidential votes cast, 2000 is not published for cities and towns

By share Asian, not Hispanic

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
Cupertino city, CA71.7%—
Milpitas city, CA71.5%—
South San Gabriel CDP, CA69.5%—
San Marino city, CA68.9%—
Waipahu CDP, HI67.1%—
Lowest
Terryville CDP, CT0%—
Woodland Park city, CO0%—
Lochbuie town, CO0%—
Eagle town, CO0%—
Mecca CDP, CA0%—
ZIP codes — Presidential votes cast, 2000 is not published for ZIP codes

By share Asian, not Hispanic

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Highest
ZIP 94539 (Fremont city, CA)78.4%—
ZIP 11355 (New York city, NY)74.5%—
ZIP 94555 (Fremont city, CA)73.2%—
ZIP 95035 (Milpitas city, CA)71.3%—
ZIP 95014 (Santa Clara County, CA)71.3%—
Lowest
ZIP 08734 (Ocean County, NJ)0%—
ZIP 08533 (Ocean County, NJ)0%—
ZIP 08059 (Mount Ephraim borough, NJ)0%—
ZIP 07524 (Paterson city, NJ)0%—
ZIP 06331 (Windham County, CT)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,006 counties: the five with far more presidential votes cast, 2000 than their share Asian, not Hispanic would suggest, and the five with far less.

Share Asian, not HispanicPresidential votes cast, 2000, per 1,000 residents
Above the line
Blaine County, NE0%906.49
Garfield County, MT0%793.18
Harding County, NM0%792.78
Sharkey County, MS0%754.42
Kent County, TX0%737.28
Below the line
Pinal County, AZ1.57%91.92
Webb County, TX0.456%117.92
Hidalgo County, TX0.993%114.71
Yuma County, AZ1.08%137.96
Hays County, TX1.82%133.83

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+0.4951,995 counties reporting both
After adjusting for density, income and education+0.308the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.279size taken out as well as density
Also comparing only within each state+0.273each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.264the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 258 equally weighted counties
Middle value inside single states+0.596the link worked out inside each of 38 states with at least 20 counties reporting
Using ranks, after adjusting+0.197uses 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 = 6.4e-45. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.001. 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 3.3e-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 +0.20 (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?

Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.

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 Share Asian, not Hispanic and Presidential votes cast, 2000