placestats

Lack of reliable transportation and Mean adjusted gross income per return

Across 2,272 counties, Lack of reliable transportation and Mean adjusted gross income per return have a link of -0.56, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and what is left could easily be chance. Most of the first figure came from both measures following how urban, rich and educated a place is, not from a link with each other.

Mostly gone 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

Lack of reliable transportation

The estimated percentage of adults who lacked reliable transport for daily life in the past year. It is adjusted so places with older or younger populations can be compared fairly. It is an estimate from a statistical model, not a direct count from a local survey.

CDC PLACES · PLACES LACKTRPT, age-adjusted

Mean adjusted gross income per return

Average adjusted gross income (income after a few deductions, as reported to the IRS) per tax return. Business and farm losses are subtracted. It counts only people who file, and people who earn too little to have to file are missing, which makes the poorest places look better off.

IRS Statistics of Income · IRS SOI A00100 / N1

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 lack of reliable transportation

Lack of reliable transportationMean adjusted gross income per return
Highest
Kusilvak Census Area, AK26.8%$27,083
McKinley County, NM24%$44,629
Sioux County, ND24%$46,466
East Carroll Parish, LA23.5%$44,004
Humphreys County, MS23%$40,573
Lowest
Falls Church city, VA4.1%$209,630
Arlington County, VA4.7%$159,170
Los Alamos County, NM4.7%$138,590
Delaware County, OH4.8%$139,360
Hamilton County, IN4.8%$150,000

By mean adjusted gross income per return

Lack of reliable transportationMean adjusted gross income per return
Highest
Summit County, UT4.9%$265,720
New York County, NY7.7%$257,310
Blaine County, ID6.5%$252,880
Western Connecticut Planning Region, CT7.1%$241,530
Marin County, CA6.2%$236,960
Lowest
Kusilvak Census Area, AK26.8%$27,083
Holmes County, MS22.5%$37,015
Quitman County, MS21.4%$39,003
Noxubee County, MS19.1%$39,883
Perry County, AL20.7%$40,388
States — Lack of reliable transportation is not published for states

By mean adjusted gross income per return

Lack of reliable transportationMean adjusted gross income per return
Highest
District of Columbia—$143,630
Connecticut—$121,890
Massachusetts—$121,200
Washington—$114,940
New Jersey—$112,230
Lowest
Mississippi—$65,732
West Virginia—$68,201
New Mexico—$69,650
Louisiana—$73,396
Kentucky—$73,748
Metro areas

By lack of reliable transportation

Lack of reliable transportationMean adjusted gross income per return
Highest
Gallup, NM24%$44,629
Clarksdale, MS20.9%$52,539
Greenville, MS19.5%$50,176
Greenwood, MS18.2%$60,889
Cleveland, MS18%$56,684
Lowest
Los Alamos, NM4.7%$138,590
Heber, UT5.12%$220,380
Madison, WI5.81%$103,320
Bismarck, ND5.83%$97,247
Pella, IA5.9%$94,041

By mean adjusted gross income per return

Lack of reliable transportationMean adjusted gross income per return
Highest
Jackson, WY-ID6.4%$387,550
Hailey, ID7.06%$220,660
Heber, UT5.12%$220,380
San Jose-Sunnyvale-Santa Clara, CA6.61%$211,950
Bridgeport-Stamford-Danbury, CT8.41%$199,610
Lowest
Deming, NM17%$41,040
Gallup, NM24%$44,629
Las Vegas, NM12.7%$46,955
Lumberton, NC17.6%$48,158
Forrest City, AR16.6%$48,484
Cities and towns — Mean adjusted gross income per return is not published for cities and towns

By lack of reliable transportation

Lack of reliable transportationMean adjusted gross income per return
Highest
Benton Harbor city, MI27%—
Tallulah city, LA26.6%—
East Bakersfield CDP, CA26.2%—
Buena Vista CDP, MI26%—
Yazoo City city, MS25.9%—
Lowest
Difficult Run CDP, VA3.4%—
Great Falls CDP, VA3.5%—
Little Silver borough, NJ3.5%—
Scarsdale village, NY3.6%—
Fair Haven borough, NJ3.6%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,283 counties: the five with far more mean adjusted gross income per return than their lack of reliable transportation would suggest, and the five with far less.

Lack of reliable transportationMean adjusted gross income per return
Above the line
New York County, NY7.7%$257,310
Summit County, UT4.9%$265,720
Blaine County, ID6.5%$252,880
Western Connecticut Planning Region, CT7.1%$241,530
San Mateo County, CA6.5%$236,960
Below the line
Blaine County, NE7%$40,970
Thomas County, NE6%$48,991
Petroleum County, MT7.5%$47,891
Logan County, NE5.9%$58,526
Arthur County, NE6.7%$55,448

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-0.5622,272 counties reporting both
After adjusting for density, income and education-0.008the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.009size taken out as well as density
Also comparing only within each state-0.002each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.104the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 192 equally weighted counties
Middle value inside single states-0.698the link worked out inside each of 30 states with at least 20 counties reporting
Using ranks, after adjusting-0.250uses 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 = 0.690. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.870. 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 0.720.

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 +0.18 (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 Lack of reliable transportation and Mean adjusted gross income per return