Gross domestic product and Threat of utility shut-off
Counting counties equally, Gross domestic product and Threat of utility shut-off have a link of +0.10 across 2,235 counties, on a scale from −1 to +1. Giving bigger places more weight asks about the average resident rather than the average place, and it flips the link to -0.15. Neither figure is likely to be chance. So this is a real disagreement about who is being described.
Flips when bigger places count more
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
Gross domestic product
Gross domestic product: the value of everything produced in the area, in dollars of that year (not adjusted for inflation).
BEA Regional Accounts · BEA CAGDP2 line 1
Threat of utility shut-off
The estimated percentage of adults who received a notice threatening to shut off a utility 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 SHUTUTILITY, age-adjusted
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 gross domestic product
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| Eureka County, NV | 934,880 | 13.1% |
| North Slope Borough, AK | 724,167 | 9.7% |
| Butte County, ID | 659,156 | 10.8% |
| Storey County, NV | 636,869 | 6.7% |
| New York County, NY | 574,810 | 7.1% |
| Lowest | ||
| Carroll County, MS | 13,738 | 13.1% |
| Echols County, GA | 13,853 | 9.3% |
| Glascock County, GA | 14,046 | 9.3% |
| Lincoln County, WV | 15,350 | 14.8% |
| Greene County, MS | 15,598 | 11% |
By threat of utility shut-off
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| East Carroll Parish, LA | 24,998 | 27.8% |
| Madison Parish, LA | 50,873 | 25.5% |
| Greene County, AL | 36,519 | 25.5% |
| McKinley County, NM | 41,150 | 25% |
| Kusilvak Census Area, AK | 21,443 | 24.9% |
| Lowest | ||
| Ozaukee County, WI | 75,062 | 4.1% |
| Waukesha County, WI | 98,467 | 4.2% |
| Arlington County, VA | 199,216 | 4.3% |
| Loudoun County, VA | 98,384 | 4.4% |
| Forsyth County, GA | 57,705 | 4.4% |
States — Threat of utility shut-off is not published for states
By gross domestic product
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| District of Columbia | 259,270 | — |
| New York | 109,826 | — |
| Massachusetts | 105,525 | — |
| Washington | 103,535 | — |
| Delaware | 102,727 | — |
| Lowest | ||
| Mississippi | 51,334 | — |
| West Virginia | 56,869 | — |
| Arkansas | 58,731 | — |
| Alabama | 61,244 | — |
| Kentucky | 62,799 | — |
Metro areas
By gross domestic product
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| San Jose-Sunnyvale-Santa Clara, CA | 209,413 | 4.81% |
| Williston, ND | 186,033 | 6.7% |
| Los Alamos, NM | 181,074 | 4.8% |
| Carlsbad-Artesia, NM | 172,493 | 10.8% |
| San Francisco-Oakland-Fremont, CA | 166,505 | 5.66% |
| Lowest | ||
| Winchester, VA-WV | 3470.05 | 7.88% |
| Charlottesville, VA | 11,224 | 6.29% |
| Lynchburg, VA | 15,532 | 8.45% |
| Blacksburg-Christiansburg-Radford, VA | 17,826 | 7.74% |
| Picayune, MS | 25,165 | 9.3% |
By threat of utility shut-off
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| Gallup, NM | 42,570 | 25% |
| Clarksdale, MS | 42,780 | 22.5% |
| Greenville, MS | 42,407 | 20.4% |
| Selma, AL | 45,608 | 20.4% |
| Forrest City, AR | 41,589 | 19.9% |
| Lowest | ||
| Los Alamos, NM | 181,074 | 4.8% |
| San Jose-Sunnyvale-Santa Clara, CA | 209,413 | 4.81% |
| Madison, WI | 97,073 | 4.9% |
| Heber, UT | 94,686 | 4.97% |
| Appleton, WI | 75,769 | 5.06% |
Cities and towns — Gross domestic product is not published for cities and towns
By threat of utility shut-off
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Highest | ||
| Benton Harbor city, MI | — | 34.6% |
| Glasgow Village CDP, MO | — | 33.3% |
| Buena Vista CDP, MI | — | 33.2% |
| Tallulah city, LA | — | 32.3% |
| Jennings city, MO | — | 31.8% |
| Lowest | ||
| San Marino city, CA | — | 2.6% |
| Cupertino city, CA | — | 2.7% |
| Saratoga city, CA | — | 2.8% |
| Los Altos city, CA | — | 2.8% |
| Palos Verdes Estates city, CA | — | 2.9% |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,246 counties: the five with far more threat of utility shut-off than their gross domestic product would suggest, and the five with far less.
| Gross domestic product, per 1,000 residents | Threat of utility shut-off | |
|---|---|---|
| Above the line | ||
| East Carroll Parish, LA | 24,998 | 27.8% |
| Eureka County, NV | 934,880 | 13.1% |
| Madison Parish, LA | 50,873 | 25.5% |
| Greene County, AL | 36,519 | 25.5% |
| McKinley County, NM | 41,150 | 25% |
| Below the line | ||
| Ozaukee County, WI | 75,062 | 4.1% |
| Forsyth County, GA | 57,705 | 4.4% |
| St. Croix County, WI | 60,206 | 4.5% |
| Oconee County, GA | 53,772 | 4.6% |
| Carver County, MN | 66,308 | 4.5% |
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 | -0.093 | 2,235 counties reporting both |
| After adjusting for density, income and education | +0.101 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.079 | size taken out as well as density |
| Also comparing only within each state | +0.090 | 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 | -0.154 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 187 equally weighted counties |
| Middle value inside single states | -0.144 | the link worked out inside each of 30 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.167 | 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 = 1.7e-6. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.004. 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 3.1e-6.
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 +0.61 (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
- Threat of utility shut-off and Returns claiming the earned income credit — +0.80
- Threat of utility shut-off and Homicide rate, annual — +0.72
- Threat of utility shut-off and Homicide rate — +0.71
- Threat of utility shut-off and Children in single-parent households — +0.69
- Threat of utility shut-off and Average test score — -0.65
- Threat of utility shut-off and Social connection score — -0.63
- Threat of utility shut-off and Years of potential life lost — +0.59
- Threat of utility shut-off and Premature mortality — +0.58
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 Gross domestic product and Threat of utility shut-off