Frequent mental distress and Private job growth
Counting counties equally, Frequent mental distress and Private job growth have a link of +0.06 across 2,927 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 -0.14. 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
Frequent mental distress
The estimated percentage of adults who report poor mental health on 14 or more days a month. 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 MHLTH, age-adjusted
Private job growth
The percentage change in private-sector jobs from the year before.
BLS Quarterly Census of Employment and Wages · BLS QCEW annual averages, private, all industries, change in annual_avg_emplvl
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 frequent mental distress
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Mingo County, WV | 26.6% | 7.39 |
| McDowell County, WV | 26% | 1.31 |
| Hancock County, TN | 25.7% | 0.531 |
| Calhoun County, WV | 25.7% | 9.04 |
| Logan County, WV | 25.4% | 4.31 |
| Lowest | ||
| Fairfax County, VA | 12.7% | 0.978 |
| Montgomery County, MD | 12.8% | 1.44 |
| Aleutians West Census Area, AK | 13% | 6.65 |
| Aleutians East Borough, AK | 13% | 2.76 |
| Falls Church city, VA | 13% | 4.87 |
By private job growth
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Prairie County, MT | 20.9% | 112 |
| Washington County, AL | 19.6% | 99.8 |
| Schuyler County, MO | 19.4% | 65.4 |
| Calhoun County, AR | 19.7% | 63.6 |
| Upton County, TX | 19.3% | 51.1 |
| Lowest | ||
| Cimarron County, OK | 16.7% | -30.5 |
| Lynn County, TX | 18.9% | -29.8 |
| Mineral County, NV | 22.4% | -29 |
| Haskell County, TX | 19.1% | -28.9 |
| Loup County, NE | 14.9% | -28.6 |
States — Frequent mental distress is not published for states
By private job growth
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Florida | — | 3.5 |
| Texas | — | 3.46 |
| Alaska | — | 3.17 |
| Delaware | — | 3.08 |
| Nevada | — | 3.02 |
| Lowest | ||
| California | — | 0.138 |
| Massachusetts | — | 1.03 |
| Iowa | — | 1.18 |
| Indiana | — | 1.26 |
| Mississippi | — | 1.33 |
Metro areas
By frequent mental distress
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Beckley, WV | 24.3% | 4.25 |
| Gallup, NM | 23.7% | 2.42 |
| Newport, TN | 23.5% | 1.77 |
| Charleston, WV | 23.2% | 4.84 |
| Parkersburg-Vienna, WV | 23% | 3.94 |
| Lowest | ||
| Los Alamos, NM | 13% | 13 |
| Jackson, WY-ID | 13.8% | 4.25 |
| Bismarck, ND | 14.1% | 1.2 |
| San Jose-Sunnyvale-Santa Clara, CA | 14.1% | -0.00289 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 14.1% | 2.08 |
By private job growth
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Town of Pecos, TX | 17.9% | 13.8 |
| Los Alamos, NM | 13% | 13 |
| Camden, AR | 20.9% | 11.6 |
| Albemarle, NC | 18.8% | 10.7 |
| Natchitoches, LA | 21.3% | 10 |
| Lowest | ||
| Elkhart-Goshen, IN | 19.6% | -7.62 |
| Zapata, TX | 19% | -7.25 |
| Gaffney, SC | 19.8% | -6.76 |
| Greenwood, MS | 19.3% | -6.02 |
| Raymondville, TX | 18.3% | -4.7 |
Cities and towns — Private job growth is not published for cities and towns
By frequent mental distress
| Frequent mental distress | Private job growth | |
|---|---|---|
| Highest | ||
| Kiryas Joel village, NY | 25.7% | — |
| New Square village, NY | 25.4% | — |
| Tallulah city, LA | 25.3% | — |
| La Follette city, TN | 25.2% | — |
| Parkersburg city, WV | 25.2% | — |
| Lowest | ||
| Travilah CDP, MD | 10.6% | — |
| Loudoun Valley Estates CDP, VA | 10.7% | — |
| Princeton Meadows CDP, NJ | 10.7% | — |
| Floris CDP, VA | 10.8% | — |
| North Hills village, NY | 11% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,947 counties: the five with far more private job growth than their frequent mental distress would suggest, and the five with far less.
| Frequent mental distress | Private job growth | |
|---|---|---|
| Above the line | ||
| Prairie County, MT | 20.9% | 112 |
| Washington County, AL | 19.6% | 99.8 |
| Schuyler County, MO | 19.4% | 65.4 |
| Calhoun County, AR | 19.7% | 63.6 |
| Upton County, TX | 19.3% | 51.1 |
| Below the line | ||
| Cimarron County, OK | 16.7% | -30.5 |
| Lynn County, TX | 18.9% | -29.8 |
| Mineral County, NV | 22.4% | -29 |
| Haskell County, TX | 19.1% | -28.9 |
| Loup County, NE | 14.9% | -28.6 |
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 | -0.002 | 2,927 counties reporting both |
| After adjusting for density, income and education | +0.062 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.062 | size taken out as well as density |
| Also comparing only within each state | +0.012 | 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 | -0.138 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 263 equally weighted counties |
| Middle value inside single states | -0.062 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.026 | 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 = 0.001. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.007. 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 0.001.
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 +0.01 (p = 0.110). Once the adjustment is made, little map pattern is left.
Did they change together?
The figures above compare counties at one moment. This asks a different question: where frequent mental distress rose most, did private job growthmove 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 | — | — | — | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 2,906 | -0.03 | -0.00 | 0.984 |
Not measurable over the main period, so only the second period is shown. How change is measured.
Related pairs
- Frequent mental distress and Drug overdose deaths — +0.42
- Frequent mental distress and Premature mortality — +0.34
- Frequent mental distress and Injury deaths — +0.34
- Frequent mental distress and Adult income rank of children raised poor here — -0.33
- Frequent mental distress and Life expectancy — -0.32
- Frequent mental distress and Years of potential life lost — +0.31
- Frequent mental distress and Safety score — -0.30
- Frequent mental distress and Economic connectedness in high school — -0.24
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 Frequent mental distress and Private job growth