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Returns claiming the earned income credit and Days of 90°F or hotter

Where Returns claiming the earned income credit is higher, Days of 90°F or hotter tends to be higher. The link is moderate: +0.54 across 3,089 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 +0.23.

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

Returns claiming the earned income credit

The percentage of tax returns claiming the earned income tax credit, which goes to working households with low incomes. It is a count from tax records of the working poor, not a survey estimate.

IRS Statistics of Income · IRS SOI N59660 / N1

Days of 90°F or hotter

The number of days in the year when the high temperature reached 90°F or more. The high is the average over the county of NOAA's daily map of weather station readings, made of squares about 3 miles wide. So a county with hot valleys and cool mountains counts fewer days than its hottest town. A state's figure is the average of its counties, each weighted by its land area. Lower 48 states only, from 1951.

NOAA NCEI nClimGrid-Daily · NOAA NCEI nClimGrid-Daily v1.0.0 county averages (prcp/tmax/tmin, scaled), hot_days; states, counties averaged by land area

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 returns claiming the earned income credit

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Humphreys County, MS47.6%99
Tunica County, MS46.3%71
Holmes County, MS46%96
Oglala Lakota County, SD45.5%22
Zapata County, TX45.4%192
Lowest
Mineral County, CO0%0
Daggett County, UT0%1
Roberts County, TX0%86
McMullen County, TX0%141
Loving County, TX0%142

By days of 90°F or hotter

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Starr County, TX44.4%196
Zapata County, TX45.4%192
Hidalgo County, TX36.9%189
Brooks County, TX39.2%187
Jim Hogg County, TX32.7%186
Lowest
Teller County, CO9.54%0
Summit County, CO6.08%0
San Juan County, CO6.38%0
Saguache County, CO19.6%0
Routt County, CO5.71%0
States

By returns claiming the earned income credit

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Mississippi26.7%89
Louisiana24%114
Georgia20.6%62.7
Arkansas20.5%63.5
Alabama20.3%63.6
Lowest
New Hampshire7.91%1.69
Massachusetts9.56%3.43
Washington9.82%11.2
Minnesota9.98%8.01
Vermont10.1%0.566

By days of 90°F or hotter

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Texas19.4%118
Louisiana24%114
Florida19.1%105
Arizona15.3%90.2
Mississippi26.7%89
Lowest
Rhode Island12.2%0.396
Vermont10.1%0.566
New York15.1%0.947
Maine11.3%1.16
New Hampshire7.91%1.69
Metro areas

By returns claiming the earned income credit

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Zapata, TX45.4%192
Rio Grande City-Roma, TX44.4%196
Clarksdale, MS42%66
Greenville, MS41.2%88
Greenwood, MS38.7%87.9
Lowest
Los Alamos, NM3.7%11
Jackson, WY-ID6.19%0
Heber, UT6.24%0
Edwards, CO6.39%0
Nantucket, MA6.55%0

By days of 90°F or hotter

Returns claiming the earned income creditDays of 90°F or hotter
Highest
Rio Grande City-Roma, TX44.4%196
Zapata, TX45.4%192
McAllen-Edinburg-Mission, TX36.9%189
Alice, TX32.6%178
Yuma, AZ29.6%176
Lowest
Youngstown-Warren, OH16.7%0
Wooster, OH10.7%0
Wheeling, WV-OH14.4%0
Waynesville, NC14.7%0
Watertown-Fort Drum, NY17%0

Counties furthest from the pattern

Compared with the straight line that best fits all 3,108 counties: the five with far more days of 90°F or hotter than their returns claiming the earned income credit would suggest, and the five with far less.

Returns claiming the earned income creditDays of 90°F or hotter
Above the line
Kenedy County, TX0%158
Loving County, TX0%142
McMullen County, TX0%141
Maricopa County, AZ13.8%168
Terrell County, TX6.9%140
Below the line
Oglala Lakota County, SD45.5%22
Menominee County, WI35.6%2
Buffalo County, SD43.1%26
Todd County, SD40.7%24
McCreary County, KY32.5%4

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+0.5133,089 counties reporting both
After adjusting for density, income and education+0.542the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.535size taken out as well as density
Also comparing only within each state+0.262each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal+0.294compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more)
Giving bigger counties more weight+0.518the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 284 equally weighted counties
Middle value inside single states+0.230the link worked out inside each of 39 states with at least 20 counties reporting
Using ranks, after adjusting+0.568uses 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 = 5.4e-235. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.9e-7. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 9.6e-234.

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 +0.79 (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 returns claiming the earned income credit rose most, did days of 90°F or hottermove 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic3,108-0.27-0.060.399
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,108+0.06-0.030.315

Over the main period, any link between the changes could easily be chance. How change is measured.

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 Returns claiming the earned income credit and Days of 90°F or hotter