placestats

Republican share of the two-party vote, 2008 and Firearm share of suicides, standardised

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 Republican share of the two-party vote, 2008 is higher, Firearm share of suicides, standardised tends to be higher. The link is weak: +0.32 across 2,209 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 35 states, the middle value of the link is +0.24.

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

Republican share of the two-party vote, 2008

The Republican candidate's share of the votes for the two main parties in the 2008 presidential election, McCain against Obama. Third-party votes are left out, just as in the other years.

MIT Election Data and Science Lab · MIT county presidential returns

Firearm share of suicides, standardised

The gun share of suicides compared with what you would expect if each sex, age and race group here used guns at its national rate. 100 means it matches what the local population mix would predict. Above 100 means more gun suicide than that. It is the best county measure of gun access available, and it closely tracks RAND's state estimates of gun ownership.

CDC WONDER mortality; RAND firearm ownership database · CDC WONDER underlying cause of death, suicide by intent, firearm by mechanism, 2018-2023

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 republican share of the two-party vote, 2008

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Highest
Franklin County, ID87.6113
Madison County, ID87.2100
Livingston Parish, LA86.6118
Gray County, TX85.7144
Jackson County, KY85.6138
Lowest
District of Columbia, DC6.646.9
Petersburg city, VA10.3106
Prince George's County, MD10.585.1
Bronx County, NY1126.6
Baltimore city, MD11.878.3

By firearm share of suicides, standardised

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Highest
Gates County, NC47.4181
Hale County, AL39.1178
Harrison County, MO66.1177
Ritchie County, WV73.6176
Teton County, MT59.2175
Lowest
New York County, NY13.612.9
Queens County, NY24.622.5
Kings County, NY20.124.6
Bronx County, NY1126.6
Norfolk County, MA40.529.7
States — Republican share of the two-party vote, 2008 is not published for states

By firearm share of suicides, standardised

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Highest
Wyoming—130
Mississippi—129
Alabama—129
Louisiana—121
Georgia—120
Lowest
Massachusetts—41.3
Hawaii—45
District of Columbia—46.9
Rhode Island—50.3
New Jersey—51.7
Metro areas

By republican share of the two-party vote, 2008

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Highest
Pampa, TX86.1144
Rexburg, ID85.5107
Vernal, UT85.2122
Borger, TX84.8131
Cullman, AL83.1104
Lowest
Rio Grande City-Roma, TX15.3167
Taos, NM17.2128
Las Vegas, NM19.6101
Santa Cruz-Watsonville, CA20.465.1
Eagle Pass, TX21.3100

By firearm share of suicides, standardised

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Highest
Cleveland, MS32.1173
Rio Grande City-Roma, TX15.3167
Winnemucca, NV65.3161
Snyder, TX80.2160
Brenham, TX71.6159
Lowest
New York-Newark-Jersey City, NY-NJ35.335.2
Barnstable Town, MA42.938.8
Boston-Cambridge-Newton, MA-NH37.439.2
Pittsfield, MA23.140.7
Urban Honolulu, HI29.240.9

Counties furthest from the pattern

Compared with the straight line that best fits all 2,209 counties: the five with far more firearm share of suicides, standardised than their republican share of the two-party vote, 2008 would suggest, and the five with far less.

Republican share of the two-party vote, 2008Firearm share of suicides, standardised
Above the line
Starr County, TX15.3167
Bolivar County, MS32.1173
Hale County, AL39.1178
Gates County, NC47.4181
Leflore County, MS31.5161
Below the line
Anderson County, TX7252.8
Norfolk County, MA40.529.7
Richmond County, NY5239.2
Bergen County, NJ45.236.2
New York County, NY13.612.9

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+0.4362,209 counties reporting both
After adjusting for density, income and education+0.317the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.307size taken out as well as density
Also comparing only within each state+0.036each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.452the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 273 equally weighted counties
Middle value inside single states+0.238the link worked out inside each of 35 states with at least 20 counties reporting
Using ranks, after adjusting+0.343uses 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 = 7.6e-53. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.7e-9. 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 4.3e-52.

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 +0.33 (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 Republican share of the two-party vote, 2008 and Firearm share of suicides, standardised