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There was an election yesterday pitting a conservative versus a liberal. The race was for a state Supreme Court seat in an important swing state, Wisconsin.
The conservative is currently ahead, which has got the liberal press in a knot. One news site announced that “Dems Appear To Blow A Crucial Race For Wisconsin Supreme Court.”
Before this becomes another (erroneous) example of how rural America is controlling elections, let’s look at the results.
The nonpartisan race was between Appeals Court Judge Brian Hagedorn, the conservative, and Appeals Court Judge Lisa Neubauer, the liberal. The Milwaukee Sentinel reported that Wisconsin groups that normally support conservative candidates “counted him (Hagedorn) out and wouldn’t spend on his behalf.” Democrats figured they had this won and that they could gain more ground after the 202 election.
The day after the election, however, Hagedorn declared victory.
We know little about Wisconsin politics, but we can count. And so today we looked at whether geography played a role in Hagedorn’s lead.
It did. The liberal, Neubauer, won decisively only in the counties that make up the central cities of the state’s largest metro areas. She lost in the suburbs, with the exception of eking out a narrow win in midsized suburbs. She lost in the exurbs of major metropolitan areas. She lost in small cities. And, yes, she lost in rural counties. But her losses there pale in comparison to the trouncing she took in the suburbs of major metropolitan areas (metros with 1 million or more residents).
You can see the results as of Wednesday morning in the chart above. (Definitions of the geographic categories are at the bottom of this article.) The biggest conservative margins were run up in the four suburban counties of Milwaukee, where Hagedorn built a 69,000-vote lead. In the state’s 46 rural counties, Hagedorn’s lead was 41,000.
The conservative won two out of every three votes in the suburbs of the state’s largest city. Hagedorn won just 56 percent of the rural vote.
As with other election, this wasn’t a contest between rural and urban. In this “crucial race” — as with most election in the country today — liberal (Democratic) votes are clustered in the center of the largest metropolitan areas. The rest of the land votes conservative, and the most conservative voters in this case lived closer to the city than to any dairy farm.
Geographic Category Definitions
Center of Major Metros: Central counties of metropolitan statistical areas of 1 million residents or more.
Suburbs of Major Metros: Suburbs of metros of 1 million residents or more.
Exurbs of Major Metros: Outlying counties of metros of 1 million residents or more where most of the population lives in rural areas (unurbanized) as defined by the Census.
Center of Midsized Metros: Central counties of metropolitan areas of 250,000 to 1 million.
Suburbs of Midsized Metros: Suburban counties of metropolitan areas of 250,000 to 1 million.
Metros under 250,000: All counties in metropolitan areas under 250,000 residents.
Nonmetro, Adjacent: Nonmetropolitan counties adjacent to a metro.
Nonmetro, Nonadjacent: Nonmetropolitan counties not adjacent to a metro.