How Red Or Blue Is Your State? Your Congressional District?

Last fall, Joe Biden became the first Democratic presidential candidate to win Arizona since 1996 and the first to promote Georgia since 1992. But does this mean Arizona and Georgia are now blue states?

Well, not yet – at least by our definition.

Allow us to introduce (or reintroduce) the Party Political Lean Metric from FiveThirtyEight – our method of measuring state or district bias, similar to that of the Cook Political Report Partisan Voter Index or Inside Elections Baseline. We define “Partisan Lean” as the average margin difference between the vote of a state or district and the overall vote of the country. For example, if a state has a partisan propensity of thirty-five and eight of R + 5, it means that it is 5 percentage points more republican than the entire nation. In other words, in an election that is strictly nationally bound, we would expect the Republicans to win that state with 5 points.

By that definition, Arizona and Georgia are still (slightly) red states – R + 7.6 and R + 7.4, respectively. While they may have voted for Biden in 2020, they did so by a margin smaller than his national vote gain of 4.5 percentage points. (Those partisan values ​​also take into account the results of other elections where Republicans in Arizona and Georgia did better. More on that in a minute.) But here are the new partisans from FiveThirtyEight, who are leaning for each state for the 2021-22 election cycle , updated with the results of the 2020 elections.

The partisans of FiveThirtyEight reject every state

Average margin difference between the election of each state and the total election of the country in congressional and gubernatorial elections according to a mixture of results from the presidential and state elections

Status Partisan Lean Status Partisan Lean
District of Columbia D + 68.2 Arizona R + 7.6
Massachusetts D + 32.6 Florida R + 7.6
Hawaii D + 31.6 Iowa R + 9.7
Vermont D + 27.5 Texas R + 12.0
Maryland D + 25.9 Ohio R + 12.4
California D + 25.5 Alaska R + 14.6
Rhode Island D + 24.0 South carolina R + 18.6
new York D + 20.0 Indiana R + 20.0
Delaware D + 13.7 Montana R + 20.0
Illinois D + 13.4 Mississippi R + 20.3
Washington D + 12.4 Louisiana R + 20.5
Connecticut D + 12.1 Kansas R + 20.7
New Jersey D + 12.0 Missouri R + 21.2
Oregon D + 10.6 Nebraska R + 24.8
New Mexico D + 7.0 Utah R + 26.3
Colorado D + 6.4 Kentucky R + 27.1
Virginia D + 4.6 Tennessee R + 29.4
Maine D + 4.0 Alabama R + 29.6
Minnesota D + 1.9 Arkansas R + 31.8
New Hampshire D + 0.3 South Dakota R + 32.2
Michigan R + 1.6 West Virginia R + 35.5
Nevada R + 2.5 Idaho R + 37.0
Pennsylvania R + 2.9 Oklahoma R + 37.2
Wisconsin R + 4.1 North Dakota R + 37.2
North Carolina R + 4.8 Wyoming R + 49.7
Georgia R + 7.4

Sources: Landtag election websites, Daily Kos Elections

We also calculated the partisan strength for each convention district you can find our GitHub page. (One brief caveat: the ten-year restructuring process means that almost every district will be redrawn before the mid-term elections in 2022. Therefore, the current partisan leans at the district level are mostly only useful for special elections. Don’t worry: we will count. The partisans are leaning on the new ones Congressional districts once they’re done.)

Now that you know what our new Partisan Lean Scores are, let’s talk about how – and how not – to use them. First of all we would like to emphasize again that the partisan tendencies of FiveThirtyEight are an expression of relative Partisanship; That said, they don’t necessarily tell us how red or blue a place is in absolute terms. And since the Democrats won the national referendum in seven of the last eight presidential elections, it’s a good argument that the U.S. as a whole is actually a bit to the left of center and that a state with a partisan of thirty-five, for example, can R + 1 actually vote more democratically than republican.

The reason we prefer a relative metric to an absolute one is because we can better understand how a state or district might vote in a given national setting. For example, if general congressional polls suggest Republicans win the House referendum by 6 points, we may conclude that a Democratic congressman sitting in a district with a D + 4 partisan inclination is at risk. (Assuming a unified national turnaround, Republicans would carry this district by 2 points.) Partisans’ bias towards national election does not mean that a national tie is our standard expectation. it only simplifies the mathematics of the superposition of different national moods (D + 6, R + 3, etc.).

The other thing you should know about our Partisan Lean Score is that it doesn’t just reflect the results of a single presidential election (i.e. 2020). Rather, this version of Partisan Lean (to be used for congressional and gubernatorial elections) is calculated as 50 percent of the state or district’s propensity for the nation in the 2020 presidential election, 25 percent as the relative propensity for the 2016 presidential election, and 25 percent a custom state legislative stance based on the nationwide referendum in the last four State House elections.

Empirically, we have found that this mixture of a few different choices provides the most accurate expression of a place’s true partiality. While the most recent presidential election is the most important input, the previous one also has predictive power, especially for states (like Arizona and Georgia) that suddenly shifted to one party (they often see a reversal from the mean). And the legislative component of the state explains the fact that some states (like West Virginia) vote differently for voting posts than they do for the president.

As a reminder, partisan denials for all 50 states (plus Washington, D.C.) and all 435 congressional districts are open to the public on our GitHub page. We are excited to invite you to use it for all of your Congress and Governor Race needs in 2022. We’ll use it in our own analyzes, but we’d like to see it catch on with others too. If you do something cool with it, don’t forget it tag us or write us a message.

Mary Radcliffe, Aaron Bycoffe, Nate Silver, and Geoffrey Skelley contributed to the research.

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