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NEW from me & @christinezhang:
Much was made of US exit polls showing non-white voters swinging towards Trump, but is it that simple?
We spent 10 days poring over data from thousands of precincts in battleground states to get a more robust answer
Story: https://www.ft.com/content/31a0273a-d745-4ed5-b497-c7c61c26e32d
Much was made of US exit polls showing non-white voters swinging towards Trump, but is it that simple?
We spent 10 days poring over data from thousands of precincts in battleground states to get a more robust answer
Story: https://www.ft.com/content/31a0273a-d745-4ed5-b497-c7c61c26e32d
1) At first glance, the precinct-level data do support the exit poll’s finding of a non-white shift towards Trump:
Majority-black, -Latino and -Asian neighbourhoods in Atlanta, Philadelphia, Arizona and California all returned higher vote shares for Trump this year vs 2016.
Majority-black, -Latino and -Asian neighbourhoods in Atlanta, Philadelphia, Arizona and California all returned higher vote shares for Trump this year vs 2016.
2) But there’s a problem with proportional shift analysis:
Asking e.g "did the % of Latino voters backing Trump increase?" ignores turnout, and in doing so it ignores what elections are actually decided by: numbers of votes.
Asking e.g "did the % of Latino voters backing Trump increase?" ignores turnout, and in doing so it ignores what elections are actually decided by: numbers of votes.
3) e.g:
If black voters went 94% D vs 5% R in 2016, then 93D vs 6R in 2020, that’s a 2pt shift to Trump
But if turnout rose by 3%, the margin in *number of votes* actually goes more blue, because the
in votes cast *among a very D demographic* offsets switching from D to R
If black voters went 94% D vs 5% R in 2016, then 93D vs 6R in 2020, that’s a 2pt shift to Trump
But if turnout rose by 3%, the margin in *number of votes* actually goes more blue, because the
in votes cast *among a very D demographic* offsets switching from D to R
4) That’s exactly what happened in Atlanta, except turnout actually rose by 7% in majority-black areas, so altho people focused on a small pro-Trump % shift, these neighbourhoods actually delivered a net 15,000 vote swing to Biden (who currently leads Trump in GA by 14,172 votes)
5) Here’s the same thing in map form:
The majority-black southern precincts of Atlanta swung slightly towards Trump, but they remained staunchly blue, and that combo of a strong pro-Dem lean with a rise in turnout meant lots of net gains in votes for Dems.
The majority-black southern precincts of Atlanta swung slightly towards Trump, but they remained staunchly blue, and that combo of a strong pro-Dem lean with a rise in turnout meant lots of net gains in votes for Dems.
6) We see similar patterns in another key state; Arizona
Here, majority-Latino precincts in Phoenix shifted towards Trump by ~2.5 % pts
But turnout in these areas — which still broke 72%D to 27%R — surged by 32%, so they still added thousands more new votes for Biden than Trump
Here, majority-Latino precincts in Phoenix shifted towards Trump by ~2.5 % pts
But turnout in these areas — which still broke 72%D to 27%R — surged by 32%, so they still added thousands more new votes for Biden than Trump
7) But if the proportional swing is large, or the pre-existing pro-Dem lean small, these shifts can translate into big vote swings to Trump.
That’s what happened in Orange County CA, where majority-Asian precincts swung to Trump by >30pts and delivered him a net 10,000 vote gain
That’s what happened in Orange County CA, where majority-Asian precincts swung to Trump by >30pts and delivered him a net 10,000 vote gain
8) However, one other thing is true of all of those maps:
Although majority-minority precincts in city centres often did shift the vote margin in Biden’s favour, the Dems made much bigger gains in majority-white suburbs both in terms of proportional swing and absolute vote swing
Although majority-minority precincts in city centres often did shift the vote margin in Biden’s favour, the Dems made much bigger gains in majority-white suburbs both in terms of proportional swing and absolute vote swing
9) If you look at shifts in voting patterns across the US as a whole, the Dems increased their margin more in dense, large metros than in the suburbs.
But zoom in on the key battleground states that took Biden to victory and it was a suburban blue wave that made the difference.
But zoom in on the key battleground states that took Biden to victory and it was a suburban blue wave that made the difference.
10) In recent decades a huge rural-urban gap has opened up in US politics, leaving the suburbs as the key battleground. This is especially true in swing states.
This will pose challenges for both parties
This will pose challenges for both parties
11) The challenge for the Dems is how to keep those same suburban swing-voters on side in 2024.
Of all Biden voters, white voters were most likely to say they picked him as an anti-Trump vote. Many of these are lifelong Republicans who have said their Dem 2020 vote is a one-off
Of all Biden voters, white voters were most likely to say they picked him as an anti-Trump vote. Many of these are lifelong Republicans who have said their Dem 2020 vote is a one-off
12) Without the anti-Trump motivation in 2024, will they revert to their Republican habits, or will they stay blue?
13) And for black, Latino & Asian voters to have shifted proportionally towards Trump in a high-turnout election suggests new non-white voters are less pro-Dem than those that have been voting for years. How do Ds combat R messaging among these groups as they join the electorate?
13) Meanwhile the Republicans are gaining ground with non-white voters (especially those without college education), but also need Trump’s white non-college base, many of whom are Trump voters more than Republican voters.
14) Some concluding points:
Demography is not destiny. If any Dems were operating on the basis that a diversifying county will naturally shift the needle in their direction, these results cast that into severe doubt
Demography is not destiny. If any Dems were operating on the basis that a diversifying county will naturally shift the needle in their direction, these results cast that into severe doubt
15) Terms like "black", "Latino", "Asian" etc mask huge political diversity within each of those labels.
Or as @lorellapraeli told @christinezhang, "You need to understand that [Latinos] are different in New Mexico, and we are different in Nevada, and different in Florida."
Or as @lorellapraeli told @christinezhang, "You need to understand that [Latinos] are different in New Mexico, and we are different in Nevada, and different in Florida."
16/16 Percentage point swings are interesting for understanding the shifting sands of the electorate, but it’s critical to factor in turnout before concluding that any one group or other did or did not propel a candidate to victory.
17/16 Please also read @ChristineZhang’s thread, which gets into our methods & caveats https://twitter.com/christinezhang/status/1328349303375589377
And precinct data is a jungle, so we’re indebted to US political geography heroes including @derekwillis @sixtysixwards @jdjmke @jtannen215 @Garrett_Archer @joemfox
And precinct data is a jungle, so we’re indebted to US political geography heroes including @derekwillis @sixtysixwards @jdjmke @jtannen215 @Garrett_Archer @joemfox
Last but most importantly of all, a huge thanks to @AdrienneKlasa who resisted the temptation to murder me when we filed a 1500 word first draft, and then managed to extract a coherent story from our brain-dump.
Editors, they are good.
Editors, they are good.
