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Laura Bronner

@laurabronner

16 Dec 20

@geoffreyvs

The suburbs shifted to the Democrats, and they also got more diverse. Is the story that simple? @geoffreyvs, @elena___mejia, @ameliatd and I find that education makes the story more complicated.

53eig.ht/3qPfxUW

The biggest shift towards Biden was in suburban counties that became *both* more diverse *and* more educated over the past decade -- a lot of the GA suburbs fall into this category.

But for counties where increased diversity and education *didn't* go together, the Democratic swing was bigger in places that became more educated but not more diverse, than in places that became more diverse but not more educated.

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Laura Bronner

@laurabronner

12 Nov 20

@wiederkehra

Where we saw red and blue mirages -- or, as I wanted to headline it, "Election Night was a marshmallow test for the country, and we failed".

@wiederkehra, @baseballot and I look at vote counts 1.5, 12 and 72 hours after polls closed in each state:

53eig.ht/3lkZmvC

In some states, generally those where absentee votes were processed before Election Day, the race looked competitive after 90 minutes, only for Election Day ballots to conclusively put Trump in the lead. (Missouri and Montana are also in this category.)

In other states, generally those where absentee ballots were counted after the election, what appeared like an early lead for Trump disappeared as more ballots were counted, like in Michigan, which was unable to process ballots before election week.

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Laura Bronner

@laurabronner

31 Oct 20

Four reasons Biden has a better shot than Clinton did in 2016 -- and 2 reasons there's still uncertainty.
A summary 🧵:

1. Biden's lead is bigger and more stable than Clinton's was.

Clinton's lead was smaller throughout, and more unstable. Biden's has never been < 6.6 points.

2. There are fewer undecideds than 2016.

A week before the 2016 election, around 14% of respondents said they were undecided or intended to vote third party -- and the vast majority of late deciders voted for Trump: 53eig.ht/2fIYJK2

This year, there are much fewer.

3. State polls have improved.

In 2016, it was state polls that had polling errors, in part because they hadn't needed to weight by education before (ft.com/content/b32976…). But polls have improved (53eig.ht/34wDia0), and there are more state polls now.

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Laura Bronner

@laurabronner

31 Oct 20

@derStandardat

Vier Gründe, wieso es 2020 um Biden besser steht als 2016 um Clinton -- und zwei Gründe, wieso es trotzdem noch viel Unsicherheit gibt, heute im @derStandardat:

derstandard.at/story/20001213…

1. Bidens Vorsprung ist größer und stabiler als jener Clintons.

Clintons Vorsprung war durchgehend kleiner und schrumpfte zeitweise auf einen Prozentpunkt; Bidens lag nie unter 6.6 Punkten.

2. Es gibt weniger unentschlossene Wähler als 2016.

Eine Woche vor der Wahl 2016 gab es rund 14% Unentschlossene/Kleinparteienwähler -- und Trump konnte bei genau bei Wählern, die sich spät entschieden, punkten: fivethirtyeight.com/features/why-f…

Diesmal gibt es nur 5% Unentschlossene.

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Laura Bronner

@laurabronner

30 Oct 20

@ameliatd

Why is turnout among young Americans so low? @ameliatd, @jazzmyth and I find that while people under 35 *are* more skeptical of the system, they're not apathetic. Instead, they're more likely to face structural barriers like not being able to get off work

53eig.ht/31Tsh22

(+ bonus charts!) How do we know young people aren't more apathetic? Well, they're not significantly more likely to say they don't vote because the system is too broken, or because they don't believe in voting. But they *are* more likely to say they wanted to vote, but couldn't.

In fact, if anything, young people are *less* likely to say that they don't vote because nothing will change for people like them no matter who wins!

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Laura Bronner

@laurabronner

25 Jun 20

@jazzmyth

We have evidence of racial discrepancies in police use of force. But some studies miss the extent of the problem by using data that's already biased.

fivethirtyeight.com/features/why-s…

@jazzmyth's awesome chart helps me explain this paper by @dean_c_knox, @conjugateprior and @jonmummolo:

In short, as the paper explains, looking at use of force just among the set of people police have *stopped* isn't enough to let you correctly estimate racial discrepancies. If there's bias in who gets stopped in the first place, that confounds your estimate.

A simple example: Even assuming use of force among stopped people is equal across race (which is unrealistic), bias in stops means that your denominator is wrong. More Black people have been stopped without cause, so "equal" treatment is actually evidence of discrimination:

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