The White Working Class and the 2016 Election

Carnes, Nicholas; Lupu, Noam

doi:10.1017/s1537592720001267

Cited by 50 publications

(28 citation statements)

References 43 publications

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“…Carnes and Lupu (2020) find no evidence of outsized support for Trump in the 2016 election among self-described white working class voters, but their paper does not examine the potentially moderating force of localized economic distress due to manufacturing layoffs.2 Wright (2012) questions whether unemployment decreases incumbent vote share of both parties. He finds that unemployment is a partisan issue for voters: higher levels of unemployment increase the vote shares of Democratic (but not Republican) gubernatorial and presidential candidates.…”

mentioning

confidence: 99%

Gone For Good: Deindustrialization, White Voter Backlash, and US Presidential Voting

Baccini

Weymouth

2021

Am Polit Sci Rev

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Globalization and automation have contributed to deindustrialization and the loss of millions of manufacturing jobs, yielding important electoral implications across advanced democracies. Coupling insights from economic voting and social identity theory, we consider how different groups in society may construe manufacturing job losses in contrasting ways. We argue that deindustrialization threatens dominant group status, leading some white voters in affected localities to favor candidates they believe will address economic distress and defend racial hierarchy. Examining three US presidential elections, we find white voters were more likely to vote for Republican challengers where manufacturing layoffs were high, whereas Black voters in hard-hit localities were more likely to vote for Democrats. In survey data, white respondents, in contrast to people of color, associated local manufacturing job losses with obstacles to individual upward mobility and with broader American economic decline. Group-based identities help explain divergent political reactions to common economic shocks.

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confidence: 99%

Gone For Good: Deindustrialization, White Voter Backlash, and US Presidential Voting

Baccini

Weymouth

2021

Am Polit Sci Rev

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“…Although the Democratic Party has had a stable advantage among Black voters for several decades now, there are reasons to expect that the signal from a voter's race is larger than in the past. First, Trump's victory in 2016 was a continuation of "the decadeslong expansion of Republican support among white working-class Americans" (Carnes and Lupu, 2020). In addition, the GOP is believed to have activated white identity (Tesler, 2016;Sides et al, 2017Sides et al, , 2019 in response to Barack Obama's electoral wins.…”

Section: Group-based Votingmentioning

confidence: 99%

“…6 In the 2016 presidential election, the income effect is believed to have interacted with education. Carnes and Lupu (2020), for example, show that the diploma divide in 2016 "was driven largely by more affluent Americans." 7…”

Section: Group-based Votingmentioning

confidence: 99%

The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections

Zilinsky

Kim

2021

Preprint

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Partisan sorting by social groups is believed to increase affective polarization and decrease group-level leverage in representation. Mounting evidence suggests that social groups are increasingly polarized in voting behavior, but how reliable are demographic labels as predictors of vote choice? We test for demographic sorting, using public opinion surveys between 1952-2020 and applying tree-based machine learning models to calculate out-of-sample predictions of presidential voting decisions. We calculate predictions based on voters' demographics and then gradually incorporate more information to test whether the electorate is becoming more predictable. Demographics alone typically can predict 63.5% of vote choices correctly. But contrary to the sorting hypothesis's implications, demographics have not grown more predictive over time, while partisanship has. Additional information about voters, such as issue positions or candidate perceptions, continue to be necessary for obtaining out-of-sample error rates of 5% or less. However, their added value decreases as partisanship's predictive power grows.

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Section: Group-based Votingmentioning

confidence: 99%

“…5 In the 2016 presidential election, the income effect is believed to have interacted with education. Carnes and Lupu (2020), for example, show that the diploma divide in 2016 "was driven largely by more affluent Americans." 6 Age.…”

Section: Group-based Votingmentioning

confidence: 99%

The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections

Kim

Zilinsky

2021

Preprint

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Political parties are increasingly homogeneous both ideologically and demographically. With increased party-line voting, a natural corollary of sorting is that membership in demographic groups should be increasingly prognostic of vote choice. We argue that predictability of voting decisions is a useful quantity of interest for testing hypotheses from the literature on partisan and demographic sorting. Contrary to expectations, we find that demographic sorting has not produced a very predictable electorate. Tree-based machine learning models, trained on demographic labels from public opinion surveys between 1952 and 2020, predict only 63.5% of out-of-sample vote choices correctly on average. Moreover, demographics have not grown more predictive over time, while partisanship has. Partisanship's diagnosticity has risen in absolute terms, and its relative dominance over ideology has been stable for the last seven decades. Additional data about voters can still yield superior predictions, but its added value decreases over time as partisanship's predictive power grows.

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The White Working Class and the 2016 Election

Cited by 50 publications

References 43 publications

Gone For Good: Deindustrialization, White Voter Backlash, and US Presidential Voting

Gone For Good: Deindustrialization, White Voter Backlash, and US Presidential Voting

The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections

The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections

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