19 Things We Learned from the 2016 Election

Gelman, Andrew; Azari, Julia R.

doi:10.1080/2330443x.2017.1356775

Cited by 22 publications

(12 citation statements)

References 21 publications

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“…That is, there is a consistent pattern of underreporting by Trump's supporters, inducing on average about −0.005 data defect correlation, and hence D I ≈ 2.5 × 10 −5 . This quantitative finding provides numerical evidence to the general belief that there was serious underreporting by Trump's supporters [see e.g., Cohn (2017), Gelman and Azari (2017)]. The quantitative measure of the bias in terms of the data defect correlationρ N is of value for predicting future elections, such as the 2020 US presidential election.…”

Section: Estimatingmentioning

confidence: 62%

Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election

Meng¹

2018

Ann. Appl. Stat.

228

233

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Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. By developing measures for data quality, this article suggests a framework to address such a question: "Which one should I trust more: a 1% survey with 60% response rate or a self-reported administrative dataset covering 80% of the population?" A 5-element Eulerformula-like identity shows that for any dataset of size n, probabilistic or not, the difference between the sample average X n and the population average X N is the product of three terms: (1) a data quality measure, ρ R,X , the correlation between X j and the response/recording indicator R j ; (2) a data quantity measure, √ (N − n)/n, where N is the population size; and (3) a problem difficulty measure, σ X , the standard deviation of X. This decomposition provides multiple insights: (I) Probabilistic sampling ensures high data quality by controlling ρ R,X at the level of N −1/2 ; (II) When we lose this control, the impact of N is no longer canceled by ρ R,X , leading to a Law of Large Populations (LLP), that is, our estimation error, relative to the benchmarking rate 1/ √ n, increases with √ N; and (III) the "bigness" of such Big Data (for population inferences) should be measured by the relative size f = n/N , not the absolute size n; (IV) When combining data sources for population inferences, those relatively tiny but higher quality ones should be given far more weights than suggested by their sizes.Estimates obtained from the Cooperative Congressional Election Study (CCES) of the 2016 US presidential election suggest a ρ R,X ≈ −0.005 for self-reporting to vote for Donald Trump. Because of LLP, this seemingly minuscule data defect correlation implies that the simple sample proportion of the self-reported voting preference for Trump from 1% of the US eligible voters, that is, n ≈ 2,300,000, has the same mean squared error as the corresponding sample proportion from a genuine simple random sample of size n ≈ 400, a 99.98% reduction of sample size (and hence our confidence). The

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Section: Estimatingmentioning

confidence: 62%

Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election

Meng¹

2018

Ann. Appl. Stat.

228

233

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“…As for Trump supporters who stated otherwise when polled: anecdotes aside, it seems doubtful there are many people who would go to the trouble of responding to a poll and then answering insincerely. For one thing, a Trumpsupporting respondent who states a preference for Biden is actually hurting, not helping, his preferred candidate, in that Trump's perceived unpopularity is one reason why some of his fellow Republicans would be less strongly supportive of him, and state-level polling errors in 2016 were not consistent with the "shy Trump voter" hypothesis, 22 nor did analysis of 2020 polls support this theory. 23 Differential nonresponse and differential turnout seem like more plausible explanations of polling error.…”

Section: Explanations For Polling Errorsmentioning

confidence: 98%

Failure and Success in Political Polling and Election Forecasting

Gelman

2021

Statistics and Public Policy

Self Cite

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“…Inherent weaknesses of party organization, however, are paired with historic levels of voter polarization. 34 Active and engaged voters are far more worried about sins of the other side than behavior within their own camp. 35 These factors are fertile soil for the current experiment in Trumpism's "pluto-populism," a hybrid of plutocracy and popular mobilization.…”

Section: Ring Rulementioning

confidence: 99%

Bringing The “Ring” Back In: The Politics Of Booty Capitalism

Broxmeyer

2020

J. gilded age prog. era

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One striking parallel between the Gilded Age and today is the return of the “ring” as a volatile form of governance. Rings are small bands of entrepreneurs who, through capture of key political institutions, exploit the coalition-building imperatives of party politics to rapidly accumulate large personal fortunes. The vernacular hails from the 1860s and 1870s, when the Tweed Ring, Gold Ring, Whiskey Ring, and Bank Ring, among others, scandalized the public with smash and grab practices. In the Trump era, Paul Manafort's career and the president's family business share many similarities to these classic episodes, both in the contested origin of private wealth and the circulation of profits throughout the political system. Rings thus have a tendency to recur in American political development. When electoral politics becomes financialized, speculators may seize opportunities to extract a hefty surplus from the public domain. In premodern contexts, Max Weber called this plundering phenomenon “booty capitalism.” The variant specific to American electoral institutions highlights a periodic conflict in democratic capitalism that arises over the legitimacy of political wealth accumulation.

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19 Things We Learned from the 2016 Election

Cited by 22 publications

References 21 publications

Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election

Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election

Failure and Success in Political Polling and Election Forecasting

Bringing The “Ring” Back In: The Politics Of Booty Capitalism

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