A Recap of the 2016 Election Forecasts

Campbell, James E.; Norpoth, Helmut; Abramowitz, Alan I.; Lewis‐Beck, Michael S.; Tien, Charles; Erikson, Robert S.; Wlezien, Christopher; Lockerbie, Brad; Holbrook, Thomas M.; Jérôme, Bruno; Jérôme-Speziari, Veronique; Graefe, Andreas; Armstrong, J. Scott; Jones, Randall J.; Cuzán, Alfred G.

doi:10.1017/s1049096516002766

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Almost none of the analytical models developed during the campaign was effectively able to predict Donald Trump’s victory in the Electoral College. Although in general polls were quite accurate in predicting the vote share for the two candidates at the national level, estimations in several states were understandably less precise and failed to capture Trump’s narrow margins of victory in some key battlegrounds (Campbell et al, 2017; Terhanian, 2017). More specifically, Trump was effectively able to outpace the polls in several states that in the last 8 years (or more) voted consistently for the Democratic candidate.…”

Section: The “Surprising” Statesmentioning

confidence: 95%

The integrity of the 2016 US Presidential Election: Exploring the possible impact of ideology on experts’ judgments

Curini

2019

Party Politics

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Using new comparative expert data about the 2016 US Presidential Election, this article explores the effects of experts’ ideological preferences on their evaluations of electoral integrity. Without contesting the claim that the 2016 election faced challenges of integrity, our analyses reveal a substantial association between negative evaluation of election integrity and experts’ ideological orientation. Our results also suggest that this ideological effect is stronger in States that ‘swung’ from Democratic to Republican in the 2016 election.

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Section: The “Surprising” Statesmentioning

confidence: 95%

The integrity of the 2016 US Presidential Election: Exploring the possible impact of ideology on experts’ judgments

Curini

2019

Party Politics

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“…Trump's election ran against the grain of many polling-based forecasts that could not capture state-level effects. Major media polling aggregators used polling data reported with substantial confidence intervals to create a strong expectation that a Clinton win was inevitable, despite long-range forecasting models from prominent (and largely ignored) academic sources predicting a moderate probability of a fairly narrow Clinton popular vote victory (Lewis-Beck and Tien 2016; Campbell et al 2017).…”

Section: How Did It Come To This?mentioning

confidence: 99%

Donald trump’s challenge to the study of elections, public opinion and parties

Donovan

Bowler

2018

Journal of Elections, Public Opinion and Parties

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The 2016 US presidential contest, and Donald Trump's election, present challenges to political science. This special issue explores some of what political science got "right" and "wrong" in terms of anticipating, or failing to anticipate, Trump's success. It explores some of what the discipline may learn from the 2016 election by asking what Trump's candidacy illuminates about the success and limitations of literature on elections, public opinion, and parties. These papers touch on the uniqueness of Trump's candidacy, but it is important to start by placing the 2016 election in context. As Endres, Panagopoulos, and Weinschenk (2018) document, polling throughout the election suggested Hillary Clinton would win, and, indeed, Clinton won the popular vote with a 2% margin, earning nearly 3 million more votes than Trump. The Electoral College being what it is, if less than 40,000 votes flipped differently in three states 1 that Trump narrowly won, Clinton could have been President. Under that scenario, one that most observers expected, this issue would certainly be different: perhaps examining the continuity of Barack Obama legacy, Obama's enduring multi-racial coalition, the embrace of globalism, and, most certainly, the role of gender in the election of the first woman as US president. Trump would have played a role in that counterfactual issue, but not the starring role. The odd context of the 2016 US presidential election It is important to consider the election in the context of widespread dissatisfaction with the major candidates. Exit polls had 54% rating Clinton unfavorably, and 60% rating Trump unfavorably. A quarter of voters were motivated by dislike of the candidates; most of those voted Trump. Nearly a third of Trump voters found him not honest or trustworthy, and 23% of Trump voters found him unqualified. 2 This suggests widespread dissatisfaction, as

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“…A comparison with the preceding 2016 election, which among other things was also characterized by an intensely polarized environment, might raise the question whether the 2020 election could be equally difficult to forecast. In 2016, most election forecasts favored the Democratic candidate Hillary Clinton, but Donald Trump eventually gained a majority in the Electoral College despite losing the Popular Vote (Campbell et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Fundamentals-Based State-Level Forecasts of the 2020 US Presidential Election

Nollenberger¹,

Unger²

2020

Preprint

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Forecasts of US presidential elections have gained considerable attention in recent years. However, as became evident in 2016 with the victory of Donald Trump, most of them consider presidential elections only at the national level, neglecting that these are ultimately decided by the Electoral College. In order to improve accuracy, we believe that forecasts should instead address outcomes at the state-level to determine the eventual Electoral College winner. We develop a political economy model of the incumbent vote share across states based on different short- and long-term predictors, referring up to the end of the second quarter of election years. Testing it against election outcomes since 1980, our model correctly predicts the eventual election winner in 9 out of 10 cases – including 2016 –, with the 2000 election being the exception. For the 2020 election, it expects Trump to lose the Electoral College, as only 6.2 percent of simulated outcomes cross the required threshold of 270 Electoral Votes, with a mean prediction of 106 Electoral Votes.

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A Recap of the 2016 Election Forecasts

Cited by 14 publications

References 6 publications

The integrity of the 2016 US Presidential Election: Exploring the possible impact of ideology on experts’ judgments

The integrity of the 2016 US Presidential Election: Exploring the possible impact of ideology on experts’ judgments

Donald trump’s challenge to the study of elections, public opinion and parties

Fundamentals-Based State-Level Forecasts of the 2020 US Presidential Election

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