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With 16 Months to go, Negative Partisanship Predicts the 2020 Presidential Election

Authors: Rachel Bitecofer;

With 16 Months to go, Negative Partisanship Predicts the 2020 Presidential Election

Abstract

American elections have become increasingly nationalized and highly predictable; with partisanship serving as an identity-based, dominant vote determinant for all but a small portion of Americans. Rather than a relatively fixed pool of voters rewarding or punishing the parties for their platforms or performance in office like elections of the past, the two party’s electoral fortunes rise and fall with the ebb and flow of turnout among key elements of their increasingly fixed coalitions; mobilized or demobilized by gaining or losing control of the presidency. Voters from the party out-of-power, galvanized to vote by negative partisanship, increase their turnout in subsequent elections, while turnout from the party in-power wanes due to complacency. The electoral performance of the opposition party is also improved by the predictable movement of pure Independent voters away from the party in power due the hyper-partisan and highly-charged negative political environment. The size of the “swing” of pure Independents is conditioned on economic conditions or on other “shocks” to the political system such as unpopular wars or unpopular incumbent presidents. The Negative Partisanship model predicted the Democrat's 40 seat House gain four months before Election Day in the 2018 Midterms. The 2020 forecast currently shows Democrats recapturing the White House with 278 Electoral College votes, including the key states of Michigan, Wisconsin, and Pennsylvania.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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