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Explaining the Blue Shift in Election Canvassing

Authors: Edward B. Foley; Charles Stewart III;

Explaining the Blue Shift in Election Canvassing

Abstract

We conduct statistical analysis of the rise in the number of votes counted after Election Day (“overtime votes”) and the growing tendency of these votes to disproportionately favor Democrats in presidential elections (the “blue shift”). We provide a historical description of these two time series, from 1948 to 2016, and establish that the timing of the persistent growth in the blue-shifted overtime vote began with the 2004 election. Variation in the size of the overtime vote is associated with the number of mail and provisional ballots and with the partisanship of the state, while the blue shift is associated with state partisanship but not with the number of mail and provisional ballots. Our analysis has relevance to post-Election-Day dynamics, especially in elections where a close election-night results that favor one candidate may yield to close final-canvass results that favor the other candidate. We conclude by discussing the conditions under which overtime votes counted in the 2020 presidential election could prove especially problematic.

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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!
6
Top 10%
Average
Average
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