The U.S. Electoral College and Spatial Biases in Voter Power

Warf, Barney

doi:10.1080/00045600802516017

Cited by 11 publications

(6 citation statements)

References 53 publications

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“…Not only did Trump lose the national popular vote, but he also underperformed relative to Mitt Romney nationwide, receiving 45.9 percent of votes in 2016 compared to Mitt Romney’s 47.1 percent in 2012. But, because U.S. presidential elections are based on the Electoral College, some states are more important than others to the outcome (McKee and Teigen 2009; Warf 2009). Therefore, small advantages in key places enabled Trump to accumulate the set of electors needed to claim victory in 2016.…”

Section: Election Numbers: the Predicable And The Unexpectedmentioning

confidence: 99%

More than a rural revolt: Landscapes of despair and the 2016 Presidential election

Monnat

Brown

2017

Journal of Rural Studies

177

122

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Section: Election Numbers: the Predicable And The Unexpectedmentioning

confidence: 99%

More than a rural revolt: Landscapes of despair and the 2016 Presidential election

Monnat

Brown

2017

Journal of Rural Studies

177

122

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“…Figures 5-7 show the relationships between the percentage of seats won and the percentage of the national vote for political parties in the USA, UK, and Canada in the post-World War Two era. The basic relationship between seats and votes follows the so-called cubic law (Kendall & Stuart, 1950), but this relationship depends on both the boundaries of constituencies and the geographic pattern of the vote in each election (Johnston, 2002a(Johnston, , 2002bWarf, 2008;Johnston & Pattie, 2011). In general terms, plurality systems penalize parties whose support is spread relatively evenly, unless it is high enough to constitute a plurality in many constituencies.…”

Section: Converting Votes To Seatsmentioning

confidence: 99%

Electoral geography: From mapping votes to representing power

Forest

2017

Geography Compass

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In some ways, electoral geography has never been more popular. From the detailed, online maps of the Brexit vote to discussions of the electoral college versus the popular vote in the 2016 Trump–Clinton U.S. presidential contest, the relationships among geography, voting, and political power have seldom been more visible. The popularity of electoral geography in social and news media, however, does not necessarily reflect its presence in scholarly discussions, and indeed, in some ways, the former has replaced the latter. Digital technology and the burgeoning availability of electronic data mean that it is easier than ever to create maps of votes, often in near real time. Yet the academic field of electoral geography encompasses more than just mapping votes, including the study of election campaigns, political parties, electoral systems, and gerrymandering. The 3 major approaches are the geography of voting (mapping and visualizing votes), geographic influences on voting (the effect of place on political preferences and behavior), and the geography of representation (the analysis of electoral systems). Indeed, the structure of the electoral system, including gerrymandering, is often the key to understanding how political and racial/ethnic minorities can (or cannot) wield power and influence. This article examines each approach after a brief review of the historical origins of the subfield.

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“…This stirred a debate in the media whether the Electoral College system currently has a built-in bias against Democrats. It is impossible to do full justice to this literature; let us mention as an example [6,10,11,15,18,19,21,22].…”

Section: Introductionmentioning

confidence: 99%

Partisan Lean of States: Electoral College and Popular Vote

Sarantsev

2019

Preprint

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We compare federal election results for each state versus the USA in 1992USA in , 1994USA in , until 2018, to model partisan lean of each state and its dependence on the nationwide popular vote. For each state, we model both its current partisan lean and its rate of change, as well as sensitivity of state results with respect to the nationwide popular vote, using Bayesian linear regression. We apply this to simulate the Electoral College outcome in 2020, given even (equal) nationwide popular vote, as well as 2016, 2008, and 2004 nationwide popular vote. Taking equal popular vote for two major parties, we find whether the Electoral College is biased towards one of these parties.

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The U.S. Electoral College and Spatial Biases in Voter Power

Cited by 11 publications

References 53 publications

More than a rural revolt: Landscapes of despair and the 2016 Presidential election

More than a rural revolt: Landscapes of despair and the 2016 Presidential election

Electoral geography: From mapping votes to representing power

Partisan Lean of States: Electoral College and Popular Vote

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