Data and Code for: Inversions in US Presidential Elections: 1836-2016
Principal Investigator(s): View help for Principal Investigator(s) Michael Geruso, University of Texas-Austin; Dean Spears, University of Texas-Austin; Ishaana Talesara, University of Texas-Austin
Version: View help for Version V1
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working_data | 12/28/2020 09:43:AM | ||
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Project Citation:
Geruso, Michael, Spears, Dean, and Talesara, Ishaana. Data and Code for: Inversions in US Presidential Elections: 1836-2016. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-12-17. https://doi.org/10.3886/E123381V1
Project Description
Summary:
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Inversions---in which the popular vote winner loses the election---have occurred in four US presidential races. We show that rather than being statistical flukes, inversions have been ex ante likely since the early 1800s. In elections yielding a popular vote margin within one point (one-eighth of presidential elections), about 40% will be inversions in expectation. We show this conditional probability is remarkably stable across historical periods---despite differences in which groups voted, which states existed, and which parties participated. Our findings imply that the US has experienced so few inversions merely because there have been so few elections (and fewer close elections).
Scope of Project
Subject Terms:
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Electoral College;
U.S. Presidential elections;
economic demography
JEL Classification:
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D72 Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
J10 Demographic Economics: General
D72 Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
J10 Demographic Economics: General
Geographic Coverage:
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United States
Time Period(s):
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1836 – 2016 (U.S. Presidential elections 1836-2016)
Collection Date(s):
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11/2016 – 10/2020
Universe:
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U.S. Presidential elections (historical and simulated)
Data Type(s):
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administrative records data;
census/enumeration data;
program source code;
survey data
Methodology
Unit(s) of Observation:
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state-year (eg. Texas in 2012)
Geographic Unit:
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U.S. states
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