News Release 

Who does the electoral college favor?

Simulations from Columbia University researchers show a slight bias toward Trump but less of a tilt than in the 2016 election.

Columbia University School of Engineering and Applied Science

Research News

Simulations from Columbia University researchers show a slight bias toward Trump but less of a tilt than in the 2016 election.

Trump's 2016 victory in the Electoral College without leading in the popular vote has led to wide speculation of a repeat in 2020. Columbia University researchers have been wondering the same thing.

Robert Erikson, professor of political science, and Karl Sigman, professor of industrial engineering and operations research at Columbia Engineering, have examined how Electoral College outcomes are conditioned by how states voted in previous elections. Their simulations suggest that in 2020 the Electoral College bias is likely to again favor the Republicans, but to a lesser degree than in 2016.

In a new study published today in PNAS, Erikson and Sigman, together with PhD candidate Linan Yao, show how to forecast the electoral vote if the 2020 popular vote is close, taking into account the configuration of state voting in 2016. They examined the degree of Electoral College bias in past elections and the degree to which it can be predicted in advance from prior state voting patterns, going back to 1980.

Based on thousands of simulations, their research suggests that the bias in 2020 will likely favor Trump again, but to a lesser degree than in 2016. And, the team notes, there is sufficient range in the possible outcomes so that the bias could even favor Biden.

The team found that in past presidential elections, potential Electoral College bias was slimmer than in 2016. In these elections, the difference among states in their presidential voting is a function of the states' most recent presidential voting, plus new inputs, such as changes in population, not predicted by the state's past vote. While the distribution from the 2016 election matters, the evidence from past elections suggests that states can depart considerably from their previous vote and that there is thus some uncertainty for 2020 as well.

"We note that 2016 was a statistical outlier," said Erikson, who pointed out that Trump won in 2016 by barely winning Wisconsin, Michigan, and Pennsylvania. If the 2020 election is similarly close nationally, those outcomes could be different. Several other states (Arizona, Florida, Georgia, North Carolina) could also be in play in 2020, just based on how close they were in 2016. Added Erikson, "The Democratic versus Republican divisions in the prior election have mattered, but only up to a point. That is why the same national popular vote as 2016 could have a different Electoral College outcome."

The researchers looked at the percentage of Democrats and Republicans in a state as a function of the vote in the prior election, and measured the percentages relative to the state mean. Each state's vote as a relative position on the Democrat-Republican scale approximates that from the prior election plus a random "error" term. That error is critical as it represents new factors since 2016 that shape state voting, such as, for instance, votes by new migrants from other states. The Columbia simulations incorporate this error to reflect all the possible configurations of states, assuming a close vote nationally.

"We found that Biden probably does not need as big a popular vote margin as Hillary Clinton did," said Sigman. "If the vote were 51-49, as it was with Hillary Clinton, that would be the tipping point, and the Electoral College could go either way rather than a certain Trump victory. Our study shows that the 2020 election has less of a tilt to the Republicans than it did in 2016."

###

About the Study

The study is titled "Electoral College Bias and the 2020 Presidential Election."

Authors are: Robert S. Erikson a, Karl Sigman b, and Linan Yao a

A Department of Political Science, Columbia University

B Department of Industrial Engineering and Operations Research, Columbia Engineering

LINKS:

Paper: https://www.pnas.org/cgi/doi/10.1073/pnas.2013581117

DOI: 10.1073/pnas.2013581117

https://www.pnas.org/

https://polisci.columbia.edu/content/robert-s-erikson

https://polisci.columbia.edu/content/about-columbia-political-science

https://www.engineering.columbia.edu/faculty/karl-sigman

http://engineering.columbia.edu/

https://ieor.columbia.edu/

Columbia Engineering

Columbia Engineering, based in New York City, is one of the top engineering schools in the U.S. and one of the oldest in the nation. Also known as The Fu Foundation School of Engineering and Applied Science, the School expands knowledge and advances technology through the pioneering research of its more than 220 faculty, while educating undergraduate and graduate students in a collaborative environment to become leaders informed by a firm foundation in engineering. The School's faculty are at the center of the University's cross-disciplinary research, contributing to the Data Science Institute, Earth Institute, Zuckerman Mind Brain Behavior Institute, Precision Medicine Initiative, and the Columbia Nano Initiative. Guided by its strategic vision, "Columbia Engineering for Humanity," the School aims to translate ideas into innovations that foster a sustainable, healthy, secure, connected, and creative humanity.

Department of Political Science, Columbia University

The Department of Political Science at Columbia University was founded in 1880 and was the first of its kind in the country. Today it is one of the largest departments in the university, serving one of the largest undergraduate majors on campus and annually attracting an elite group of graduate students. Its faculty includes leaders and prizewinners in every subfield in the discipline. Its graduates are leaders in global and domestic private and public sector organizations and professors in world-class colleges and universities in the U.S. and abroad.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.