Samuel Baltz

Massachusetts Institute of Technology

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Charles Stewart

Massachusetts Institute of Technology

Charles Stewart

Massachusetts Institute of Technology

Joseph R. Loffredo

Massachusetts Institute of Technology

Kevin E. Acevedo Jetter

Massachusetts Institute of Technology

James M. Snyder

National Bureau of Economic Research

Shiro Kuriwaki

Yale University

Zachary Djanogly Garai

Massachusetts Institute of Technology

Kate Murray

Massachusetts Institute of Technology

Can E. Mutlu

Harvard University

Shigeo Hirano

Columbia University

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Massachusetts Institute of Technology

Harvard University

University of Zurich

Harvard University Press

University of California, Berkeley

Rutgers, The State University of New Jersey

Rutgers Sexual and Reproductive Health and Rights

New York University

Michigan State University

University of California, San Diego

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Cast vote records: A database of ballots from the 2020 U.S. Election

Shiro Kuriwaki Mason Reece Samuel Baltz Aleksandra Conevska Joseph R. Loffredo

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers-covering 42.7 million voters in 20 states who voted for more than 2,204 candidates. This database serves as an uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump.

10.2139/ssrn.5018616

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What Happens When the President Calls You an ‘Enemy of the People’? Election Officials and Public Sentiment

SSRN Electronic Journal (2022)

Joelle Gross Samuel Baltz Charles Stewart

False information about the legitimacy of recent American elections has prompted a barrage of harsh rhetoric against the officials who administer them. This spike in negativity, largely occurring through social media, is driving people out of these essential jobs. This article measures the extent of this negativity, how it has trended over time, which state administrations are targeted by it most, and what sorts of accounts are sending it. By collecting every reply to any Twitter account managed by the agency or person officially responsible for administering a state’s elections, we show that the usage of keywords related to election fraud has spiked in recent years, while the sentiments of the replies have grown almost universally harsher. While left-leaning repliers are usually negative towards Republican officials, and vice versa, some officials have begun to receive negative replies from both the left and right, with sustained pile-ons led almost entirely by right-leaning repliers.

10.2139/ssrn.4206554

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Citations (1)

Audits of the 2020 American Election Show an Accurate Vote Count

Samuel Baltz Fernanda Gonzalez Kevin Guo J. Jaffe Charles Stewart

10.2139/ssrn.4956762

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Votes Can Be Confidently Bought in Some Ranked Ballot Elections, and What to Do about It

Political Analysis (2024)

Jack Williams Samuel Baltz Charles Stewart

Abstract We show that, in some ranked ballot elections, it may be possible to violate the secret vote. There are so many ways to rank even a handful of candidates that many possible rankings might not be cast by any voter. So, a vote buyer could pay someone to rank the candidates a certain way and then use the announced election results to verify that the voter followed through. We examine the feasibility of this attack both theoretically and empirically, focusing on instant runoff voting (IRV). Although many IRV elections have few enough candidates that this scheme is not feasible, we use data from San Francisco and a proposed election rule change in Oakland to show that some important IRV elections can have large numbers of unused rankings. There is no evidence that this vote-buying scheme has ever been used. However, its existence has implications for the administration and security of IRV elections. This scheme is more feasible when more candidates can be ranked in the election and when the election results report all the ways that candidates were ranked.

10.1017/pan.2024.4

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American election results at the precinct level

Scientific Data (2022)

Samuel Baltz Alexander Agadjanian Declan Chin John Curiel Kevin DeLuca

Abstract We describe the creation and quality assurance of a dataset containing nearly all available precinct-level election results from the 2016, 2018, and 2020 American elections. Precincts are the smallest level of election administration, and election results at this granularity are needed to address many important questions. However, election results are individually reported by each state with little standardization or data quality assurance. We have collected, cleaned, and standardized precinct-level election results from every available race above the very local level in almost every state across the last three national election years. Our data include nearly every candidate for president, US Congress, governor, or state legislator, and hundreds of thousands of precinct-level results for judicial races, other statewide races, and even local races and ballot initiatives. In this article we describe the process of finding this information and standardizing it. Then we aggregate the precinct-level results up to geographies that have official totals, and show that our totals never differ from the official nationwide data by more than 0.457%.

Precinct

Governor

10.1038/s41597-022-01745-0

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Citations (12)

An unexpected consensus among diverse ways to measure democracy

Democratization (2022)

Samuel Baltz Fabricio Vasselai Allen Hicken

We show that behind decades of disagreements over how to classify regime types, there is an unexpectedly strong consensus about which countries have been democratic in which years. We match many-valued classifications of democracies and autocracies to binary classifications, by finding the cutpoint that dichotomizes each many-valued measure so that it matches each binary measure as closely as possible. Despite the sharp differences between these datasets, we find identical or nearly identical optimal cutpoints for almost every major classification. This is evidence of a strong underlying agreement between the classifications. We show that the many-valued measures, when dichotomized in the optimal way, can replicate major results in the study of democracy. We also examine which sorts of countries are counted as democracies in this consensus, and we show that countries where national elections have never produced a turnover between parties are a particular focus of disagreement.

Autocracy

Replicate

10.1080/13510347.2021.2013822

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Citations (17)

Replication Data for: Reducing Bias in Wikipedia’s Coverage of Political Scientists

Harvard Dataverse (2021)

Samuel Baltz

One in five political scientists with biographies on Wikipedia are women, while nearly half are American. These data document remaining biases by nationality in Wikipedia's coverage of political scientists.

Replication

10.7910/dvn/xaxmvk

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Computer Simulations of Elections, With Applications to Understanding Electoral System Reform

Deep Blue (University of Michigan) (2022)

Samuel Baltz

Electoral reform

Electoral system

10.7302/4597

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Cast vote records: A database of ballots from the 2020 U.S. Election

arXiv (Cornell University) (2024)

Shiro Kuriwaki Mason Reece Samuel Baltz Aleksandra Conevska Joseph R. Loffredo

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers -- covering 42.7 million voters in 20 states who voted for more than 2,204 candidates. This database serves as a uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump.

10.48550/arxiv.2411.05020

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The probability of casting a pivotal vote in an Instant Runoff Voting election

arXiv (Cornell University) (2022)

Samuel Baltz

I derive the probability that a vote cast in an Instant Runoff Voting election will change the election winner. I phrase that probability in terms of the candidates' expected vote totals, and then I estimate its magnitude for different distributions of voter preferences. The result is very similar to the probability of casting a pivotal vote in a Single-Member District Plurality election, which suggests that Instant Runoff Voting does not actually increase or decrease voters' incentives to vote strategically. The derivation uncovers a counter-intuitive phenomenon that I call "indirect pivotality", in which a voter can cause one candidate to win by ranking some other candidate on their ballot.

Spoilt vote

Bullet voting

Anti-plurality voting

Contingent vote

Approval voting

Instant

Instant-runoff voting

10.48550/arxiv.2210.01657

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