Voter Confidence and Electoral Participation

Last registered on February 24, 2023

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Pre-Trial

Trial Information

General Information

Title

Voter Confidence and Electoral Participation

RCT ID

AEARCTR-0010300

Initial registration date

October 24, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

October 31, 2022, 3:34 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

February 24, 2023, 6:35 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Thomas Cao

Affiliation

Stanford University

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

Completed

Start date

2022-10-25

End date

2022-11-07

Keywords

Electoral, Governance

Additional Keywords

American politics, political attitudes

JEL code(s)

D72

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

In the past two decades, 30% of U.S. voters have indicated mistrust in electoral outcomes, and the number is growing in the aftermath of the 2020 presidential elections and an increasingly polarized political landscape. Our project seeks to utilize messaging on the bipartisan nature of the electoral processes to enhance voter confidence in U.S. election results, and evaluate whether an increase in voter confidence will lead to higher turnout for different groups of registered voters.

We plan to conduct a randomized experiment before the midterm elections in November 2022 and match respondents’ treatment status with their voting behavior on an individual level based on the subsequent voter file data. We will analyze our treatment effects on those who voted in 2018 and those who did not, and across ideological lines.

As the first large-scale experiment to evaluate the causal relationship between voter confidence and electoral participation, our project will contribute to research on American politics, political communication, and behavioral/experimental economics. Moreover, our findings will provide actionable insights and policy recommendations for election officials.

External Link(s)

Registration Citation

Citation

Cao, Thomas. 2023. "Voter Confidence and Electoral Participation." AEA RCT Registry. February 24. https://doi.org/10.1257/rct.10300-2.0

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Experimental Details

Interventions

Intervention(s)

We will provide treatment messaging focusing on the bipartisan oversight aspect of the electoral process, embedded in a short survey.

Intervention Start Date

2022-10-25

Intervention End Date

2022-11-07

Primary Outcomes

Primary Outcomes (end points)

Our primary outcomes are the respondents' self-reported confidence levels in their state's/national election outcomes and whether they turn out to vote in the 2022 midterm elections.

Primary Outcomes (explanation)

Confidence levels in state/national election outcomes will be self-reported on a 1~5 scale. Individual-level voter turnout information will be available from the official voter files (organized by L2).

Secondary Outcomes

Secondary Outcomes (end points)

Our secondary outcomes are the respondents' self-reported voter confidence in election outcomes in red/blue/swing states on a 1~5 scale, self-reported voting history in 2018 and 2020 (for cross-validation purposes), self-reported turnout inclination (on a 1~5 scale where 1 = definitely not and 5 = definitely), self-reported interest levels in politics, the midterm elections, care about election results, estimations of how close the elections will be, and which party will win the House and Senate respectively.

Secondary Outcomes (explanation)

Experimental Design

Registered voters will receive an email invite to our survey. Respondents will be randomly assigned to treatment and control groups. Their answers will be linked to official voter files for voting information.

Experimental Design Details

Registered voters will receive an email invite to our survey. We will first ask about each respondent's ideology and information sources (traditional and social media consumption), and whether they have already voted in the 2022 midterm elections. Then, respondents will be randomly assigned to treatment and control groups with equal probabilities. The treatment group will see the bipartisan oversight message. The control will only see a generic message on the upcoming midterm elections. After the message, both groups will see post-treatment questions on voting history and confidence levels in various election outcomes. If a respondent does not indicate they have already voted in the 2022 elections, they will be asked whether they will turn out to vote, and if so, how (early vs. on November 8; in person vs. by mail). Finally, we will include standard demographic questions to cross-validate the individual-level information on registered voters provided by L2.

Randomization Method

Randomization is done using the Qualtrics randomizer.

Randomization Unit

Individual respondents

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

We plan to send out approx. 8 million emails in order to recruit 20,000 respondents, although the actual number of respondents will be determined by our response rates (our baseline assumption is 0.25%).

Sample size (or number of clusters) by treatment arms

Approximately half of the respondents will be assigned to treatment and control each.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We expect that one-third to one-half of our respondents will have already voted when they take our survey. As a result, our actual sample size should be around 10,000~13,000. This will allow us to identify a treatment effect of 2 percentage point difference in turnout rates at alpha = 0.05 level with 80% power. Equivalently, if there is no effect, N = 10,000 will achieve a standard error of 0.01 and thus a 95% CI of [-0.02, 0.02], allowing us to rule out with high confidence any effect greater than 2 percentage points.

Supporting Documents and Materials

IRB