One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout

Last registered on August 02, 2018

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Pre-Trial

Trial Information

General Information

Title

One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout

RCT ID

AEARCTR-0003199

Initial registration date

August 01, 2018

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

August 02, 2018, 1:53 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Mitchell Hoffman

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Collin Raymond

PI Affiliation

Purdue University

Contact Investigator

PI Name

John Morgan

PI Affiliation

UC Berkeley

Contact Investigator

PI Name

Alan Gerber

PI Affiliation

Yale University

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2010-10-20

End date

2016-04-30

Keywords

Electoral, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Abstract

A common feature of many models of voter turnout is that increasing the perceived closeness of the election should increase voter turnout. However, cleanly testing this prediction is difficult and little is known about voter beliefs regarding the closeness of a given race. In a field experiment during the 2010 US gubernatorial elections, we elicit voter beliefs about the closeness of the election before and after showing different polls, which, depending on treatment, indicate a close race or a not close race. Subjects update their beliefs in response to new information, but systematically overestimate the probability of a very close election. However, the decision to vote is unaffected by beliefs about the closeness of the election. A follow-up field experiment, conducted during the 2014 gubernatorial elections but at much larger scale, also points to little relationship between poll information about closeness and voter turnout.

External Link(s)

Registration Citation

Citation

Gerber, Alan et al. 2018. "One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout." AEA RCT Registry. August 02. https://doi.org/10.1257/rct.3199-1.0

Former Citation

Gerber, Alan et al. 2018. "One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout." AEA RCT Registry. August 02. https://www.socialscienceregistry.org/trials/3199/history/32522

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Why people vote is a core question in political economy. In classic instrumental models of voting, natural assumptions lead to the prediction that individuals are more likely to vote when they believe the election to be close. We conduct two large-scale field experiments / randomized controlled trials (RCTs) in the US that exogenously shift voters' beliefs about the election being close by randomly showing different polls to voters.

Intervention Start Date

2010-10-20

Intervention End Date

2016-04-30

Primary Outcomes

Primary Outcomes (end points)

Voter turnout

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The first RCT was conducted during the 2010 US gubernatorial election cycle and included over 16,000 voters. Using computer surveys in 13 US states, we asked potential voters to predict the vote margin, as well as their beliefs about the chance that the governor's race would be very close (e.g., decided by less than 100 votes). Exploiting variation in real-world polls prior to the election, we divide subjects into groups. We informed the ``Close" group of the results of a poll indicating the narrowest margin between the two candidates, whereas the ``Not Close" group saw a poll indicating the greatest gap between the candidates. (In addition, there was a third group (``Control") who received no poll information and did not get surveyed.) After the election, we used administrative data to determine if people actually voted.

We conducted a second large-scale RCT during the 2014 gubernatorial elections. We randomly mailed postcards to about 80,000 households (125,000 individuals) where we again provided information from the most close or least close poll. Including the control households that didn't get postcards, we have a sample size of over 1.38 million voters. A cross-randomized treatment randomly provided an expert prediction of whether the electorate size would be smaller or larger.

Experimental Design Details

Randomization Method

Randomization done by a computer.

Randomization Unit

Individual for 2010 experiment. Household for 2014 experiment.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

About 16,000 voters in 2010 RCT. About 875,000 households in 2014 RCT.

Sample size: planned number of observations

About 16,000 voters in 2010 RCT. About 1.38 million voters in 2014 RCT.

Sample size (or number of clusters) by treatment arms

In 2010, rough even split between 3 arms (close poll, not close poll, control). In 2014, about 90% control and 10% treated. Treated split roughly evenly between 4 arms in 2x2 design (Close vs. Not Close Poll, Small Electorate Likely vs. Not).

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

Supporting Documents and Materials

IRB