Recycling uncast votes as a means to increase voter turn out

Last registered on October 19, 2021
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Pre-Trial

Trial Information

General Information

Title
Recycling uncast votes as a means to increase voter turn out
RCT ID
AEARCTR-0006679
Initial registration date
October 27, 2020

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 28, 2020, 9:14 AM EDT

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

Last updated
October 19, 2021, 6:37 AM EDT

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

Locations

Country
Switzerland
Region
Zurich

Primary Investigator

Name
Serhiy Kandul
Affiliation
University of Zurich
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Other Primary Investigator(s)

PI Name
Olexandr Nikolaychuk
PI Affiliation
FSU Jena
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Additional Trial Information

Status
In development
Start date
2020-10-29
End date
2021-10-31
Keywords
Behavior, Electoral, Lab
Additional Keywords
voter turnout, voting preferences, behavioral economics, online experiment
JEL code(s)
D01, D02, D72, D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Sufficient voter turnout is a desirable property of a healthy democratic system. Unfortunately, historical data show that a sizable portion of the eligible electorate tends to abstain. This project aims to increase voter turnout by leveraging important insights from personal and social psychology and behavioral economics.
The main idea is to modify the current vote counting rule in the following manner: uncast votes are not ignored but rather, randomly allocated to the alternatives. We hypothesize that this intervention will prompt people to turn out to vote to avoid having their votes misused or misallocated by the random mechanism. The intervention does not restrict voters' freedom (as opposed to, e.g., obligatory voting) and would be easy to implement in real life. We test the effectiveness of the proposed intervention both in the field (U.S. presidential elections) and in the laboratory experiments in Switzerland.
External Link(s)

Registration Citation

Citation
Kandul, Serhiy and Olexandr Nikolaychuk. 2021. "Recycling uncast votes as a means to increase voter turn out." AEA RCT Registry. October 19. https://doi.org/10.1257/rct.6679-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The intervention is centered around the idea of integrating uncast votes into the determination of the winner of the election.
Intervention Start Date
2020-10-29
Intervention End Date
2021-07-30

Primary Outcomes

Primary Outcomes (end points)
voter turnout, attitudes towards the intervention
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Voting preferences, i.e. the share of voters supporting a particular candidate
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We study several variations of randomization mechanism.

Experimental Design Details
In the baseline, participants are reminded of the election and choose to vote or abstain. In condition "random", before participants choose to vote or abstain, we inform them that the uncast votes will be randomly allocated among candidates. In condition "default", before participants choose to vote or abstain we inform them that all the votes were initially randomly allocated among candidates. These basic conditions are implemented in an unincentivized survey of U.S. voters in the context of 2020 presidential election. In a more abstract abstract incentivized laboratory setting in Switzerland we implement "random+feedback" condition where participants learn individually the allocation of their uncast votes.
Randomization Method
Randomized is done by a software (oTree)
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1800 for the US. survey;
600 for the experiment in Switzerland.
Sample size: planned number of observations
1800 for the US. survey; 600 for the lab study in Switzerland.
Sample size (or number of clusters) by treatment arms
600 per condition for the U.S. survey;
200 per condition (3 conditions in total) in the lab experiment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association of Experimental Economics
IRB Approval Date
2020-10-27
IRB Approval Number
4HAMU7uB
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials