Back to History Current Version

An Experimental Study of Political Expertise and the Democratic Ideal

Last registered on November 05, 2019

Pre-Trial

Trial Information

General Information

Title
An Experimental Study of Political Expertise and the Democratic Ideal
RCT ID
AEARCTR-0004981
Initial registration date
November 04, 2019

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
November 05, 2019, 9:39 AM EST

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

Locations

Region

Primary Investigator

Affiliation
Florida State University

Other Primary Investigator(s)

PI Affiliation
University of Rochester

Additional Trial Information

Status
In development
Start date
2019-11-05
End date
2020-06-01
Secondary IDs
Abstract
How does the level of political expertise among voters affect the quality of democratic choice? The answer to this question underlies one of the key hallmarks of representative democracy, namely, whether individuals with the best knowledge of political issues and policies are the ones who ultimately make political decisions. We propose experiments that will address this question by investigating the quality of democratic choice made by individual voters who have the same goals and studying how changes in the level of expertise among voters affect the quality of democratic choice and voters' willingness to participate in elections. Using a novel experimental design we will directly elicit voters' willingness to vote which will allow us to examine several distinct collective action issues that arise in democratic decision-making.
External Link(s)

Registration Citation

Citation
Ou, Kai and Scott Tyson. 2019. "An Experimental Study of Political Expertise and the Democratic Ideal." AEA RCT Registry. November 05. https://doi.org/10.1257/rct.4981-1.1
Experimental Details

Interventions

Intervention(s)
We will change the level of political expertise in the laboratory.
Intervention Start Date
2019-11-06
Intervention End Date
2020-06-01

Primary Outcomes

Primary Outcomes (end points)
Whether an individual voter decides to participate in voting and what the maximum cost they would like to take to make their vote count
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will conduct laboratory experiments and use undergraduate student subjects to investigate how the change of level of political expertise affects voting decisions and the quality of democratic choice.
Experimental Design Details
Our experiment will represent a common value election between two alternatives, A and B, where the best option for every voter (or experimental subject) will be the same, but where the alternative that best serves voters' interest will not be known to every voter. To determine voters' best alternative, an equally likely state of the world (also A or B) will be independently drawn.

The electorate will be composed of 5 (human) subjects who must collectively choose an alternative, where the committee's decision will be made through a simple majority election, i.e. whichever color receives more votes will be the collective decision that is applied to everyone; ties will be broken by a fair coin toss. To represent the behavior of voters whose preferences are independent of the state of the world, we also consider two additional votes cast randomly by the computer, which we refer to as the partisan bias. The partisan bias will have three possible values that correspond to two votes for A, two votes for B, or one vote for each, A and B. Each of these events are equally likely. The partisan bias in our experiment will provide a hurdle that subjects must cross to achieve their best alternative. The state of the world and the level of the partisan bias will be randomly determined prior to subjects making voting decisions.

Voters will not know the state of the world ex ante and will not be told what the correct decision is until after the collective decision has been made. Voters will have identical preferences that depend only on the group decision and the state of the world. If the collective decision matches the state of the world, then all participants will receive a high payoff of 110 experimental points, Otherwise, all participants will receive a low payoff of 10 experimental points.

Before voting, some voters will be exogenously assigned as expert voters, and will be perfectly informed of the state of the world, which tells them which alternative best serves the common interest. In addition, an expert voter will also be told the value of the partisan bias. To determine whether a voter is assigned expertise, in each election the computer will generate a random INFORMATION NUMBER for each subject from a uniform distribution between 1 and 100 points. Subjects will become experts based on an exogenously assigned cutoff over INFORMATION NUMBERS that is symmetric, commonly known, and fixed in each treatment. If the computer randomly generates an INFORMATION NUMBER that is higher than the exogenously assigned threshold for each treatment, the subject will not be told the state of the world or the partisan bias (and is not charged the expertise fee). If the INFORMATION NUMBER is lower than the exogenously assigned threshold, then the state of the world and partisan bias is privately revealed. There is no cost for the information (political expertise). We administer expertise in this way because our method is relatively simple for subjects to understand and calculate the level of expertise. As a critical part of our design, expert voters do not know exactly about the number of how many voters are experts, but all the voters know the distribution of expert voters, and can form a posterior belief regarding the number of expert voters.

After expertise has been administered, each subject will be asked to make a voting decision. We design a novel method to elicit an individual's willingness to vote that takes advantage of the Becker-Degroot-Marshak (BDM) mechanism. Once a subject reports her willingness to vote, the computer will independently generate a voting cost from a uniform distribution between 1 and 100. If a voter had reported a vote choice, and if her individual voting cost is lower than her reported willingness to vote, then the subject will pay the voting cost, and her vote is included in the collective decision. Instead, if a subject's individual voting cost is higher than her reported willingness to vote, then the subject's vote will not be counted toward the committee's decision and she will not be charged the cost of voting.
Randomization Method
We will use undergraduate student subjects in our laboratory experiments. 120 subjects will be randomly recruited from a subject pool in which there are more than 3000 registered subjects. The recruited subjects will be randomly assigned to treatments and sessions.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
120 subjects
Sample size: planned number of observations
3600 observations will be generated from 120 subjects playing voting games for 30 rounds
Sample size (or number of clusters) by treatment arms
60 subjects for High Expertise Treatment
60 subjects for Low Expertise Treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
IRB

Institutional Review Boards (IRBs)

IRB Name
Florida State University
IRB Approval Date
2019-10-22
IRB Approval Number
n/a

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Intervention

Is the intervention completed?
Yes
Intervention Completion Date
December 31, 2020, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
December 31, 2020, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
120 student subjects
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
120 participants, 24 independent electorates, 720 elections, 3600 vote choices and willingness-to-vote
Final Sample Size (or Number of Clusters) by Treatment Arms
60 participants, 12 independent electorates, 360 elections, 1800 vote choices and willingness-to-vote
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials