Political Identity and Conjunction Bias

Last registered on January 02, 2025
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Pre-Trial

Trial Information

General Information

Title
Political Identity and Conjunction Bias
RCT ID
AEARCTR-0014756
Initial registration date
November 03, 2024

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
January 02, 2025, 7:05 AM EST

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

Locations

Country
China
Region

Primary Investigator

Name
Ziyu Zhang
Affiliation
Renmin University of China
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Other Primary Investigator(s)

PI Name
Bin Miao
PI Affiliation
Renmin University of China
Contact Investigator
PI Name
Songfa Zhong
PI Affiliation
Hong Kong University of Science and Technology, National University of Singapore
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Additional Trial Information

Status
In development
Start date
2024-10-01
End date
2025-12-31
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We investigate the role of political identity and decision bias through an online experiment involving a representative US sample (N≈1200), deployed days before the 2024 US presidential election. Participants are asked to choose between event-contingent lotteries and sure amounts, where the events involve the US election outcome, unemployment rate change, and public health care system ranking change. Our analysis explores (i) how decision-making varies between single and joint events to identify conjunction bias, (ii) how partisan identities influences this bias.
External Link(s)

Registration Citation

Citation
Miao, Bin, Ziyu Zhang and Songfa Zhong. 2025. "Political Identity and Conjunction Bias." AEA RCT Registry. January 02. https://doi.org/10.1257/rct.14756-1.0
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Experimental Details

Interventions

Intervention(s)
In our experiment, participants make risk choices based on the outcomes of the 2024 presidential election, unemployment in the following year, healthcare ranking in the following year, and combinations of these events. They will also estimate probabilities for each event and combination.

Intervention (Hidden)
The key treatment variation is what participants bet on: directional changes (increase/decrease) versus numerical parity (odd/even digits), and the ordering of events in joint scenarios. This design allows us to identify how political identify influences conjunction fallacy across different contexts.

The experiment will take place days before the 2024 presidential election, with a follow-up payment one year later based on prediction accuracy.
Intervention Start Date
2024-10-31
Intervention End Date
2024-11-05

Primary Outcomes

Primary Outcomes (end points)
1. (Certainty equivalents) Differences between valuations of single-event bets versus joint-event bets.
2. (Probability estimates) Differences between probability estimates of single event versus joint events.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In Part I, participants complete 14 incentivized multiple-choice lists that aim to elicit their certainty equivalents for single-event bets and joint-event bets.
In Part II, participants complete 14 corresponding probability estimation tasks (unincentivized) for the same events.

Experimental Design Details
Main Treatment:
Single-event bets: election outcome, directional changes in unemployment /healthcare ranking.
Joint-event bets: describe election outcome and directional change of unemployment/healthcare ranking in sequential order (e.g., Trump wins and unemployment rate increases from Sep. 2024 to Sep. 2025).
We compare decisions between single events and joint events to identify conjunction bias and subsequently investigate whether this bias differs across political parties.

The following two treatments are designed to explore the mechanisms.

Reverse Order Treatment:
Single-event bets: election outcome, directional changes in unemployment/healthcare ranking.
Joint-event bets: describe election outcome and directional change of unemployment/healthcare ranking in reversed order. We describe the directional change in unemployment or healthcare first, then the election outcome (e.g., unemployment rate increases from Sep. 2024 to Sep. 2025 and Trump wins).

Odd/Even Treatment:
Single-event bets: election outcome, parity of unemployment rate/healthcare ranking.
Joint-event bets: describe election outcome and parity of unemployment/healthcare ranking in sequential order (e.g., Trump wins and unemployment rate is odd in Sep. 2025).
Randomization Method
randomization done by a computer program
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
About 1200 participants
Sample size (or number of clusters) by treatment arms
About 400 participants for each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Lab of National Governance and Development, Renmin University of China
IRB Approval Date
2024-10-01
IRB Approval Number
RUCecon-202410-1
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