How Information Display Affects Health Insurance Decisions

Last registered on June 23, 2026
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Pre-Trial

Trial Information

General Information

Title
How Information Display Affects Health Insurance Decisions
RCT ID
AEARCTR-0018968
Initial registration date
June 21, 2026

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
June 23, 2026, 8:35 AM EDT

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

Locations

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Primary Investigator

Name
Iris SooJin Park
Affiliation
University of Wisconsin - Madison
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Other Primary Investigator(s)

PI Name
Justin Sydnor
PI Affiliation
University of Wisconsin - Madison
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PI Name
Yuxin Wen
PI Affiliation
University of Wisconsin - Madison
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Additional Trial Information

Status
In development
Start date
2026-06-23
End date
2026-06-29
Keywords
Behavior, Health
Additional Keywords
Insurance Decisions
JEL code(s)
I13, D81
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how the format used to present health-insurance information affects plan choices, and how these effects depend on the type of financial-risk information most relevant to the decision. In an online experiment, participants are randomly assigned to view health-insurance plan comparisons using one of four display formats: standard plan features, a simple summary graph, a detailed distribution graph, or a scenario-based display. Participants then make a series of choices between pairs of health insurance plans that differ in premiums, cost sharing, and the distribution of possible out-of-pocket costs. The choice scenarios vary the structure of the decision problem, including dominance, variance, tail risk, and risk tradeoffs. The primary outcome is whether participants choose the benchmark-preferred plan in each scenario. The study tests not only whether alternative displays improve decision quality on average, but also whether their effects depend on the structure of the choice problem and the type of financial-risk information being communicated.
External Link(s)

Registration Citation

Citation
Park, Iris SooJin , Justin Sydnor and Yuxin Wen. 2026. "How Information Display Affects Health Insurance Decisions." AEA RCT Registry. June 23. https://doi.org/10.1257/rct.18968-1.0
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Experimental Details

Interventions

Intervention(s)
The intervention varies the format in which health-insurance plan information is displayed. Participants are randomly assigned to one of four display formats: standard plan features only, a simple summary graph, a detailed distribution graph, or a scenario-based display.
Intervention Start Date
2026-06-23
Intervention End Date
2026-06-29

Primary Outcomes

Primary Outcomes (end points)
For four of the five decision scenarios, there is a plan choice that aligns better with standard theory of first-order and second-order stochastic dominance. The primary outcome of interest for the choices in each of these four sets is whether the participant chose the "dominant" plan in this sense. In the fifth scenario, both plans are rationalizable. For descriptive purposes we will focus on analyzing the share who chose the more risk-averse option (more insurance coverage and higher premiums).
Primary Outcomes (explanation)
For each of the five health-insurance plan-choice scenarios, participants’ choices between Plan A and Plan B will be evaluated relative to a benchmark-preferred plan defined by the structure of the scenario.

In the dominance scenario, the benchmark-preferred plan has lower total costs in every spending state. In the second-order stochastic dominance scenario, the benchmark-preferred plan has lower average costs and no worse outcomes across the spending distribution. In the variance-shift scenario, the benchmark-preferred plan has similar expected costs but less variation in total spending. In the tail-risk scenario, the benchmark-preferred plan reduces the chance of very high-cost outcomes. In the risk-tradeoff scenario, there is not a single universally preferred plan.

Secondary Outcomes

Secondary Outcomes (end points)
We do not have secondary outcome metrics, but we do intend to conduct as a secondary endpoint analysis of the interaction between elicited risk preferences and choices in the tail-risk and risk-tradeoff plan sets. The question of interest is whether any of the visualization treatments increase the correlation between plan choice and measured risk attitudes in these two settings.

As additional secondary end points we will analyze the interaction between choices in the situations with dominance and two measure that capture potential bias: a) failure to understand dominance in a separate dominance question and b) low insurance literacy as measured by inability to accurately estimate out-of-pocket costs given a spending scenario and plan details.
Secondary Outcomes (explanation)
Both of the secondary end points described above are focused on the idea that a key way to assess the value of decision aids is to understand whether they are helping to reduce bias. The measures of a) dominance understanding, b) low insurance literacy, and c) stated risk preferences, give us independent measures of potential sources of bias and the plan is to report how the relationship between those measures and the plan choices is affected by the information-display treatments.

Experimental Design

Experimental Design
Participants are randomly assigned to one of four versions of an online health-insurance plan-choice simulation. The versions differ in how information about plan costs and financial risk is displayed: a control condition with standard plan features only, a simple summary graph, a detailed distribution graph, or a scenario-based display. All participants make choices between pairs of health insurance plans across 5 situations that vary the tradeoffs between plans. We measure a) understanding of dominance, b) insurance literacy, and c) attitudes toward financial risk and use these as independent proxies for potential biases in plan selection.
Experimental Design Details
Not available
Randomization Method
Randomization is done by computer in Qualtrics using embedded-data random-number fields. Participants are randomly assigned to one of four study groups.
Randomization Unit
Treatment assignment is at the participant level. Participants are also individually randomized to one of two plan-pair versions for each scenario type, to the Plan A/Plan B display order.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2,000 individual participants. The treatment is not clustered.
Sample size: planned number of observations
2,000 participants.
Sample size (or number of clusters) by treatment arms
Planned sample size is 500 participants per arm:

* 500 participants in the control arm
* 500 participants in the simple summary graph arm
* 500 participants in the detailed distribution graph arm
* 500 participants in the scenario-based display arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our primary tests will be comparisons of proportions selecting the "dominant" option between two groups. Each group has 500 subjects. Assuming power of 80%, statistical significance threshold of 5%, and a baseline "dominant share" of 50%, we estimate that we are powered for a MDE = 9 percentage point difference in any of our individual group comparisons.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The Minimal Risk Research IRB
IRB Approval Date
2026-05-19
IRB Approval Number
N/A
Analysis Plan