Understanding the Propensity for Individuals to Remain Underinsured for Health in the United States: Avoidance versus Inattention

Last registered on December 14, 2021

Pre-Trial

Trial Information

General Information

Title
Understanding the Propensity for Individuals to Remain Underinsured for Health in the United States: Avoidance versus Inattention
RCT ID
AEARCTR-0008691
Initial registration date
December 10, 2021

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
December 14, 2021, 3:25 PM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Michigan

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2021-12-10
End date
2022-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In the United States, a significant number of adults are uninsured for medical care. There are likely inefficiencies that arise from people not insuring, both in the form of higher mortality as well as uncompensated care costs to providers. In light of all the evidence supporting the take-up of more generous health insurance plans, it remains an open question as to why individuals still appear to underinsure for medical care by taking out no insurance or enrolling in plans with high out of pocket costs. I seek to investigate these patterns. Do individuals underinsure for medical care because they are inattentive to or even unaware about underlying health risks of mortality and illness because such information can be inaccessible or costly to obtain? Or, do they underinsure as a consequence of actively avoiding information about health risks?

Relying on existing observational data cannot allow us to disentangle information avoiders from non-avoiders, nor does it allow us to understand what form of information interventions are more effective in incentivizing attention. I design an experimental study that does both. Specifically, my study allows me to provide novel insights into (a) descriptive characteristics about who information avoiders are, (b) the impact of randomly assigning individuals to two potential treatment groups (low health salience versus high health salience) on the level of risk aversion individuals appear to display when they select for plans, and (c) treatment effect heterogeneity across characteristics including baseline knowledge, learning, and avoider-``types''.
External Link(s)

Registration Citation

Citation
Garud, Keshav. 2021. "Understanding the Propensity for Individuals to Remain Underinsured for Health in the United States: Avoidance versus Inattention." AEA RCT Registry. December 14. https://doi.org/10.1257/rct.8691-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-12-11
Intervention End Date
2022-01-30

Primary Outcomes

Primary Outcomes (end points)
1) Health lottery switch probability from no insurance to the risky health plan
2) Health lottery switch probability from the risky to the safe health plan
Primary Outcomes (explanation)
My analysis plan provides a detailed explanation of how I use individuals' lottery responses to construct my main variables. The primary outcomes listed above are based on individuals for whom I can observe a switch. Additionally, in my analysis plan, I specify a detailed decision rule to adjust how I measure the two outcomes listed above in the case when I observe a reasonable proportion of individuals switching from no insurance directly to the safe health plan.

Secondary Outcomes

Secondary Outcomes (end points)
1) Counts of the number of safe, risky, and "no insurance" choices for each individual
2) Indicator for whether an individual chooses only safe, only risky, or only "no insurance".
Secondary Outcomes (explanation)
My secondary outcomes include additional variation that may come from individuals who never switch lottery choices in the health simulation. I provide further detail for these in my analysis plan.

Experimental Design

Experimental Design
First, I teach and quiz subjects on insurance terms and health facts. I also measure how willing individuals would be to spend time learning about plan characteristics. After this, subjects choose a preferred option: (a) prefer to view health information about mortality and illness for a higher bonus or (b) prefer to view alternative (non-health) information for a lower bonus. I vary the bonus payment associated with viewing health information across different experimental groups, holding the lower bonus for viewing non-health information fixed. I then randomly assign subjects with a higher probability to their preferred option. Individuals who are assigned to view the health information option are randomly assigned to two different types of health information: a first treatment with only facts about mortality and illness and a second treatment with the same facts and in addition, accompanying images. After the interventions, all subjects then complete a health insurance lottery for a hypothetical health issue, choosing between different plan options that differ in their spot price and generosity. Individuals also explain their preferences in the lottery and receive a bonus for thoughtful responses. In the final part, individuals answer several personal and demographic questions.
Experimental Design Details
Randomization Method
Computerized randomization within the survey/online experiment software.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1200-1500 individuals (depending on recruitment)
Sample size: planned number of observations
1200-1500 individuals (depending on recruitment)
Sample size (or number of clusters) by treatment arms
By design, this will be dependent on how many people choose to avoid salient health information in the experiment. However, I assume there may be fewer avoiders than non-avoiders and therefore, there may be more individuals assigned to treatment arms than to the control arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board
IRB Approval Date
2021-07-16
IRB Approval Number
N/A (Exempt Status)
Analysis Plan

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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