Health attribution and health policy preferences: A survey experiment

Last registered on November 01, 2022
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Pre-Trial

Trial Information

General Information

Title
Health attribution and health policy preferences: A survey experiment
RCT ID
AEARCTR-0010126
Initial registration date
October 19, 2022

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 25, 2022, 10:45 AM EDT

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

Last updated
November 01, 2022, 4:46 PM EDT

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

Locations

Country
Germany
Region

Primary Investigator

Name
Sharon Baute
Affiliation
University of Konstanz
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Other Primary Investigator(s)

PI Name
Luna Bellani
PI Affiliation
University of Konstanz
Contact Investigator

Additional Trial Information

Status
In development
Start date
2022-11-10
End date
2023-01-01
Keywords
Behavior, Governance, Health, Welfare
Additional Keywords
inequality, health attribution, survey experiment, preferences
JEL code(s)
Health and Inequality; Health Behavior; Health Insurance, Public and Private; Welfare Economics; Equity, Justice, Inequality, and Other Normative Criteria and Measurement;
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
European welfare states are being challenged by persisting health inequalities. Against this background, this study will examine the relation between health attribution and health policy preferences. The purpose is twofold. First, we aim to unravel how citizens attribute health inequalities and what the individual-level predictors of health inequality attribution are. Second, we aim to gain a better understanding of how health attribution shapes health policy preferences. To this end, the study draws on a survey experiment and observational data, to be fielded among the German population. The module is part of a large-N opinion survey on inequality and social mobility conducted by the University of Konstanz.
External Link(s)

Registration Citation

Citation
Baute, Sharon and Luna Bellani. 2022. "Health attribution and health policy preferences: A survey experiment." AEA RCT Registry. November 01. https://doi.org/10.1257/rct.10126-1.1
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Experimental Details

Interventions

Intervention(s)
A survey module with an embedded survey experiment is designed. To examine how citizens attribute responsibility for health and health care costs, survey vignettes are used to describe a fictive person in need of medical treatment. The vignettes provide a causal cue on the health problems of the fictive person. In total, respondents are presented two different scenarios of the in total 36 unique scenarios included in the experiment. The characteristics in each vignette are randomly assigned. Hereby, heterogeneity in the effects of individual dimensions will be analysed.
Intervention Start Date
2022-11-10
Intervention End Date
2022-12-05

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes of interest are respondents' attribution of responsibility of (1) health outcomes and (2) health care costs.

(1) Question wording:
"Some people might think that this person is to blame for his own health problems. Others might think that this person is not to blame at all.
What is your spontaneous impression about it?"
Answer categories:
11-point
0 = This person is not at all to blame
10 = This person is completely to blame

(2) Question wording:
"Some people feel that in a fair society, this person should pay for the entire cost of his medical treatment. Others think that in a fair society, all treatment costs of this person should be covered by the society, through taxes or insurance contributions. What is your opinion?"
Answer categories:
11-point
0 = Society should pay all the costs
10 = The person should pay all the costs
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcomes are respondents' (1) health inequality attributions and (2) general health policy preferences.

(1)
Question wordings:

"On average, low-income Germans live 6 years shorter than high-income Germans. There are many reasons for the difference in life expectancy. What do you think is the main reason for this shorter life expectancy?"

Answer categories:
1. Because their own behavior damages their health.
2. Because of inborn characteristics (genetic or biological)
3. Because of the environmental conditions they are exposed to at work or where they live
4. Because of unequal treatment in the health care system

(2)
Question wording:
"How willing would you be to pay higher taxes to improve health care for all people in Germany?"

Answer categories:
5-point
1 - Very unwilling, 2 - Fairly unwilling, 3 - Neither willing nor unwilling, 4 - Fairly willing, 5 - Very willing

Question wording:
"Please indicate now to what extent the following things should be the responsibility of the state: ensure adequate health care for the sick"

Answer categories:
11-point
0 = The state should not be responsible at all
10 = The state should be entirely responsible
Secondary Outcomes (explanation)
Different operationalizations of the health inequality attribution variable are possible on the basis of the full range of response categories (behavioral, biological, environmental, institutional), including binary variables.

Experimental Design

Experimental Design
The survey vignettes differ along three dimensions: the income, the health problem, and a cue about the cause of the health problem. A 3*3*4 experimental research design is implemented, resulting in 36 unique combinations. Each vignette consists of four pieces of information: (1) An introduction sentence, (2) a randomization of the income, (3) a randomization of the health problem, (4) a randomization of the causal cue of the health problem.
Experimental Design Details
Randomization Method
Randomization by computer.
Randomization Unit
Randomisation at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
6000 individuals.
Sample size: planned number of observations
6000 individuals.
Sample size (or number of clusters) by treatment arms
A minimum of 330 observations per unique vignette.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
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