Wealth Effects and Demand for Probabilistic Insurance
Last registered on July 01, 2019

Pre-Trial

Trial Information
General Information
Title
Wealth Effects and Demand for Probabilistic Insurance
RCT ID
AEARCTR-0004106
Initial registration date
April 12, 2019
Last updated
July 01, 2019 2:27 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Universität Hamburg
Other Primary Investigator(s)
Additional Trial Information
Status
Completed
Start date
2019-04-15
End date
2019-06-19
Secondary IDs
Abstract
This study examines the effect of different wealth levels on insurance demand for probabilistic insurance contracts. Previous research has shown that even the existence of very small default probabilities lead to a sharp decrease in individuals’ willingness to pay. In addition to wealth effects, we also investigate the impact of cognitive effort and financial literacy on the demand for probabilistic insurance.
External Link(s)
Registration Citation
Citation
Hillebrandt, Marc-Andre. 2019. "Wealth Effects and Demand for Probabilistic Insurance." AEA RCT Registry. July 01. https://www.socialscienceregistry.org/trials/4106/history/49011
Experimental Details
Interventions
Intervention(s)
In a previous task, individuals are equipped with different endowments. Then, they are confronted with an investment decision that is actually played out, resulting in different wealth levels. Afterwards, all subjects will answer the same set of questions regarding insurance demand with different default probabilities (but in randomized order).
Intervention Start Date
2019-04-15
Intervention End Date
2019-06-19
Primary Outcomes
Primary Outcomes (end points)
Investigation of willingness to pay for probabilistic insurance coverage given different wealth levels
Primary Outcomes (explanation)
Subjects state their willingness to pay for three probabilistic insurance contracts. Default probabilities are given by 0%, 0.1%, and 1%. Previous research has shown that even the existence of very small default probabilities lead to a sharp decrease in individuals’ willingness to pay. In this experiment, we analyze different factors (such as wealth, cognitive effort, financial literacy, and other sociodemographic factors) influencing the demand for probabilistic insurance.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Most studies (such as Wakker et al. (1997), and Zimmer et al. (2009)) testing the hypothesis of probabilistic insurance so far used hypothetical payoffs. Zimmer et al. (2018) were the first to conduct an incentive-compatible experiment using real monetary payoffs. In their study, subjects were able to insure themselves against a potentially high loss. In contrast, the value to be insured in our study is relatively low. Subjects are exposed to the risk of a loss of 3,500 Talers. This occurs with a probability of 5%. Afterwards, three insurance contracts will be presented (in random order to avoid an order effect), which only differ in the probability of default, namely 0%, 0.1%, and 1% default probability. Subjects are asked to state their maximum willingness to pay. Since all subjects will be incentivized, they should state their true willingness to pay.
Prior to this insurance decision, subjects are equipped with different endowments and execute an investment task following Gneezy and Potters (1997) (see AEA RCT Registry “Relative Wealth Placement and Risk-Taking Behavior” [currently under review, we will edit this and replace it with a link given a positive review] for more details). As a result, two individuals can subsequently achieve the same wealth level, even though they have shown different levels of risk aversion previously. This way, we hope to disentangle wealth effects from other confounding effects such as risk aversion, cognitive effort, financial literacy, and other sociodemographic factors like gender.
Experimental Design Details
Randomization Method
All subjects will be recruited using hroot (Bock, Baetge, and Nicklisch, 2014). Further randomization will be done by oTree software.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
18 experimental sessions
Sample size: planned number of observations
432 subjects
Sample size (or number of clusters) by treatment arms
144 subjects beginning to state their willingness to pay for insurance given default probabilities of 0%, 0.1%, and 1%, respectively
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
June 19, 2019, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
June 19, 2019, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
19 experimental sessions
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
420 subjects
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers