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Predictors of insurance decisions
Last registered on March 15, 2018


Trial Information
General Information
Predictors of insurance decisions
Initial registration date
March 14, 2018
Last updated
March 15, 2018 5:25 PM EDT
Primary Investigator
University of Georgia
Other Primary Investigator(s)
PI Affiliation
Ludwig-Maximilians-Universität Munich
PI Affiliation
University of Wisconsin-Madison
Additional Trial Information
In development
Start date
End date
Secondary IDs
We investigate the connection between risk preferences and demand for insurance by asking three questions. First, are the identified behavioral constructs related to risk aversion—utility curvature, probability weighting, certainty effect and loss aversion—able to accurately predict an individual’s desired level of insurance? Second, given that there is some predictive power to these behavioral factors, which factor or combination of factors has the best predictive power? Third, which of the different decision models proposed in the literature can best predict the demand for insurance?. We will conduct an incentive compatible experiment in which we elicit these behavioral motives using lottery-based multiple price lists and ask subjects to make a number of insurance choices of varying prices and loss probabilities. We then conduct various analyses to answer the three questions stated above.
External Link(s)
Registration Citation
Jaspersen, Johannes, Marc Ragin and Justin Sydnor. 2018. "Predictors of insurance decisions." AEA RCT Registry. March 15. https://doi.org/10.1257/rct.2783-1.0.
Former Citation
Jaspersen, Johannes et al. 2018. "Predictors of insurance decisions." AEA RCT Registry. March 15. http://www.socialscienceregistry.org/trials/2783/history/26728.
Sponsors & Partners

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Experimental Details
There are no randomized conditions or interventions in this study. It is a measurement and correlation study and while the order of questions will be randomized, all subjects will answer the same sets of questions.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Subjects will select the share of a $3 possible loss they wish to insure, from 0 to 100% in 12 scenarios that vary in the probability of the loss and the loading factor that determines the cost of the insurance.
Primary Outcomes (explanation)
Our dependent variable is the proportion of the $3 loss the subject has chosen to insure in each scenario.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We plan to elicit risk preferences and simulate the insurance purchase decision in an experiment conducted online using the Qualtrics survey software. Our primary sample is subjects recruited via Amazon Mechanical Turk (“mTurk”), and we aim to recruit 1,350 mTurk subjects. As a robustness check, we will also have subjects complete the same Qualtrics study in-person at the University of Wisconsin-Madison BRITE lab. We are targeting 350 subject to complete the in-person version of the study. All subjects will see the same questions, though the order of questions and left/right display of choices will be randomized.

Subjects begin by earning $5.00 in virtual currency for completing a typing task. Each subject will then make 113 choices, one of which will be selected to play out at the end and determine their earnings. We determine risk preferences by presenting subjects with a series of 101 economic choices between uncertain outcomes. This includes 48 questions where subjects can only gain money (between $0.50 and $60.00), 32 questions where subjects can only lose money (between $0.10 and $4.00), and 21 questions where subjects may either gain money ($0.50 or $5.00) or lose money ($0.20 or $2.00). Subjects will use radio buttons to indicate their choice in these lotteries.

Subjects will also make 12 insurance decisions over a potential loss to their earnings of $3.00. In each of the scenarios, subjects may lose $3.00 of their $5.00 initial earnings and have the option to buy insurance to cover all or part of the loss. Subjects use a slider to choose an insurance amount ranging from 0% (no insurance) to 100% (full insurance) in 1% increments. With each click on a level of insurance, subjects can see the price of the insurance and both "loss" and "no loss" outcomes.

Prior to both the insurance and the lottery tasks, subjects have a set of instructions on how to make their choices and must answer several questions to show they understand the instructions. If they answer the questions incorrectly, they are asked to reread the instructions and try again (in-person subjects may ask clarification questions of the experimenter).

After completing both the lottery and insurance tasks, we draw a virtual “ball” from a simulated bucket filled with balls numbered 1-113. The number on the selected ball indicates the question number that will be played out. We then draw a second ball to determine the outcome of that question number (whether there is a gain, a loss, or nothing). The original $5.00 balance is adjusted up or down based on the outcome of the draw and the price of insurance selected (if applicable).
Experimental Design Details
Randomization Method
Randomization will be done by computer
Randomization Unit
Question order, left/right display of choices, question selected to play for real money using subject's choice, "ball" drawn to determine outcome
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
1,350 subjects on Amazon MTurk and 350 subjects at the University of Wisconsin BRITE Lab. For Amazon, we will post the task with the target recruitment as the limit and close the sample when either those targets are hit or 3 weeks have passed, whichever comes first. For the BRITE Lab, we will target 350 subjects for the study. To reach this target we will recruit subjects to the lab and close recruitment when 350 subjects have completed the survey (with complete defined as answering all questions in the incentivized survey elicitation).
Sample size: planned number of observations
1,350 subjects on Amazon MTurk and 350 subjects at the University of Wisconsin BRITE Lab.
Sample size (or number of clusters) by treatment arms
1,350 subjects on Amazon MTurk and 350 subjects at the University of Wisconsin BRITE Lab.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In the basic preference-scale correlations with insurance demand analysis number 1 using non-parametric preference scales, we should be able to detect a change of 0.05 standard deviations in the dependent variable due to a 1 standard deviation change in one of the 6 independent variables at a 1% significance level 95% of the time.
IRB Name
University of Georgia Institutional Review Board
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)