Demand for Sustainable Insurance

Last registered on October 04, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Demand for Sustainable Insurance

RCT ID

AEARCTR-0012016

Initial registration date

September 21, 2023

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 04, 2023, 1:40 PM EDT

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

Locations

Country

Germany

Region

Primary Investigator

Name

Timo Greve

Affiliation

Universität Hamburg

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Petra Steinorth

PI Affiliation

Universität Hamburg

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-09-22

End date

2023-10-17

Keywords

Behavior, Environment & Energy, Finance & Microfinance

Additional Keywords

Sustainability, Insurance

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

In this research project, we investigate the role of sustainability on insurance purchasing behavior. We are the the first to conduct an incentive-compatible experiment using real monetary payoffs to test the willingness to pay for sustainable insurance compared to an insurance contract without such component.

External Link(s)

Registration Citation

Citation

Greve, Timo and Petra Steinorth. 2023. "Demand for Sustainable Insurance." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12016-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

In an incentivized labratory experiment, we present subjects insurance contracts with and without sustainability features and elicit the subejcts willingsness to pay.

Intervention Start Date

2023-10-04

Intervention End Date

2023-10-17

Primary Outcomes

Primary Outcomes (end points)

Willingness to pay in monetary units for 5 different insurance contracts differing only in their sustainability component.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We conduct an experimental study with a mixed design to elicit preferences for insurance products with different sustainability features. Subjects state their willingness to pay for five insurance contracts with different sustainability features after completing a real effort tast. Order of insurance contracts is randomized and design is inspired by Zimmer et al. (2018). In addition, we survey demographics and inculde attention checks.

Experimental Design Details

In a first task, participants have to perform a Real Effort Task similar to Jaspersen et al. (2022), for which they received a reward. Subjects have to type four text passages. We show an image of the written passage and subject have to type the text into a box below.
Following this, the subjects are told that their capital is under risk of theft and with a probability of five percent they would lose their whole capital. The participants are informed that they have the possibility to buy full insurance against this threat and that they have the opportunity to state their maximum willingness to pay for five insurance contracts. The contracts provide the same coverage and differ only in their sustainability component (10% or 20 % of the premium is donated for environmental protection, 10% or 20 % of the premium is donated to social causes or no sustainable component, respectively). The participants are informed, that their decision leads to a real monetary donation for the displayed company (e.g. Atomsfair gGmbH for the environmental component), if this contract will be selected.
The participants don’t know beforehand which of these contracts will be the randomly selected contract, for which the stated willingness to pay is compared to the true insurance price. If the WTP is higher or equal than the true price, the participant is insured and his wealth is reduced by the true price. If the WTP is lower no insurance protection is granted. To quantify the maximum willingness to pay (WTP), we use a modification of the standard Becker-DeGroot-Marschak mechanism (Becker et al., 1964), which is also utilized in Schade’s (Schade and Kunreuther, 2001) secret price mechanism. We inform the participants that it is in their best interest to state their true maximum willingness to pay and advised them to do so. If they state a price lower than their maximum willingness to pay, they might not be insured even though they would have been willing to buy protection at a higher price than the amount stated. If they state a price higher than their maximum willingness to pay, they might have to pay this higher price even though they would not have been willing to do so.
In addition, participants answer several survey questions including sociodemographics and individual preferences. We also include attention checks and ask participants about their understanding of the purpose of the experiment.

Randomization Method

Furthermore subjects are randomly drawn out of the lab's participant pool using hroot.The contracts are displayed in a randomized order generated by computers. Subjects are randomly seated (done by the lab) and receive a random envelope containing their secret insurance premiums.

Randomization Unit

Individual randomization in seven experimental session.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Seven sessions a 30 individuals

Sample size: planned number of observations

This experiment will have approximately 200 individuals, who state the WTP for five insurance conmtracts each, resulting in 1000 observations.

Sample size (or number of clusters) by treatment arms

Each insurance contracts will have approximately 200 decisions

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We used G*Power (Linear multiple regression: Fixed model, R^2 deviation from zero). f^2 = 0.02 (Conservative) alpha = 0.05 Power = 0.95 Predictors = 4 (Basemodel: one dummy for each contract with a sustainability component - > 4 in total) Leading to a total sample size of 934. We will include controlvariables (which would lead to a higher samplesize needed), given that the observed effect is stronger than our conservative approach.

Supporting Documents and Materials

IRB