Slowing down to add it up: Using behavioural insights to support decision-making about add-on insurance
Last registered on March 22, 2021

Pre-Trial

Trial Information
General Information
Title
Slowing down to add it up: Using behavioural insights to support decision-making about add-on insurance
RCT ID
AEARCTR-0006236
Initial registration date
July 28, 2020
Last updated
March 22, 2021 6:21 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Behavioural Economics Team of the Australian Government
Other Primary Investigator(s)
PI Affiliation
BETA
PI Affiliation
BETA
Additional Trial Information
Status
Completed
Start date
2020-07-29
End date
2021-03-11
Secondary IDs
Abstract
This study aims to assess the impacts of an information sheet on the sale of add-on insurance. We will test the intervention through a framed-field online experiment with randomisation at the individual level.
Registration Citation
Citation
Breckenridge, Ashley, BETA Team Registration and Hanne Watkins. 2021. "Slowing down to add it up: Using behavioural insights to support decision-making about add-on insurance." AEA RCT Registry. March 22. https://doi.org/10.1257/rct.6236-2.0.
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Experimental Details
Interventions
Intervention(s)
Participants completed an online survey with elements mimicking a real-world scenario, in which they purchased a primary product (e.g., plane ticket, mobile phone) and were shown an advertisement for an add-on insurance product (e.g., travel insurance, extended warranty, consumer credit insurance) towards the end of the purchase. In the treatment conditions, participants were shown the ASIC information sheet immediately before the sale of the add-on product. The information sheet was the key intervention.
Intervention Start Date
2020-07-29
Intervention End Date
2020-08-19
Primary Outcomes
Primary Outcomes (end points)
Whether respondents indicate that they will 'buy' the add-on insurance or not.
Primary Outcomes (explanation)
This is a binary measure. All respondents will be required to decide whether they will 'buy' or 'not buy' add-on insurance in a hypothetical purchasing scenario. We will calculate sample proportions from this response.
Secondary Outcomes
Secondary Outcomes (end points)
Whether respondents indicate on the information sheet that they would like to 'opt-out' of follow-up about the add-on insurance
Secondary Outcomes (explanation)
This is a binary measure. When participants are shown the information sheet the first time (in the hypothetical scenario), they may or may not click on the 'opt-out' box. We will calculate sample proportions from this response.
Experimental Design
Experimental Design
This is a multi-celled framed-field experiment, randomised at the individual level. There will be 21 different cells that respondents can be allocated to.
Experimental Design Details

This study uses a 2x3 factorial design: We will have two independent variables (IVs) creating six versions of the information sheet. In addition, we have a no-information-sheet control condition.

IV 1: Colour of information sheet. Participants will receive either a blue information sheet (education condition), or a red information sheet (warning condition).

IV 2: Claims ratio. The information sheet will either show no claims ratio for the add on product, or it will show that the add-on product has a low claims ratio, or it will show a moderate claims ratio.

Finally, there are three separate purchasing scenarios (phone, travel, loan). 'Scenario' is not an independent variable; we are averaging across scenarios for our primary analyses.
Randomization Method
Randomisation done by the survey software (Qualtrics).
Randomization Unit
individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1
Sample size: planned number of observations
6300 individuals
Sample size (or number of clusters) by treatment arms
300 per cell of the design (21 cells = 3 x (3 x 2 + 1))
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Cohen's h (standardized effect size for comparing proportions) = 0.09. This corresponds to the intervention having an effect of about 3 to 5 percentage points.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
BETA
IRB Approval Date
2020-07-20
IRB Approval Number
BETA-041
Analysis Plan
Analysis Plan Documents
Preanalysis plan BETA 041

MD5: 34969538182fd5d0739033302416ec11

SHA1: ada47790ae52c9b6a897856084e6b66330022ab5

Uploaded At: July 28, 2020

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
August 12, 2020, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
August 12, 2020, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
6243 individuals
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
6243 individuals
Final Sample Size (or Number of Clusters) by Treatment Arms
There were 7 arms in the trial: 1 control + 6 treatment arms. The design was a 2 x 3 factorial, crossing colour (red, blue) with claims ration (CR: none, low, medium). Individuals were randomised to one of these conditions. control = 907 blue-no CR = 899 blue-low CR = 882 blue-mod CR = 889 red-no CR = 886 red-low CR = 897 red mod CR = 883
Data Publication
Data Publication
Is public data available?
No

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Program Files
Program Files
No
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS
Description
Public Report Behavioural Insights and Add-on Insurance
Citation
Commonwealth of Australia, Department of the Prime Minister and Cabinet, Slowing down to add it up: Using behavioural insights to improve decision-making about add-on insurance. ISBN: 978-1-925364-34-7