Anchors Away! A Large-Scale Trial Testing Approaches to Reducing Credit Card Debt

Last registered on December 21, 2018

View Trial History

Pre-Trial

Trial Information

General Information

Title

Anchors Away! A Large-Scale Trial Testing Approaches to Reducing Credit Card Debt

RCT ID

AEARCTR-0003414

Initial registration date

December 18, 2018

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

December 21, 2018, 10:32 PM EST

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

Locations

Country

Australia

Region

Primary Investigator

Name

Michael Hiscox

Affiliation

Harvard University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Ryan Buell

PI Affiliation

Harvard Business School

Contact Investigator

Additional Trial Information

Status

In development

Start date

2018-10-31

End date

2019-12-31

Keywords

Finance & Microfinance

Additional Keywords

credit card, minimum repayment, anchoring

JEL code(s)

D03 (Behavioural Microeconomics: underlying principles); D14 (Household Saving; Personal Finance); D31 (Personal Income, Wealth, and Their Distributions); G02 (Behavioral Finance: Underlying Principles)

Secondary IDs

Abstract

The aim of the study is to test whether changes to the choice architecture of credit card repayment platforms can encourage customers who have persistently been making very low repayments to increase their average credit card repayments, pay off outstanding balances more quickly, and reduce the interest charges they incur. Specifically, we assess whether adding a "Pay 5%" option to the digital platform provided by a leading bank increases average credit card repayments among these customers. We also assess the effect of removing the "Pay Minimum (2%)" option from the menu provided in the platform. In the first case, the intervention is designed to provide an alternative anchor for customers who cannot pay their full balance, but could pay more than the minimum due. In the second case, the intervention is designed to remove the low anchor altogether and allow customers to make a choice with reference only to the high anchor (the full balance).

External Link(s)

Registration Citation

Citation

Buell, Ryan and Michael Hiscox. 2018. "Anchors Away! A Large-Scale Trial Testing Approaches to Reducing Credit Card Debt." AEA RCT Registry. December 21. https://doi.org/10.1257/rct.3414-1.0

Former Citation

Buell, Ryan and Michael Hiscox. 2018. "Anchors Away! A Large-Scale Trial Testing Approaches to Reducing Credit Card Debt." AEA RCT Registry. December 21. https://www.socialscienceregistry.org/trials/3414/history/39500

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Customers using the banks smartphone app to make payments on their credit card currently see a choice menu with the following options:Pay closing, Pay minimum, Pay other amount (entered manually). The interventions provide customers with two alternative choice menus:
1. Pay closing, Pay 5%, Pay minimum, Pay other amount
2. Pay closing, Pay other amount

Intervention Start Date

2018-10-31

Intervention End Date

2019-02-28

Primary Outcomes

Primary Outcomes (end points)

Aggregated monthly dollar repayment ($)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

• Aggregated monthly repayment as a percentage of monthly balance (%)
• Dollar repayment at transaction level ($)
• Dollar repayment as a percentage of balance at transaction level (%)
• Monthly fees and interest paid ($)
• Whether or not customers pay more than minimum (binary variable, monthly)
• Number of “persistent debt” customers (monthly)
• Whether or not account falls in arrears (binary variable, monthly)

Secondary Outcomes (explanation)

Experimental Design

Eligible customers will be randomly allocated to view either the current repayment choice menu or one the two new payment choice menus (the interventions) in their smartphone app when they receive the notification that their monthly payment is due.

Experimental Design Details

Randomization Method

Eligible customers will be randomly assigned by computer algorithm to one of the three experimental groups after blocking on the following baseline covariates: segment (e.g. Persistent Debt, Revolver, All Others), age, product tenure, May 2018 payment, average balance Mar-May 2018, credit limit

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

N/A

Sample size: planned number of observations

The total potential sample size is 1,270,481. There will be 74,967 customers in treatment 1, 75,011 customers in treatment 2, and 1,120,503 customers in the control. The intervention will only be seen by customers who choose to pay their credit card via the “Account details” page or the “Statements” page of the bank’s app. This requires the customer to have a transaction account with the bank, in addition to their credit card. Over the previous 12 months approximately 10% of all credit card payments done via the app were done through this channel. We thus expect an effective sample size of approximately 127,000.

Sample size (or number of clusters) by treatment arms

Based on the above we expect effect sample sizes of approximately 7,500 customers in each treatment group and 112,000 in the control group.

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

Taking into account pre-randomization blocking on key baseline covariates (listed above), the trial is powered to detect a 1% increase payments for the whole sample. For key subgroups (e.g., customers classified in the segments of "persistent debtors" and "revolvers") the trial is powered to detect a 10% increase in payments.

Supporting Documents and Materials

IRB