Claiming Unclaimed Benefits: Testing the Effects of Digital Interventions to Help Low-Income Customers Claim a Government Benefit
Last registered on June 18, 2018

Pre-Trial

Trial Information
General Information
Title
Claiming Unclaimed Benefits: Testing the Effects of Digital Interventions to Help Low-Income Customers Claim a Government Benefit
RCT ID
AEARCTR-0003083
Initial registration date
June 13, 2018
Last updated
June 18, 2018 12:00 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Harvard University
Other Primary Investigator(s)
PI Affiliation
MIT
Additional Trial Information
Status
On going
Start date
2018-05-04
End date
2019-04-15
Secondary IDs
Abstract
Why do citizens in need leave money on the table by failing to claim valuable government benefits designed to assist them? Past research has indicated that low take-up rates for benefits may be due to a lack of awareness combined with the real or perceived complexity (and cognitive costs) associated with the process of claiming benefits. We test these explanations in a large-scale field experiment involving a sample of 250,000 individuals potentially eligible for a substantial government benefit they have not yet claimed. Using a digital application, we will notify a randomly selected group of individuals that they may be eligible for a government benefit they have not yet claimed, and then provide a sub-sample of those who respond with a simplified approach to claiming the benefit. We will assess the impact of the the notification and simplification interventions on rates of claiming.
External Link(s)
Registration Citation
Citation
Bergman, Olivia and Michael Hiscox. 2018. "Claiming Unclaimed Benefits: Testing the Effects of Digital Interventions to Help Low-Income Customers Claim a Government Benefit." AEA RCT Registry. June 18. https://www.socialscienceregistry.org/trials/3083/history/30864
Experimental Details
Interventions
Intervention(s)
In the first stage, subjects are assigned to a control group or one of 4 different treatment groups that will receive 4 different types of messages delivered to subjects via a smartphone application, notifying them about the government benefit for which they may be eligible.

In the second stage, individuals responding to a message about the benefit are assigned to the government’s existing webpage describing how to claim the benefit or to a simplified set of online instructions about how to claim the benefit.

Intervention Start Date
2018-05-04
Intervention End Date
2018-06-19
Primary Outcomes
Primary Outcomes (end points)
Percentage of individuals who claim the benefit; percentage of individuals who click to get more information about the benefit.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The purpose of the study is to test ways to connect eligible citizens with unclaimed government benefits.

The first step of our experiment aims to test the effectiveness of 4 different types of messages to understand the best way to convey information about the existence of the benefit and generate initial interest. Our total target population is 250,000 individuals who we have assessed as being likely to be eligible for the benefit. Each of the 4 notification messages will be sent to a randomly selected group of 50,000 individuals; 50,000 individuals will be assigned to a control group.

The second step of our study aims to test the effect of simplification of the process of claiming the benefit. Individuals who click to get more information about the benefit in response to one of the notification messages will be taken to one of two randomly assigned landing pages. One landing page will provide simplified information about the benefit and enable click-to-call functionality so that people can immediately attempt to claim the benefit by phone. The other landing page will display information about the benefit in a standard form provided by the government, including instructions for how to claim the benefit, and a link to the government website.
Experimental Design Details
Randomization Method
Randomization by computer
Randomization Unit
Individuals
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
N/A
Sample size: planned number of observations
250,000
Sample size (or number of clusters) by treatment arms
Our total target sample is 250,000. We will send out an initial treatment message (experimental stage 1), to 200,000 individuals: 50,000 in each of four message treatment groups, with 50,000 individuals in the control (no message) group.
Stage 2 involves directing individuals who respond to a stage 1 message by clicking to see more information to one of two different landing pages describing how to claim the benefit in simplified or in standard form (so stage 2 has 8 treatment arms + control).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard University-Area Committee on the Use of Human Subjects
IRB Approval Date
2017-04-16
IRB Approval Number
IRB18-0546
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers