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Budgeting, Feedback, and Spending Decisions
Last registered on December 17, 2016

Pre-Trial

Trial Information
General Information
Title
Budgeting, Feedback, and Spending Decisions
RCT ID
AEARCTR-0000889
Initial registration date
September 29, 2015
Last updated
December 17, 2016 10:19 PM EST
Location(s)
Region
Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
Additional Trial Information
Status
Withdrawn
Start date
2015-10-06
End date
2016-10-31
Secondary IDs
Abstract
Working with the developers of a mobile phone app that helps individuals track their spending, we study whether giving users regular feedback (via push notifications) on their progress relative to their budgets influences spending on bank cards and credit cards. Users are randomly assigned to receive progress updates relative to narrow budget goals or relative to broad budget goals. A third group is randomly assigned not to receive progress updates.
External Link(s)
Registration Citation
Citation
Beshears, John and Talia Gillis. 2016. "Budgeting, Feedback, and Spending Decisions." AEA RCT Registry. December 17. https://doi.org/10.1257/rct.889-4.0.
Former Citation
Beshears, John, John Beshears and Talia Gillis. 2016. "Budgeting, Feedback, and Spending Decisions." AEA RCT Registry. December 17. http://www.socialscienceregistry.org/trials/889/history/12550.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2015-10-06
Intervention End Date
2015-10-31
Primary Outcomes
Primary Outcomes (end points)
Our key outcome variable will be bank card and credit card spending during October 2015 in the key category (shopping, bars/restaurants, or supermarket) with the largest budget. Because the distribution of this variable will be highly skewed, we will focus on the logarithm of the variable and also plan to winsorize it (at the 99th percentile of its distribution, unless the distribution is so extreme that the 95th percentile is more appropriate). In order to increase statistical precision, we also plan to look at changes in the variable relative to September 2015 (it may not be possible, however, to obtain September 2015 data).

As secondary analyses, we plan to conduct quantile regressions to study differences in the full distribution of the key outcome variable across treatment groups. We also plan to examine secondary outcome variables such as an indicator for whether or not the goal was achieved and actual spending as a proportion of budgeted spending.

Finally, tertiary analyses will examine overall spending (as opposed to spending only within the key category with the largest budget).
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experimental design includes three groups:
1) Broad budget goal - users receive push notifications providing feedback about spending relative to a broad budget goal
2) Narrow budget goal - users receive push notifications providing feedback about spending relative to a narrow budget goal
3) Control - users do not receive push notifications providing feedback on spending relative to budget goals
Experimental Design Details
The experimental design includes three groups:
1) Monthly treatment group (~4000 users) - in the Monthly treatment group will receive information about their spending in the key category since the beginning of the month and will be reminded of their monthly spending goal
2) Weekly treatment group (~4000 users) - users in the Weekly treatment group will receive information about their spending in the key category since the beginning of the week and will be reminded of their weekly spending goal
3) Control group (~1000 users) - users do not receive push notifications providing feedback on spending relative to budget goals
Randomization Method
Randomization is done in an office by a computer.
Randomization Unit
The unit of randomization is the individual user.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The sample will include approximately 9000 users (randomization is at the user level).
Sample size: planned number of observations
The sample will include approximately 9000 users (randomization is at the user level).
Sample size (or number of clusters) by treatment arms
~4000 in each of two treatment groups and ~1000 in control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We do not have detailed data with which to perform precise power calculations, but we anticipate that the standard deviation of log winsorized spending in category in the month (in Brazilian reais) will be about about 1. In a comparison of the two treatment groups (~4000 users each), we have ~77% power with alpha=0.05 to detect a change of 0.06 in the variable (a 6% spending change). In a comparison of one of the treatment groups (~4000 users) to the control group (~1000 users), we have ~81% power with alpha=0.05 to detect a change of 0.1 in the variable (a 10% spending change).
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard University-Area Committee on the Use of Human Subjects
IRB Approval Date
2015-05-05
IRB Approval Number
IRB15-1305
Post-Trial
Post Trial Information
Study Withdrawal

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Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS