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What could possibly go wrong? Predictable Misallocation in Simple Debt Repayment Experiments
Last registered on September 08, 2020


Trial Information
General Information
What could possibly go wrong? Predictable Misallocation in Simple Debt Repayment Experiments
Initial registration date
September 05, 2020
Last updated
September 08, 2020 9:36 AM EDT
Primary Investigator
Justus-Liebig-Universität Giessen
Other Primary Investigator(s)
PI Affiliation
Justus-Liebig-University Giessen
PI Affiliation
Justus-Liebig-University Giessen
Additional Trial Information
Start date
End date
Secondary IDs
How do people repay debt? In a simple debt repayment experiment we provide subjects with two credit cards with different interest rates and levels of debt that are to be repaid. From a rational choice perspective, this is arguably one of the simplest financial decisions. Nevertheless, we observe severe deviations from optimal, i.e. debt minimizing, repayment decisions with one particularly persistent type of misallocation that has not been found before. In consecutive experiments we show that this and further fallacies are predictable so that behavior can be steered towards more efficient repayment decisions.
External Link(s)
Registration Citation
Bannier, Christina, Florian Gärtner and Darwin Semmler. 2020. "What could possibly go wrong? Predictable Misallocation in Simple Debt Repayment Experiments." AEA RCT Registry. September 08. https://doi.org/10.1257/rct.6400-1.0.
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Experimental Details
We test in an online and lab context wether people repay debts optimally, i.e. in an interest-minimizing way. We also try to predict patterns in deviations from optimal behavior, and try to increase optimal behavior.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Misallocation of income
Primary Outcomes (explanation)
Misallocation is defined as the fraction of income not used to repay the high interest card.
Secondary Outcomes
Secondary Outcomes (end points)
To test if financial literacy leads to more optimal behavior
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We use an incentivized debt repayment experiment to test how our participants use income to repay debts. We endow our subjects with two virtual credit card accounts, which both have 2200$ virtual debts and charge 3% or 5% interest rate per round, respectively. In each of the ten rounds subjects get a virtual income of 250$ that they can use to repay these debts. We meassure how much money is NOT used to repay the 5% card.
In experiment #1, we test if the basic paradigm works on MTurk and in the lab.
In experiment #2, we test if we can use the way we present information to increase or decrease optimal (=interest minimizing) behavior. Same experimental design as described, but some variation in the way we present information.
In experiment #3, we test if we can predict seven non-optimal repayment strategies, using a modified one shot game version of our experiment. We design seven pairs of scenarios ("scenario" being defined as a single combination of starting debts, interest rates and income) in which we either try to provoke a certain repayment heuristic, or suppress it. Every scenario pair differs in exactly one value, except for one, where two values differ.
Experimental Design Details
Randomization Method
In experiment #1, we replicate MTurk results in the lab to show that both subject pools behave comperable. Randomizing here is not necessary.
In experiment #2, we post sessions of all three treatments on MTurk at the same time using the same wording to advertize them. Technically this is self selection which could be seen as participants self-selecting themselves into a lab experiment where treatment 1 is conducted in one session and treatment 2 in another. Since participants cannot differentiate the treatments, there is no variable to self select for.
Experiment #3 has only one treatment.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Overall planned: 820
Sample size: planned number of observations
Overall planned: 820
Sample size (or number of clusters) by treatment arms
Experiment #1: 130 on MTurk, 100 in the lab
Experiment #2: 130 per treatment. Experiment #1 is a replication of the control group of experiment #2, we do not double count th MTurk treatment.
Experiment #3: only one treatment, 330.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
July 31, 2019, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
July 31, 2019, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Experiment #1: 131 on MTurk, 96 in the lab; Experiment #2: 131, 135 and 138 (Experiment #1 is a replication of the control group of Experiment #2, we do not double count the MTurk treatment); Experiment #3: only one treatment, 335.
Data Publication
Data Publication
Is public data available?

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Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)