Digital Money, Opt-Out Options and Bank Runs

Last registered on July 10, 2023


Trial Information

General Information

Digital Money, Opt-Out Options and Bank Runs
Initial registration date
June 28, 2023

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
July 10, 2023, 4:55 PM EDT

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



Primary Investigator

Technical University Munich

Other Primary Investigator(s)

PI Affiliation
Technical University of Munich

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
In an online experiment, we study the influence of a prior opt-out option and digital money on the prevalence of bank runs. Subjects in the experiment make two main decisions. First, they decide whether or not to enter a risky investment game. Subjects who enter the investment game then decide whether to continue investing their money or withdraw it. Continued investment leads to higher payoffs than withdrawal if and only if all subjects in a group continue to invest. Between treatments we vary whether payouts happen in the form of cash, digital money or both. We also have additional treatments that dispense of the first decision i.e. where subjects do not have an opt-out decision and enter the investment game automatically. We hypothesise three main effects: Firstly, we expect less continued investment (i.e. more bank runs) in treatments where subjects have no prior opt-out option. Secondly, we expect a higher number of bank runs in treatments where digital money is available due to a higher perceived risk of withdrawal of digital money. Thirdly, we expect an interaction effect between the availability of an opt-out option and the existence of digital money, such that when there is no opt-out option and digital money exists bank runs are particularly prevalent.
External Link(s)

Registration Citation

Gschnaidtner, Christoph and Gabriel Vollert. 2023. "Digital Money, Opt-Out Options and Bank Runs." AEA RCT Registry. July 10.
Experimental Details


We study the prevalence of bank runs in an online experiment. Specifically, we focus on two factors, the availability of a prior opt-out option and the availability of digital money, that possibly influence whether or not a bank run occurs. We measure the frequency of bank runs as the fraction of subjects withdrawing their endowments in an investment game. We hypothesise that the availability of an opt-opt out option decreases bank runs while the availability of digital money increases bank runs due to higher perceived liquidity. We also investigate interaction effects between opt-out option existence and digital money availability. We implement digital money with payouts via Paypal or a bank transfer.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Investment percentage: Fraction of individuals who chose "continue to invest" out of all individuals that entered the investment game.
Primary Outcomes (explanation)
We calculate our measure ("investment percentage") separately for all treatments. Our main hypotheses pertain the distribution of this measure across treatments.

Secondary Outcomes

Secondary Outcomes (end points)
Pre-choice belief: Belief about what fraction of subjects choose "continue to invest" rather than "withdraw", non-incentivised
Post-choice belief: Same as Pre-choice belief, but elicited after choice between "continue to invest" and "withdraw, incentivised
risk_aversion_score_1: Risk aversion score from balloon inflation task
risk_aversion_score_2: Risk aversion score from multiple price list choice
risk_aversion_score_self_assessed: Risk aversion self-assessment (scale from 0 to 10)
time_preference_self_assessed: Impatience self-assessment (scale from 0 to 10)
crt_score: number of correctly answered questions from the cognitive reflection test (max score: 3)
math_score: fraction of achieved points in maths A-level exam out of all achievable points

enter_investment_game: Fraction of subjects that entered the investment game rather than storing their money immediately (only applicable in treatments where subjects could decide on this)
stored_as_cash: Fraction of individuals that stored their money as cash rather than digital money (only applicable in the one treatment where subjects can decide on entering the investment game and have options to store money as cash or digital money)

digital_money_knowledge_score: number of correctly answered questions from our digital money quiz (max score: 3)
digital_money_advantage: description of advantages of digital money
digital_money_disadvantage: description of disadvantages of digital money
bank_run_knowledge: binary variable indicating whether subject visited a lecture on bank runs, which is part of the Economics 2 lecture series at TUM.
digital_money_pay_freq: response to self-assessment question regarding how often subject pays with digital money
cash_pay_freq: response to self-assessment question regarding how often subject pays with cash

demographics: age, gender, country_of_birth, available_income, study_subject, highest_university_degree, current_semester
Secondary Outcomes (explanation)
We also measure the timing of several decisions (eg. how long subjects take to answer control or knowledge questions). We use this for two purposes:
1. To exclude subjects from the analysis, who do not meet certain minimal time requirements
2. To gain further insight into subjects' confidence of choices

Experimental Design

Experimental Design
Our experimental design consists of five stages: An investment game, two measures of risk preferences, a cognitive reflection test, a knowledge quiz about digital money and questions on demographic background. All but the demographics questions are incentivised. Every fifth student (randomly drawn after completion of the experiment) is paid out their earnings. Earnings range between 15 and 25 euros depending on the particular choices of the subjects. The random draw procedure and the expected earnings are announced at the start of the experiment. We implement our experiment on the online platform Qualtrics.

Details on the experimental design:
In the first stage, the investment game:
Subjects are endowed with 10 euros at the start of our experiment. They make two choices in an investment game:
(1) Opt-out option: They choose whether or not to enter the investment game. If they opt-out, they receive a safe payoff of 10 euros from this part of the experiment.
(2) Those who entered are matched into groups of five and choose whether to "continue to invest" their endowment or to "withdraw" their endowment. Choosing "continue to invest" is associated with high risk, since this choice leads to the highest payoff possible only in the case where all other subject in the group also chose "continue to invest" and leads to lower payoffs the more others decide to "withdraw". Choosing "withdraw" is associated with lower risk, as possible payoffs are more evenly distributed, even though they still positively depend on the number of other individuals that chose "continue to invest".
We also elicit subjects' beliefs about others' decision to "continue to invest" or "withdraw". Specifically, we ask subjects, before they actually make their continued investment decision, about what fraction of other subjects will choose "continue to invest". We also ask subjects a similar question after they had made their investment decision.

In the second stage, the risk preference elicitation:
Subjects play a standard balloon inflation task. Subjects also choose from a standard multiple price list of risky choices.

In the third stage, the cognitive reflection test:
Subjects answer three standard questions in a cognitive reflection test.

In the fourth stage, the knowledge quiz about digital money:
Subjects answer a couple of questions to test their knowledge about digital money and bank runs. We impose a time limit of 30 seconds per question. We chose the questions in such a way, that they cannot be answered with a quick (less than 30 seconds) google search.

In the fifth stage, the demographic background elicitation:
We ask students standard question regarding their demographic background in general and progress of their studies at TUM in particular.

We have six different treatments, which only vary with regards to the investment game (first stage of the experiment). In treatments 1-3, subjects make their choices as we described above. In treatments 4-6, we deviate from above description by making participation in the investment game mandatory. Comparing "continue to invest" decisions between treatments 1-3 and 4-6 then allows us to estimate the effect of a prior opt-out option from the investment game, since such an option only exists in treatments 1-3.
Treatments also differ in regard to how money that is earned in the experiment is paid out. In treatments 1 and 4 subjects can choose between a payment in cash or digital money (via Paypal or bank transfer), in treatments 2 and 5 earnings are paid in cash, in treatments 3 and 6 earnings are paid in digital money. Comparing these treatment groups thus allows us to estimate the effect of the existence of digital money on "continue to invest" decisions. We can also estimate an interaction effect between the existence of digital money and the existence of the opt-out option. Importantly, we communicate to subjects that any earnings have to be collected in person at TUM at a later date, once the experiment is completed. Thus, there are no real "convenience" differences between digital payout and cash payout i.e. the transaction costs are the same with both types of payments.
Experimental Design Details
Randomization Method
Online experiment on Qualtrics: Randomisation via computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Online experiment with Economics 2 students (one cohort) at Technical University of Munich
Sample size: planned number of observations
about 420 students
Sample size (or number of clusters) by treatment arms
about 70 students in each treatment (70*6=420)
definitely an equal distribution across treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


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