Whistleblowing and Competition

Last registered on December 14, 2023


Trial Information

General Information

Whistleblowing and Competition
Initial registration date
March 10, 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
March 13, 2023, 3:24 PM EDT

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

Last updated
December 14, 2023, 6:05 AM EST

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

University of Amsterdam & University of Birmingham

Other Primary Investigator(s)

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Whistleblowing, i.e. the act of reporting illegal actions of a manager by an employee, is an important tool to uncover corporate fraud. Previous experimental literature has studied firms independently of each other. However, competition between firms can be an important driver of corrupt behaviour. In an experiment, we use treatments with and without firm competition and conjecture that whistleblowing is less likely under competition. We additionally explore whether managers and employees form self-serving beliefs about the prevalence and acceptability of cheating and non-whistleblowing.
External Link(s)

Registration Citation

Ioannidis, Konstantinos. 2023. "Whistleblowing and Competition." AEA RCT Registry. December 14. https://doi.org/10.1257/rct.11051-2.1
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
(i) Share of employees choosing to "blow the whistle"
(ii) Share of managers choosing "break the law"
Primary Outcomes (explanation)
(i) Each of the two employees of a firm has an opportunity to blow the whistle on their manager. This decision is elicited with the strategy method (without knowing whether the manager actually broke the law), is implemented only if the manager actually broke the law, and implemented for only one of the two employees randomly selected.

(ii) Each manager has an opportunity to double the firm surplus by doing a difficult task or by breaking the law. Breaking the law causes loss of income to members of the public. The surplus is eventually split among firm members (50% on manager, 25% each employee).

Secondary Outcomes

Secondary Outcomes (end points)
(i) Belief about the frequency of whistleblowing (integer between 0 and 72)
(ii) Belief about the frequency of breaking the law (integer between 0 and 36)
(iii) Morality judgement of employee not blowing the whistle (Likert scale from very immoral to very moral)
(iv) Morality judgement of manager breaking the law (Likert scale from very immoral to very moral)
(v) Morality judgement of public losing money due to managers breaking the law (Likert scale from very immoral to very moral)
(vi) Employee loyalty to the firm (Likert scale from not loyal at all to very loyal)
Secondary Outcomes (explanation)
The secondary outcomes are elicited in a post-experiment survey together with demographics, risk preferences, and social value orientation.

Experimental Design

Experimental Design
General structure of the experiment

Each session will have 15 subjects. Three will randomly assigned to be managers of the three firms and keep their role throughout the session. The remaining subjects are randomly assigned in each round into six employees (two per firm) and six members of the public. They will play the whistleblowing game of Butler et al, 2020 repeatedly for 12 rounds. At the end of the experiment I elicit demographics (gender, age, field of study), incentivized beliefs and morality judgements described in the secondary outcomes above, risk preferences (Eckel & Grossman, 2002), and social value orientation (Murphy et al. 2011)


(i) baseline: payoffs of all firms are independent (same as Butler et al, 2020)
(ii) competition: the surpluses of firms are compared and the winner gets an additional bonus (multiplied by 1.5) while the other two firms get a reduction (multiplied by 0.75).

Alternative hypotheses for main treatment effect

H1: The frequency of whistleblowing is lower in competition.
H2: The frequency of breaking the law is higher in competition.

Analysis of main effects

As the key specification, I use regressions of the dependent variables (either whistleblowing or breaking the law) on treatment dummies, using each choice as an observation. I cluster standard errors on the matching group level. I will also estimate these models with subject controls (risk preferences, social preferences, gender, age). Robustness of main results will be checked using non-parametric tests (ranksum tests) with data averaged on the matching group level.

Analysis for exploratory effects of beliefs and morality judgements
We expect to see lower beliefs about the frequency of whistleblowing and higher about lawbreaking under competition as well as less harsh judgements for employees and managers who stay silent and break the law respectively. We also expect to observe higher loyalty to the firm under competition. All those exploratory hypotheses will be tested non-parametrically with ranksum tests.
Experimental Design Details
If a treatment effect is established, I will run a third treatment where the winning firm is randomly determined. We also run an exploratory treatment where the members of the public in the whistleblowing game receive passive income instead of doing the number adding task.
Randomization Method
Computerized randomization
Randomization Unit
Session level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
16 sessions of 15 participants
Sample size: planned number of observations
240 participants
Sample size (or number of clusters) by treatment arms
8 sessions per treatment (60 participants)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
To approximate power, I simulate 1,000 datasets, each consisting of two treatments (no-competition and competition). I use 0.2377 as an estimate for the share of whistleblowers in the no-competition treatment based on the results of Butler et al, 2020. I vary the share of whistleblowers in competition treatment between 0.10 and 0.20. I estimate the key specification (regressing whistleblowing on treatment, clustering on simulated matching groups). I code an estimate as significant if the p-value is below a significance level of 0.05 based on two-sided hypothesis. The result of the power analysis shows that we will have power exceeding 0.80 to pick up a reduction in the whistleblowing share by at least 0.06; so if whistleblowing under competition is less than 0.17. We note that whistleblowing is the main outcome of the experiment, so the power analysis is based on testing H1.

Institutional Review Boards (IRBs)

IRB Name
University of Amsterdam
IRB Approval Date
IRB Approval Number
IRB Name
University of Birmingham
IRB Approval Date
IRB Approval Number
ERN_0894 -3


Post Trial Information

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Is the intervention completed?
Intervention Completion Date
June 01, 2023, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
May 31, 2023, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
28 sessions of 15 participants
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
420 participants
Final Sample Size (or Number of Clusters) by Treatment Arms
Primary treatments: 8 sessions per treatment (120 participants) Additional treatments: 4 sessions per treatment (60 participants)
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials