Six data points on what people do when they think no one is checking, from a wallet experiment that defied basic economics to a tax gap that shows exactly who gets away with it, including an honest look at where this research field didn't check its own work closely enough
The Honesty and Oversight Dataset & Dashboard, DT Linux
The Honesty and Oversight Dataset & Dashboard
An interactive companion to DT Linux's briefing on what happens to honesty when no one is watching: a wallet experiment that defied basic economics, a tax gap that shows exactly who gets away with it, and an honest look at where this research field didn't check its own data closely enough.
Filter by case
Three kinds of unwatched moments
How each case compares
What's in this dataset, by composition
How organizational fraud actually gets caught
A genuine part-to-whole composition: of every occupational fraud case in the latest global survey, this is how the case first came to light. This is the one place in this dataset where the numbers are shares of a single total; every other comparison here is an independent-category comparison and stays a bar chart on purpose.
Dataset schema
| Field | Description | Example |
|---|---|---|
| case_name | The experiment, effect, or gap this row describes | "The unproctored spike" |
| tier | This report's own categorization of the kind of unwatched moment, not a severity ranking | "The reckoning" |
| key_metric | The headline sourced figure for this case | "Cheating roughly doubled once proctors disappeared" |
| source | Citation for the key metric | "Journal of Academic Ethics systematic review, 2023" |
| last_verified | Date the figure was last checked against its original source | "2026-09-06" |
About this dataset
How this data was collected
Every figure behind this dataset comes from a peer-reviewed field experiment, a registered multi-lab replication, government audit data, a systematic review, or a named industry survey: Cohn and colleagues' 2019 Science wallet experiment, the 2018 registered replication of the original fudge-factor study, a 2019 meta-analysis of the watching-eyes effect, a 2021 study of 45 million online comments, IRS tax gap projections for tax year 2021, a 2023 systematic review of pandemic-era exam cheating, and the Association of Certified Fraud Examiners' latest occupational fraud survey. DT Linux's research process cross-references each figure against its original publication, including the two places where this literature's own findings did not hold up to later scrutiny; this report does not involve DT Linux running its own behavioral experiments.
Data handling and compliance
Built entirely from published academic research, government audit data, and a named industry survey, never from personal or proprietary data collected by DT Linux. Full practices in our privacy policy.
API and pipeline access
Want a compliance risk or behavioral research analysis like this refreshed on a schedule, or piped into your own systems? DT Linux builds custom research and data pipeline work per client; there is no self-serve API today, so delivery is handled directly by our team. Get in touch.
More from the DT Linux data store
Want this kind of behavioral or compliance research built for your own organization?
DT Linux designs custom compliance risk assessments, survey design, and behavioral research for organizations that need to understand where oversight actually matters, not just where it's assumed to. Get in touch if you need deeper or more customized research behind this report.