Selfish Sharing? The Impact of the Sharing Economy on Tax Reporting Honesty
Bibliographic record
Abstract
In the last decade, advances in technology have significantly disrupted the way firms provide goods and services. At the forefront of this technological disruption is the sharing economy, where individuals earn income by providing services or sharing assets through peer-to-peer (P2P) platforms. With global revenues in the sharing economy projected to increase substantially in the next decade, income from this economy will continue to be an important source of tax revenues for governments around the world. However, sceptics argue that the sharing economy inherently lends itself to dishonest reporting of taxable income. We employ an online experiment, using 746 taxpayers, to observe whether the prosocial benefits often promoted by P2P platforms reduce honest reporting of taxable sharing economy income. Consistent with moral licensing theory, we find that earning income from a prosocial-oriented P2P platform liberates taxpayers to dishonestly report their sharing economy income, and this result is fully driven by taxpayers whose personal values are incongruent with values promoted by the P2P platform. Our paper contributes to the limited but growing research on the sharing economy and its implications for ethical decisions. It also adds to the moral licensing literature by identifying value congruency as an important moderator for moral licensing effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".