The Impact of Compensation Level and Context on Income Reporting Behavior in the Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines two methodological issues in judgment and decisionmaking studies in accounting—compensation level and context—using an income reporting task. Previous research has not examined the joint effect of compensation level and context. Further, findings in previous research about these two variables may not extend to specific contexts such as an income reporting context. Specifically, the study examines the effect of different levels of compensation (including zero and very high values) on participants' income reporting behavior in the laboratory. It also examines whether the use of tax-specific instructions results in differences in income reporting behavior compared to the use of context-free instructions. The study predicts that compensation level should not affect reporting income levels when the treatment is tax-specific due to the influence of social norms. The study also makes predictions based on expected utility theory in the context-fee treatment. An experimental study was carried out in India using college students that manipulated two types of context (tax-specific and context-free) and six levels of compensation, including no compensation, grouped into three levels: Low, Medium, and High. The results show that compensation levels did not affect participants' income reporting behavior in the taxspecific treatment but in the context-free treatment, participants' income reporting behavior was negatively affected by the introduction of adequate compensation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it