Taxpayers' Tax and Financial Reporting Decisions in a Game Theoretical Model
Bibliographic record
Abstract
Abstract In a game theoretical model, this paper analyzes taxpayers' decisions on reporting financial income and taxable income to the tax authority, and the tax authority's strategic auditing. It extends the previous research by incorporating taxpayers' financial reporting decision into the model. It leads to three conclusions: (a) the tax authority is more likely to audit taxpayers reporting high accounting income but low taxable income than those reporting no accounting‐tax differences; (b) the tax authority is more likely to audit taxpayers reporting low taxable income and low accounting income if they have low financial reporting costs than if they have high such costs; and (c) the degree of connection between the true taxable income and the true accounting income affects taxpayers' reporting strategies. Résumé Dans un modèle théorique du jeu, le papier analyse les décisions des contribuables pour la déclaration de leur revenu financier et leur revenu imposable à l'administration fiscale et la stratégie de vérification comptable de l'administration fiscale. Il élargit les recherches précédentes en incorporant la décision de communication de l'information financière du contribuable dans le modèle. Cela conduit à trois conclusions: (a) l'administration fiscale est plus susceptible d'effectuer une vérification comptable des contribuables qui déclarent un revenu fiscal élevé mais un faible revenu imposable que ceux qui ne déclarent aucune différence imposable; (b) l'administration fiscale est plus susceptible d'effectuer une vérification comptable des contribuables qui déclarent un faible revenu imposable et un faible revenu fiscal s'ils déclarent de faibles coûts d'information financière que si ces coûts sont élevés; et (c) le rapport entre le revenu imposable réel et le revenu fiscal réel affecte les stratégies de déclaration de revenus des contribuables.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".