The Interrelationship between Estimated Tax Payments and Taxpayer Compliance
Bibliographic record
Abstract
This paper examines taxpayers' compliance behavior and the tax agency's audit decision in a broader, more realistic, setting. Whereas prior research has taken the taxpayer's prepayment position as exogenous, this study extends the literature by incorporating the estimated tax payment decision into a tax compliance game. A two-period game-theoretic model is used to examine the effect that the estimated tax payment rules have on taxpayers' incentives to evade and on the tax agency's audit strategy. Our primary results are as follows. First, in equilibrium taxpayers' estimated tax payment decision will depend upon the uncertainty about their true tax liability, and the cost from overpayment (the taxpayer's cost of capital) or underpayment (penalty interest) of installments of estimated tax. Second, under reasonable assumptions, high-type taxpayers who make higher installments of estimated tax are less likely to lie about their level of income than those who make lower installments—that is, taxpayers who pay low are more likely to evade. Third, the tax agency audits taxpayers who have made low reports and low estimated tax payments with a higher probability than those who have made high estimated tax payments. The gain to the tax agency from auditing taxpayers who make lower payments and evade arises not only from the penalties charged for evasion, but also from the interest charged on deficient installments of estimated tax.
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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 | 0.011 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".