Taxpayers' Prepayment Positions and Tax Return Preparation Fees*
Bibliographic record
Abstract
Abstract Individuals who have their tax returns professionally prepared often overpay estimated income taxes, effectively giving the government an interest‐free loan. To understand why tax professionals may place their clients in positive prepayment positions, we draw on mental accounting theory. Mental accounting theory suggests that by placing taxpayers in positive prepayment positions, tax professionals induce a favorable mental representation of tax return preparation fees, perhaps allowing them to collect larger fractions of billable time and costs incurred on taxpayers' behalves. Thus, we hypothesize that tax return preparation fees are higher for taxpayers in positive prepayment positions than for taxpayers in negative prepayment positions. Regression results using tax return data for 68,736 taxpayers provide strong support for this hypothesis. To more fully understand the general nature of the relationship between taxpayers' prepayment positions and tax return preparation fees, we adapt the prospect theory value function to the tax domain and formulate three additional hypotheses. Consistent with theory, regression results indicate that the relation between taxpayers' prepayment positions and tax return preparation fees is (1) positive, (2) stronger for taxpayers who receive refunds that are less than fees than it is for taxpayers who receive refunds that are greater than fees, and (3) stronger for taxpayers in negative prepayment positions than for taxpayers in positive prepayment positions.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".