Where Has The Currency Gone? And Why? The Underground Economy And Personal Income Tax Evasion In The U.S., 1970-2008
Bibliographic record
Abstract
Unaccounted for currency in the U.S. is argued to reflect the presence of widespread income tax evasion. This empirical study seeks to identify determinants of the underground economy in the U.S. in the form of federal personal income tax evasion over the period 1970-2008. In this study, we use the most recent data available on personal income tax evasion, data that are derived from the General Currency Ratio Model and measured in the form of the ratio of unreported AGI (adjusted gross income) to reported AGI. Other studies of federal income tax evasion for the U.S. are dated and do not use data this current. It is found that personal income tax evasion was an increasing function of the maximum marginal federal personal income tax rate, the percentage of federal personal income tax returns characterized by itemized deductions, and unpopular military engagements, in this case, the War in Iraq, and a decreasing function of the Tax Reform Act of 1986 (during its first two years of being implemented), the ratio of the tax free interest rate yield on high grade municipals to the interest rate yield on ten year Treasury notes (as a measure of the incentive effect of a better return to tax avoidance, which is legal), and higher audit rates of filed federal income tax returns (as a measure of risk from tax evasion) by IRS personnel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".