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Record W1755503448 · doi:10.15353/rea.v6i1.1411

Where Has The Currency Gone? And Why? The Underground Economy And Personal Income Tax Evasion In The U.S., 1970-2008

2014· article· en· W1755503448 on OpenAlexvenueno aff
Richard J. Cebula

Bibliographic record

VenueReview of Economic Analysis · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsState income taxEconomicsIndirect taxTax reformGross incomeAd valorem taxIncome taxValue-added taxDouble taxationTax rateMonetary economicsAdjusted gross incomeLabour economicsPublic economics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.254
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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