PhD thesis, University of Cincinnati, OH, USA.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: SUMMARY: Despite the magnitude of the tax evasion problem and the adverse effects thereof, the level of understanding of taxpayer behavior is surprisingly limited. Current compliance research generally uses either an experimental methodology or a regression analysis of aggregated (dated) Internal Revenue Service (IRS) audit data. This study addresses the question of whether the nonrandom element of human behavior could facilitate the detection of tax evasion. Unplanned Evasion (UPE) is defined to be blatant manipulation by the taxpayer of line items at the time of filing the return. Planned Evasion (PE), in contrast, is the result of planned actions to conceal an audit trail. The essential difference is that UPE requires that the taxpayer invent one or more numbers for the line item(s). Benford's Law (Benford 1938) is used, as an expected distribution for the digits in tabulated data, to detect UPE. The maintained hypothesis is that the digits of data that are truthfully reported, or are subject to PE, should conform to the expected frequencies. A mathematical model is developed, the result of which is a Distortion Factor, that quantifies the extent of UPE in a data set. Taxpayers on the 1985 and 1988 Individual Tax Model files (which contain unaudited tax return data) are partitioned into Low and High Income Groups. The empirical analysis, based on digital and number frequencies, indicates that the Low Income Groups practice UPE to a greater extent than the High Income Groups. Also, UPE as a form of human behavior is somewhat consistent over time. The study suggests that the IRS include in the formula used to select returns for audit, a variable that captures the extent of apparent estimation on a return. Furthermore, the Taxpayer Compliance Measurement Program should be restructured to an annual program to increase the predictive ability of the audit selection formula. The digit-based methodology could also be used to estimate the compliance level in aggregate, and for specific income and deduction fields, shortly after the returns for a particular year have been filed.
Bibtex:
@PhdThesis{
author = {Nigrini, M. J.},
title = {The detection of income tax evasion through an
analysis of digital distributions},
type = {Thesis ({Ph.D.})},
school = {Department of Accounting, University of Cincinnati},
address = {Cincinnati, OH, USA},
year = {1992},
}
Reference Type: Thesis
Subject Area(s): Accounting, General Interest