Tax Compliance Research Methodologies
Measurement and comprehension of tax compliance have never been simple endeavors because
those who avoid or evade taxes have a strong desire to hide their actions (Alm & McKee, 2006).
Income taxes are the main topic of most studies on tax compliance. Historical information,
surveys, and experiments make up the three main types of tax compliance research methods
(Slemrod, 1992).
A. Data from the Past
The IRS's Taxpayer Compliance Measurement Program (TCMP), which collected a stratified
random sample of 50,000 individual tax returns every three years for thorough line-by-line
audits, is the primary source of historical data on tax compliance in the United States. In order to
compare reported income to actual income, TCMP data is used to determine taxpayers' genuine
income. Data from the TCMP were used to offer information on a variety of aspects of tax
compliance, including income source, socioeconomic class (age, sex, location), likelihood of
detection, marginal tax rate, and income level. Surprisingly, according to the TCMP statistics,
the severity of the fines is not a significant effect, which may be because they are not always
executed (Franzoni, 2008).
However, there are several drawbacks to using TCMP data as a gauge of tax compliance. First,
since noncompliance that is not discovered by IRS audits is not included in TCMP data, it was
unable to capture all instances of noncompliance. In other words, only those who file taxes are
included in the TCMP statistics. For instance, it was estimated that non-filers made up 36% of all
undeclared income in 1976. Additionally, the IRS has a limited ability to identify tax evasion by
the unorganized sector, the self-employed, those who moonlight, and businesses that only accept
cash (J. Alm, Deskins, & McKee, 2009; Franzoni, 2008). Second, TCMP data failed to identify
sincere misreports. In contrast, it was difficult to discern between unintentional and intentional
non-compliance, which provides no guidance for developing methods to increase compliance.
Third, TCMP data contains little demographic information, no data on the attitudes of taxpayers,
and no data on additional variables that might have an impact on tax compliance (Slemrod,
1992).
Another source of historical data that can be used to quantify noncompliance is state amnesty
statistics. Amnesty data, however, shares the same drawbacks as TCMP data in that it is
restricted to those who took advantage of the tax amnesty and might not accurately reflect the
general population (Andreoni, et al., 1998).
B. Survey Results
By gathering information primarily on attitudes that affect taxpayers' compliance decisions,
survey data is used to address some limitations of historical data. In contrast to historical data,
survey methods are very helpful in examining perceptions and evaluating the factors that
influence tax compliance, such as sociological factors, procedural fairness, audit rates, fines, tax
rates, and peer pressure. The following factors, according to Franzoni (2008), were significant
determinants: 1) perceived probability of detection, 2) severity of informal sanctions, 3) moral
beliefs about tax compliance, 4) experience with other non-compliants and past experience with
IRS enforcement, and 5) demographic traits.
Survey data still has a number of issues. The accuracy of survey data is first criticized as being
unsure. It's possible that respondents will not accurately recall their reporting choices or will not
be completely honest. People might want to maintain their reputations and sporadically justify
their own actions. Respondents might therefore be reluctant to acknowledge or report their non-
compliance actions (Alm, 1999; Elffers, Weigel, & Hessing, 1987; Franzoni, 2008). Second, it
might be difficult to determine the causal direction of the connection between determinants and
noncompliance (Alm, 1999; Franzoni, 2008). Third, it can be challenging to ensure the reliability
of survey results because it depends on the sample's representativeness (Franzoni, 2008).
B. Research C.
Another approach for researching tax compliance is controlled experimentation in a lab setting.
In order to predict whether taxpayers will decide to comply or not, i.e., accurately report or
underreport given specific audit rates, penalties, rewards, etc., this method is used to simulate
situations that are as close to the real ones as possible. According to experiment findings (such as
those in Alm & McKee (2006), Baldry (1987), and Webley, Robben, Elffers, & Hessing (2010),
audit rates are a significant factor in compliance decisions. Additionally, higher compliance and
lower noncompliance are related to higher income and lower tax rates. However, unless the audit
rate is extremely high, the size of the fine is not particularly important (Franzoni, 2008).
Additionally, social expectations and moral behavior appear to have a big impact on tax
compliance (Baldry, 1987).
The experimental method's drawback is that it does not produce real compliance data.
Additionally, unlike survey methods, experimental studies could not be performed for a large
sample. Most experiments are conducted in a small group of people, typically students (Franzoni,
2008). Taxpayers won't always choose the same course of action in the real world.
These three techniques work best together when studying tax compliance. Data from the past
offers accurate and trustworthy information on tax compliance. We learn more about attitudes
and perceptions that influence tax compliance decisions from surveys. We can test potential
audit, punishment, and reward systems through experiments.
