This measuring tax evasion offer much more accuracy

This article by James Alm (2012) examines the studies completed
by Michael Allington and Agmar Sandmo, said to be the founders of contemporary
analysis in the field of tax evasion. Allington and Sandmo began their research
in this area in 1972, since then exploration within the area has been limited.
Due to the limited research, this paper investigates how to describe many of
the issues revolving around tax evasion, such as defining tax evasion, measuring
tax evasion and how to manage the affects using what has been learned from the
empirical, theoretical and experimental studies.

The article opens by discussing the measurement of tax
evasion, and the difficulties that are involved in gathering reliable
information. The methods of measurement are then broken down into categories, which
fall either under Alm’s classification of “traditional” or “modern.” Within these
categories are subcategories such as “indirect”, “direct” and “model”
approaches which Alm discusses, as well as some alternative forms of measurement
used in the more modern approaches.

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Traditional approaches to measuring tax evasion offer
much more accuracy if they apply a “direct” approach, implying that they are
being upfront with taxpayers about the study of evasion and analysis of their
tax returns. Examples of direct approaches are the sample taken by the IRS with
the implementation of their “Taxpayer Compliance Measurement Program”, later
replaced by National Research Program, that audits approximately 50,000 tax
returns per year. This study used to analyze taxpayer data in a line-by-line manor
to estimate the difference between reported income and the estimated actual
income, whereas National Research Program only audit some returns line-by-line.
The other direct approaches are less precise than the approach taken by the IRS,
as they include surveyed taxpayers, asked to describe their tax evasion conduct.
Lastly the analysis of amnesty participants as their tax data was used to derive
a measure of evasion based on their income.

There are also several “indirect” approaches to measuring
tax evasion, these measures are derived by investigating the differences found
in tax return data. These measurements vary from computing differences between
two factors, to analyzing the currency market for traces of evasion, to measuring
electricity consumption and comparing it to economic activity. Some of these approaches
measure the variance between factors, such as reported income and national
income, income and expenditure, or the labor force market. Another “indirect”
approach is the currency market, where analyzing currency transactions gives an
estimate of the “shadow economy”, this estimate can also be connected to tax
evasion as a measurement. Additionally, there has been some analysis of electricity
consumption and how it can predict the level of economic activity which provides
some markers of tax evasion that can be found when looking at economic activity
estimates and real data.

A more limited approach is the “model” approach, as its
characteristics are, it assumes multiple factors will cause shadow economy
activity, and the multiple effects it will have in the long run. Essentially this
approach examines several causes and indicators unlike “traditional” or “modern”
approaches as well as several indicators. An example of this model is the “Dynamic
Multiple Indicators – Multiple Causes” model.


Over time the approaches used to estimate evasion have
adapted, many modern approaches today assume individual’s income is split into
two categories, reported and unreported income. This assumption allows modern
methods to simply consider tax returns and use them to approximate evasion. Other
modern approaches include methods that have conducted field studies, whereas
others have used additional economic data as indicators for evasion such as consumption
and tax deductions. Similar to the traditional approaches, there is some survey
based data which considers more particular aspects of an individual’s spending
and working habits. The common factor of these measures of evasion is that none
of them include a direct measure of evasion.

Modern approaches also include some concepts that
could be considered a little be out there, for example the concept of measuring
the luminosity of the earth from outer space to approximate economic activity
and compared it to income seems like a bit of a stretch. Other interesting
measures of evasion include cigarette tax evasion.

Theoretical explanations
of evasion

Theoretical models of tax evasion all use the same
basic model, the economics-of-crime model. This model assumes that rational behavior
is to maximize utility of taking a risk, the risk in this scenario is lying or
falsely reporting income on a tax return at the risk of being caught. An adaptation
of this model is the “portfolio” model where the risk of evading taxes or a
portion of tax at the risk of being caught, which ultimately leads an individual
to pay taxes as the fear of being caught outweighs the possible monetary gain. The
“portfolio” model creates the perspective that individuals comply to taxation because
the risk of audit and or fines is too high. Despite the low audit rate this approach
still uncovers the concept that individual’s compliance is dependent on the
penalties involved. Meaning the overarching theme of this approach is that it
assumes that an individual pays taxes solely to avoid the consequences.

The avoidance of consequences is an interesting
phenomenon in this case, as audit rate is only roughly 1% of all returns, meaning
that the likelihood of being caught is very low or nearly zero. Additionally,
the penalties involved in evading taxes are generally mild, as most tax evaders
will simply have to pay the remaining unpaid balance, with penalties infrequently
being imposed. That being said, economic theory of rational behavior would
suggest that the gamble is worth the risk in this case, as the benefit to
taking the risk is available and if you are caught you pay what you would have
anyway. Therefore, underreporting income or overstating deductions would be a logical
choice as the proportion of people caught is very low. In spite of this, people
remain risk averse when it comes to tax.

The portfolio model is not without its caveats though,
as it tends to suggest individuals should report practically zero income to
give themselves the most possible benefit. This model would also predict that
increasing the tax rate would result in a positive effect on individuals
reported income. Despite the economic theory that supports individuals making
this decision, the compliance rate is in fact much higher than the predicted
level. Individuals compliance is consistently higher than predicted by
traditional economic theory, bringing to question the other attributes of compliance.
This model is reliant on behavioral economics to have the capacity to predict
an individual’s behavior, unfortunately according to the portfolio model,
individuals are not acting rationally.

Empirical explanations
of evasion

The difficulty with the empirical evidence of tax
evasion is the data, as there are no direct measures of evasion and using other
economic factors as a proxy is the most reliable method available. Despite the
difficulty in finding acceptable data there has been considerable research in
this field, as researchers have found that when a tax rate is too high, individuals
will be less likely to comply. These findings oppose that of the portfolio model,
but have some theoretical support as economic theory would agree that as tax
rates increase so does the appeal to evade taxes, as foregoing taxes would
provide benefit at a low risk.

explanations of evasion

Experimental methods for interpreting tax evasion incorporate
theory in a controlled experiment environment, which can produce more accurate data.
This allows econometric researches to use the data to estimate evasion and
other factors, that are not possible to generate in a natural setting, based on
the responses of individuals within the experiment. These factors are why experiments
may be more beneficial than theory and empirical data. Experiments follow the
same basic design, as individuals in a controlled environment are told to make
as much income as they possibly can. The experiment then precedes to go through
a few rounds, as individuals are given a choice on how much they want to report.
The experiment does include audits and risk of being caught for underreporting and
the information collected in then analyzed.

The results of these experiments have shown data
similar to the concepts found in both theoretical and empirical data, as higher
tax rates lead to less compliance by individuals while an increase audit rate
will lead to greater compliance. These experiments have also discovered that individuals
tend to exaggerate the prospect of an audit, as the probability of an
individual being audited is much lower than individuals assume.

Much like other experimental economics, these
experiments are not without faults, as many believe the experiments are not
representative of the population. Secondly a laboratory test is done in a controlled
environment where some of the factors implemented by the experimenter may not
be replicable in real world scenarios. There is also the risk of individuals behavior
being affected by the lab environment, as they may act differently since the
environment is controlled and so are the risks involved.

            Controlling Evasion

evasion can be done by using information gathered by theoretical, empirical,
and experimental models to implement new policy. Alm then argues that there are
“paradigms” that can affect evasion with the first being the “enforcement paradigm”.
This means enforcing more regular audit and harsh penalties it will minimize evasion.
The second paradigm is the “service paradigm” which considers tax authority
such as government to facilitate and provide services to taxpayers. The third and
final paradigm is the “trust paradigm” which suggests individuals will make
ethical, and moral choices to maintain social norms.

            To conclude, Alm discusses how this
area requires data from each category. Theoretical, empirical, and experimental
to best measure tax evasion. All the data possible is required to control the plethora
of behavior and motivation that affect it.