Work
in
An Agent-Based Tax Evasion Model
Calibrated
Using Survey Data Progress
Attila Szabó1,2, László Gulyás1,2, István János Tóth3
1AITIA International
Inc,
Eötvös University, Budapest
3Institute of Economics , Hungarian Academy of Sciences
2Loránd
Shadow 2011, Münster, Germany
Shadow 2011, Münster, Germany
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Acknowledgements
The partial support of the following grants of
the Hungarian Government are gratefully
acknowledged.
KMOP-1.1.2-08/1-2008-0002
OTKA T62455
OTKA K48891
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Outline
1. The TAXSIM Model
2. Earlier Results
3. Survey-Driven Calibration (work in progress)
4. Results
5. Conclusions and Future Work
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The TAXSIM Model
(since 2007)
-“All models are wrong; some models are useful.”
Box
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Previous models
• The focus is on the taxpayers' strategy:
Publications: Balsa et. al., Bloomquist, Davis et. al.,
Korobow et. al., Mittone and Patelli
• TAXSIM makes a utilitarian approach: misreporting
is a cost optimization strategy
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Actors of TAXSIM
Employee (taxpayer)
Try to find a job according to its preference
Employer (taxpayer)
Try to find employees according to its preference
Tax authority
Audits actors using a certain probabilistic strategy
Government ('society feedback')
Provides a level of contentment (services & satisfaction)
Market sector (a single sector economy)
Buys cheapest products first
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Model Architecture
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TAXSIM Decision Making
Information
from social
networks
Audits,
Governmental
services
Taxpayer strategy
Decision
Model of the
market
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Refinement of the Environment
The taxpayers make a decision on …
...what they actually can!
Taxpayer strategy respects the viable options (the
environment may override the strategy)
Employers and employees
different strategies for different goals
different motivation factors (government agent, model of the
market sector)
model of the labour market (how employers and employees
cooperate)
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TAXSIM is...
•
… a simulator that aggregates
Law enforcement
Motivation towards compliance
Taxpayers' goals
•
… rather a family of models than a simple model
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Earlier Results
-“The only thing new in this world is the history that you don't know”
(Harry S. Truman)
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First Set of Results
• Starting from a ‘pessimistic’ sector,
we investigated three scenarios:
• Improving governmental services
(taxpayers more often override optimal decisions)
• Voluntary shift to total legalization
(one company, cca. 15% market share)
• Preferential taxes for companies
(can afford higher wages at legal employment type)
200
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Employed
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80
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40
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80
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0
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0
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Employed
Legal employments
Mixed employments
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Employed
Employed
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Employed
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Legal employments
500
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2000
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3000
800
Tick
Mixed employments
900
3500
4000
4500
5000
5500
6000
Tick
Employed
0
1500
Hidden employments
1000
1100
Hidden employments
1200
Legal employments
Mixed employments
Hidden employments
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Employed
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Legal employments
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Tick
Mixed employments
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5500
Hidden employments
6000
Studied the effect of
social network topologies
• Two-dimensional grid
spatial/geographical
• Erdős-Rényi random graph (used in original studies)
small world
• Watts-Strogatz network
spatial/geographical
small world
clustered
• Real topologies depends on the modeled sector
(construction, telco, etc.)
Compliance depends on
social network topology
• Using random graph or Watts-Strogatz network
results converge to the same equilibrium, but
W-S is faster
• Using a two dimensional grid agents don’t reach
the optimal solution
Survey-Driven „Calibration”
-”To find out what happens when you change something, it is necessary to change it”
(anonymous)
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Motivation
• The Institute of Economics, Hungarian Academy
of Sciences carried out a survey among
Hungarian tax payers
• We wanted to see how much TAXSIM can be
made compatible with empirical findings.
• Also, we wanted to make the behavior of the
Tax Authority more realistic (i.e., adaptive vs
fully random)
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Selected Findings of the Survey
• Survey data shows that Hungarian tax payers
estimate the probability of discovered tax
avoidance to be 64%
• Real audit probability is 3%
• 73% of the respondents claimed that would
comply even if audit probability was 0%
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Adaptive Tax Authority
• The Hungarian Tax Authority assigns tax payers
into three categories for audits:
• Not audited yet
• Audited and complying
• Audited, non-complying
(Further, sector and income specific categories are not applicable)
• In TAXSIM the adaptive TA selects
• the neighbors (in the social net) of
the audited, non-complying agents.
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Survey-Driven Expectations
1. Citizens overestimate audit probability
2. There is about 73% of agents who would fully comply
(pay taxes) even with an audit probability of 0%
3. Adaptive audit strategy increases compliance
4. Atomized society would yield a larger shadow economy
(i.e., the social network is important)
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Results
-”The best time to plan an experiment is after you have done it.”
R. A. Fisher
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Citizens Overestimate the
Probability of Audits
• Not observed
• Agents respond to the acts of discovery by the Tax
Authority (on them or on agents in their network).
However, there is no adaptation act responding to
succesful non-compliance.
• Agents learn the audit probability almost perfectly.
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High Compliance in Face of
No Audits
• Not observed
• Agents are cost optimizers. This is a strong
motivation for non-compliance.
• The two opposite motivators are audits and
satisfaction with governmental services.
• The latter was shown earlier to be enough to
improve compliance
• Remark: the expectation was never observed
(but claimed by respondents)
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Adaptive Audit Strategy
Improves Compliance
• Opposite results: adaptive Tax Authority
yields higher level of non-compliance.
• The simulated society turns illegal much
faster.
• Agents continue to see non-compliance as a cost
optimization tool.
• Audits and fines are the cost of this tool.
• Adaptivity increases the efficiency of the Tax
Authority, but this only increases the costs of
„production”. Those who are not „illegal
enough” cannot afford this and go bankrupt.
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Adaptive Audit Strategy
Improves Compliance
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Adaptive Audit Strategy
Improves Compliance
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Social Networks Matter
• Confirmed
• Less information, more non-compliance
(remember, cost optimization is the main driver!)
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Conclusions and Future Work
-”Perfection is not possible; it's always approximation”
(anonymous)
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Conclusions and Future Work
• Only 1 in 4 expectations confirmed
• Overestimation could perhaps be achieved if we let
the agents develop „false confidence”
• Clearly, cost optimization is a very strong driver in
this model
• Yet, we intend to find the „turning point”: i.e., the level of
governmental services that would make agents comply even
without audits
• The adaptive strategy made the Tax Authority more
efficient, but fines were kept fixed
• Thus, it was possible to „price in” the fines. Further
parameter studies are warranted.
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Thank You!
http://taxsim.mass.aitia.ai
[email protected]
[email protected]
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An Agent-Based Tax Evasion Model Calibrated Using Survey Data