An Agent-Based Tax Evasion Model Calibrated Using Survey Data
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An Agent-Based Tax Evasion Model Calibrated Using Survey Data
Attila Szabó1,2, László Gulyás1,2, István János Tóth3
1AITIA International Inc,2Loránd Eötvös University, Budapest
3Institute of Economics , Hungarian Academy of Sciences
Shadow 2011, Münster, Germany
1Shadow 2011, Münster, Germany
Work in Progress
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Shadow 2011, Münster, Germany 2
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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OutlineOutline
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 ModelThe TAXSIM Model (since 2007)(since 2007)
-“All models are wrong; some models are useful.”Box
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Previous modelsPrevious models
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• 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 TAXSIMActors 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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Shadow 2011, Münster, Germany
Model ArchitectureModel Architecture
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TAXSIM Decision Making
Audits,Governmental
services
Model of the market
Information from social networks
Taxpayer strategy Decision
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Refinement of the EnvironmentRefinement 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...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)
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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.)
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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
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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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ResultsResults
-”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 Conclusions and Future WorkFuture 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!Thank You!http://taxsim.mass.aitia.ai
[email protected]@aitia.ai
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