An Agent-Based Tax Evasion Model Calibrated Using Survey Data Attila Szabó 1,2, László Gulyás 1,2, István János Tóth 3 1 AITIA International Inc, 2 Loránd Eötvös University, Budapest 3 Institute of Economics, Hungarian Academy of Sciences Shadow 2011, Münster, Germany 1 Work in Progress

Shadow 2011, Münster, Germany 2 Acknowledgements The partial support of the following grants of the Hungarian Government are gratefully acknowledged. KMOP / OTKA T62455 OTKA K48891

Shadow 2011, Münster, Germany Outline 1.The TAXSIM Model 2.Earlier Results 3.Survey-Driven Calibration (work in progress) 4.Results 5.Conclusions and Future Work 3

Shadow 2011, Münster, Germany The TAXSIM Model (since 2007) -“All models are wrong; some models are useful.” Box 4

Shadow 2011, Münster, Germany Previous models 5 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

Shadow 2011, Münster, Germany 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 6

Shadow 2011, Münster, Germany Model Architecture 7

TAXSIM Decision Making Audits, Governmental services Model of the market Information from social networks Taxpayer strategyDecision 8Shadow 2011, Münster, Germany

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) ‏ 9

Shadow 2011, Münster, Germany TAXSIM is... … a simulator that aggregates Law enforcement Motivation towards compliance Taxpayers' goals … rather a family of models than a simple model 10

Shadow 2011, Münster, Germany Earlier Results -“The only thing new in this world is the history that you don't know” (Harry S. Truman) ‏ 11

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 )

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

Shadow 2011, Münster, Germany Survey-Driven „Calibration” -”To find out what happens when you change something, it is necessary to change it” (anonymous) ‏ 16

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) 17Shadow 2011, Münster, Germany

Selected Findings of the Survey S urvey 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% 18Shadow 2011, Münster, Germany

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. 19Shadow 2011, Münster, Germany

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) 20Shadow 2011, Münster, Germany

Results -”The best time to plan an experiment is after you have done it.” R. A. Fisher 21

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. 22Shadow 2011, Münster, Germany

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) 23Shadow 2011, Münster, Germany

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. 24Shadow 2011, Münster, Germany

Adaptive Audit Strategy Improves Compliance 25Shadow 2011, Münster, Germany

Adaptive Audit Strategy Improves Compliance 26Shadow 2011, Münster, Germany

Social Networks Matter Confirmed Less information, more non-compliance (remember, cost optimization is the main driver!) 27Shadow 2011, Münster, Germany

Conclusions and Future Work -”Perfection is not possible; it's always approximation” (anonymous) ‏ 28

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. 29Shadow 2011, Münster, Germany

Thank You! 30