An Agent-Based Tax Evasion Model Calibrated Using Survey Data

1. Shadow 2011, Münster, Germany An Agent-Based Tax Evasion Model Calibrated Using Survey Data Work in Progress 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

2. Shadow 2011, Münster, Germany 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

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

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

5. Shadow 2011, Münster, Germany 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 autilitarianapproach: misreporting is a cost optimization strategy

6. 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

7. Shadow 2011, Münster, Germany Model Architecture

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

9. Shadow 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)‏

10. 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

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

12. 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)

14. 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.)

15. 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

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

17. Shadow 2011, Münster, Germany 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)

18. Shadow 2011, Münster, Germany 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%

19. Shadow 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.

20. Shadow 2011, Münster, Germany Survey-Driven Expectations • Citizensoverestimate audit probability • There is about 73% of agentswhowouldfullycomply (paytaxes) evenwith an audit probability of 0% • Adaptive audit strategyincreasescompliance • Atomizedsocietywouldyield a largershadoweconomy(i.e., thesocialnetwork is important)

21. Shadow 2011, Münster, Germany Results -”The best time to plan an experiment is after you have done it.” R. A. Fisher

22. Shadow 2011, Münster, Germany 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.

23. Shadow 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)

24. Shadow 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.

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

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

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

28. Shadow 2011, Münster, Germany Conclusions and Future Work -”Perfection is not possible; it's always approximation” (anonymous)‏

29. Shadow 2011, Münster, Germany 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.

30. Shadow 2011, Münster, Germany Thank You! http://taxsim.mass.aitia.ai aszabo@aitia.ai lgulyas@aitia.ai