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dc.contributor.authorMarkl, Christopher James
dc.date.accessioned2014-03-03T21:06:46Z
dc.date.available2014-03-03T21:06:46Z
dc.date.issued2003-12
dc.identifier.othermarkl_christopher_j_200312_ma
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/markl_christopher_j_200312_ma
dc.identifier.urihttp://hdl.handle.net/10724/21325
dc.description.abstractThe purpose of this paper is to investigate the governmental use of uncertainty in the application of tax policy. The main query examines if the government is able to raise compliance through creating uncertainty about the probability of audit. The theoretical portion of this paper uses a two-period expected utility maximizing Bayesian learner; a model that is originally postulated by Gollier (2003) for use in the context of the insurance industry. This paper references empirical work in the use of uncertainty in taxes, as well as the legality of withholding the probability of audit under the Freedom Information Act. The goal of this paper is to prove that an expected utility maximizing Bayesian learner reduces the optimal amount of tax evasion in the first period if the ratio of absolute prudence to absolute risk aversion is smaller than two.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectTax evasion
dc.subjectTax policy
dc.subjectUncertainty
dc.titleUncertainty about the probability of a tax audit : raising compliance or confusion?
dc.typeThesis
dc.description.degreeMA
dc.description.departmentEconomics
dc.description.majorEconomics
dc.description.advisorArthur Snow
dc.description.committeeArthur Snow
dc.description.committeeRonald S. Warren
dc.description.committeeGregory A. Trandel


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