Abstract
The Portfolio Investment Framework, the Principal Agent Framework and the Principal Two Agents Framework of tax evasion, individually or collectively, are inadequate to represent fairly the dynamics of corporate income tax evasion. These behavior based frameworks are for the prediction of tax evasion and their usefulness for identifying tax evasion is limited. To bridge the gap between the prediction and the identification aspects of corporate income tax evasion, this paper presents the Contextual Framework of Corporate Income Tax Evasion, structured with twelve ideas and thirty five dynamics to guide and direct future research. With a simple game theory model, the paper promotes the tax audit cut-off policy that incorporates a reward program for the agent of government, ensures audit frequency and tests independence of the agent from governance. Key words: Portfolio Investment Framework, Principle Agent Framework, Principle Two Agent Framework, Tax Evasion, Corporate Income Tax Evasion.
Highlights
Tax evasion is a loss of government revenue and is a major concern across nations
Theoretical researches on corporate income tax evasion have been disadvantaged far too long by the incompatibility and limitations of the PIF, the PAF and the PTAF to represent fairly the dynamics of corporate income tax evasion
These behavior based frameworks emphasized the logic of evasion behaviors for predicting tax evasion and ignored the identification aspect of tax evasion
Summary
Tax evasion is a loss of government revenue and is a major concern across nations. The opposing loss or benefit of tax evasion between government and tax evaders is the reason why tax evasion keeps recurring and has persisted. A key aspect in the combat against tax evasion is the ability of tax collectors to detect evasion. This aspect was ignored in the development of theoretical frameworks and modeling of tax evasion. The ignoring of the detection aspect of evasion limits the usefulness of the behavior based frameworks because the worth of the prediction is evaluated by the successful identification of evasion when it is executed
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