The complexity of tax regulations and principles of justice as determinants of taxpayer compliance: case of Indonesia
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DOIhttp://dx.doi.org/10.21511/pmf.13(1).2024.14
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Article InfoVolume 13 2024, Issue #1, pp. 174-184
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This study aims to determine and analyze the determinants of the complexity of tax regulations and the principles of justice regarding taxpayer compliance in Indonesia. The study surveyed 148 individual taxpayers who are entrepreneurs of micro, small, and medium enterprises (MSMEs) using a Google Form. The results show that the complexity of tax regulations has a significant negative effect (–0.253) on taxpayer compliance, and the principles of justice (0.501) have a significant positive effect on taxpayer compliance. An R square shows that independent variables have a 45.5% influence on increasing taxpayer compliance. The degree of the correlation relationship between all independent variables is around 0.674. The results confirm the relationship between the complexity of tax regulations and the principle of justice and their effect on taxpayer compliance. Therefore, to increase taxpayer compliance, the government should put efforts into simplifying and explaining tax regulations. This can help reduce inadvertent errors, increase taxpayer confidence, and reduce the overall cost of complying with regulations. It is important to consider aspects of justice when designing and implementing the tax system. Efforts to increase transparency, reduce injustice, and make taxes applied proportionally can help strengthen taxpayer compliance and support the sustainability of state revenues.
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JEL Classification (Paper profile tab)M48, M41, K34
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References32
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Tables8
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Figures0
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- Table 1. Frequency of tax regulation complexity
- Table 2. Frequency of the principles of justice
- Table 3. Frequency of taxpayer compliance
- Table 4. Validity of the tax regulation complexity statements
- Table 5. Validity of the principle of justice statements
- Table 6. Validity of taxpayer compliance statements
- Table 7. Reliability of research variables
- Table 8. Multiple linear regression analysis
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