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Financial Analytics: Science and Experience
 

Evaluation of the tax administration efficiency: a methodological approach

Vol. 8, Iss. 45, DECEMBER 2015

PDF  Article PDF Version

Received: 14 October 2015

Received in revised form: 6 November 2015

Accepted: 16 November 2015

Available online: 15 December 2015

Subject Heading: Tax regulation

JEL Classification: 

Pages: 37-48

Ivanova I.A. Ogarev Mordovia State University, Saransk, Republic of Mordovia, Russian Federation
ivia16@mail.ru

Efremova T.A. Ogarev Mordovia State University, Saransk, Republic of Mordovia, Russian Federation
Efrta@mail.ru

Importance The completeness and timeliness of tax revenues are subject to tax administration, i.e. government control over tax relationships.
     Objectives The research devises methods for assessing the efficiency of tax administration through econometric modeling.
     Methods The article examines correlation and regression modeling of how tax revenue depends on the tax administration efficiency. Multiple regression mainly pursues a model with multiple factors and determines the effect of each factor in particular, their common influence on the modeled indicator.
     Results We found factors, which influenced tax revenue most of all. Having examined the nature and modeled relationships between factors and results, we selected and substantiated two mathematical regression equations based on three variables. Comparing the equations per significance criteria allows to determine the substance of the correlation in question.
     Conclusions and Relevance We devise a model depicting how tax revenue depends on the tax administration efficiency, and substantiate the reliability and possibility of using the model to assess and forecast tax revenue. Modeling allows to forecast a collectible amount of taxes in the future, ensure their further reasonable allocation in line with public needs. The findings may be useful when assessing and projecting tax revenue of the budget of the State.

Keywords: tax administration, economic and statistical modeling, regression equation, multicollinearity, determination

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