RA-GAP VAT Gap Estimation Model

In this online course, you will learn the theory behind the design of the Value Added Tax (VAT) gap estimation model of the International Monetary Fund’s Revenue Administration Gap Analysis Program (RA-GAP), and you will also learn how to use the model to produce your own VAT gap estimates.

You will become familiarized with the overall structure of the model, and how each of its components interacts. You will learn what the inputs needed for the model are, how to prepare them, and how the model uses these inputs to compute the potential VAT, which is compared to the actual VAT to determine the VAT gap.

The online course comprises five main components, or modules. The first module starts by providing some general background on the concept of tax gaps, and then covers the theory behind the design of the VAT gap estimation model.

The second module moves on to looking at the various policy structures of a VAT are to be input into the model. The third module provides instructions on how various measures of actual VAT are to be prepared for use in the model, and the reason for why these measures are needed.

The fourth module focusses on the compilation of the statistical data needed to construct the potential VAT base, and how this potential VAT base is input into the model. The final module then returns to the model and demonstrates how to execute the model to obtain your results, and, more importantly, how to review and interpret those results.

In short, this course is designed to enable countries to produce VAT gap estimates on a regular and consistent basis, using the VAT gap estimation model of the IMF’s RA-GAP program; which is a well-established tax gap model.

This online course is offered by the IMF with financial support from the Government of Japan.

What you'll learn:

At the conclusion of this course, you will be able to do the following:

• Explainwhat the tax gap is, and its components.

• Explainthe different components of the IMF RA-GAP VAT gap model.

• Preparethe worksheets and data inputs for the model.

• Interpretthe results from the model.

• Diagnosedata and modelling issues that might be affecting the model results.

Note:

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