Technical Notes and Manuals

The Revenue Administration–Gap Analysis Program: Model and Methodology for Value-Added Tax Gap Estimation

By Eric Hutton

April 7, 2017

Download PDF

Preview Citation

Format: Chicago

Eric Hutton. The Revenue Administration–Gap Analysis Program: Model and Methodology for Value-Added Tax Gap Estimation, (USA: International Monetary Fund, 2017) accessed November 8, 2024

Also available in: español, français

Disclaimer: This Technical Guidance Note should not be reported as representing the views of the IMF. The views expressed in this Note are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

The IMF Fiscal Affairs Department’s Revenue Administration Gap Analysis Program (RA-GAP) assists revenue administrations from IMF member countries in monitoring taxpayer compliance through tax gap analysis. The RA-GAP methodology for estimating the VAT gap presented in this Technical Note has some distinct advantages over commonly used methodologies. By using a value-added approach to estimating potential VAT revenues, as compared to the more traditional final consumption approach used by most countries undertaking VAT gap estimation, the RA-GAP methodology can provide VAT compliance gap estimates on a sector-by-sector basis, which assists revenue administrations to better target compliance efforts to close the gap. In addition, the RA-GAP methodology uses a unique measurement for actual VAT revenues, which isolates changes in revenue performance that might be due to cash management (e.g., delays in refunds) from those due to actual changes in taxpayer compliance.

Subject: Consumption, National accounts, Revenue administration, Tax gap, Value-added tax

Keywords: RA-gap VAT gap estimation methodology, TNM

Publication Details

  • Pages:

    32

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Technical Notes and Manuals No. 2017/004

  • Stock No:

    TNMEA2017004

  • ISBN:

    9781475583618

  • ISSN:

    2075-8669