Technical Notes and Manuals

The Revenue Administration Gap Analysis Program: An Analytical Framework for Personal Income Tax Gap Estimation

August 27, 2021

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The Revenue Administration Gap Analysis Program: An Analytical Framework for Personal Income Tax Gap Estimation, (USA: International Monetary Fund, 2021) accessed November 8, 2024

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

It is generally difficult to measure revenue not collected due to noncompliance, but a growing number of countries now regularly produce and publish estimated revenue losses. Good tax gap analysis enables the detection of changes in taxpayer behavior by consistent estimates over time. This Technical Note sets out the theoretical concepts for personal income tax (PIT) gap estimation, the different measurement approaches available, and their implications for the scope and presentation of statistics. The note also focuses on the practical steps for measuring the PIT gap by establishing a random audit program to collect data, and how to scale findings from the sample to the population.

Subject: Auditing, Expenditure, Personal income tax, Public financial management (PFM), Revenue administration, Revenue Administration Gap Analysis Program (RA-GAP), Revenue performance assessment, Tax gap, Taxes

Keywords: Auditing, Gap estimate, Non-Observed Economy, Personal Income Tax, Personal Income Tax gap estimation, PIT gap, Publication order, Random Audit Program, Revenue Administration Gap Analysis Program (RA-GAP), Shadow Economy, Tax Administration, Tax Avoidance, Tax Compliance, Tax Evasion, Tax Gap, Taxpayer registry data

Publication Details

  • Pages:

    37

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Technical Notes and Manuals No. 2021/009

  • Stock No:

    TNMEA2021009

  • ISBN:

    9781513577173

  • ISSN:

    2075-8669

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