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.
This technical note addresses the following questions: • What are the main ways in which different countries assess and collect personal income tax (PIT) and social insurance contributions (SIC) liabilities (Section I)? • What is the case for transferring responsibility for a country’s SIC collection from its social insurance agency(ies) to its tax authority (Section II)? • What changes does such integration of collection functions involve (Section III)? • Are there any lessons from international experience to guide such reforms (Section IV)? • How to build on these lessons when planning a transfer of collection functions (Section V)? • Are there any beneficial alternatives to full integration of functions (Section VI)?
Mr. Zamid Aligishiev, Mr. Giovanni Melina, and Luis-Felipe Zanna
This note is a user’s manual for the DIGNAR-19 toolkit, an application aimed at facilitating the use of the DIGNAR-19 model by economists with no to little knowledge of Matlab and Dynare via a user-friendly Excel-based interface. he toolkit comprises three tools—the simulation tool, the graphing tool, and the realism tool—that translate the contents of an Excel input file into instructions for Matlab/Dynare programs. These programs are executed behind the scenes. Outputs are saved in a separate Excel file and can also be visualized in customizable charts.
Ernesto Crivelli, Ruud A. de Mooij, J. E. J. De Vrijer, Mr. Shafik Hebous, and Mr. Alexander D Klemm
This paper aims to contribute to the European policy debate on corporate income tax reform in three ways. First, it takes a step back to review the performance of the CIT in Europe over the past several decades and the important role played by MNEs in European economies. Second, it analyses corporate tax spillovers in Europe with a focus on the channels and magnitudes of both profit shifting and CIT competition. Third, the paper examines the progress made in European CIT coordination and discusses reforms to strengthen the harmonization of corporate tax policies, in order to effectively reduce both tax competition and profit shifting.
This technical note and manual (TNM) addresses the following questions: (1) What are the main challenges in administering the value-added tax on imported digital services and the measures that countries have introduced to address the challenges?; (2) What are the main challenges in administering the value-added tax on low-value imported goods and the measures that countries have introduced to address the challenges? ;and (3) What are the key tasks in implementing the measures for improving the administration of the value-added tax on imported digital services and low-value imported goods?
International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.