Mario Pessoa, Andrew Okello, Artur Swistak, Muyangwa Muyangwa, Virginia Alonso-Albarran, and Vincent de Paul Koukpaizan
The value-added tax (VAT) has the potential to generate significant government revenue. Despite its intrinsic self-enforcement capacity, many tax administrations find it challenging to refund excess input credits, which is critical to a well-functioning VAT system. Improperly functioning VAT refund practices can have profound implications for fiscal policy and management, including inaccurate deficit measurement, spending overruns, poor budget credibility, impaired treasury operations, and arrears accumulation.This note addresses the following issues: (1) What are VAT refunds and why should they be managed properly? (2) What practices should be put in place (in tax policy, tax administration, budget and treasury management, debt, and fiscal statistics) to help manage key aspects of VAT refunds? For a refund mechanism to be credible, the tax administration must ensure that it is equipped with the strategies, processes, and abilities needed to identify VAT refund fraud. It must also be prepared to act quickly to combat such fraud/schemes.
In recent years, the IMF has released a growing number of reports and other documents covering economic and financial developments and trends in member countries. Each report, prepared by a staff team after discussions with government officials, is published at the option of the member country.
This paper makes a new attack on the old problem of measuring horizontal inequity in the income tax. Local measures of inequality of posttax income among pretax equals are proposed, which reflect alternative value judgments about the nature and magnitude of an inequity. These measures are aggregated into global indices. The welfare gain from eliminating horizontal inequity revenue-neutrally, and the revenue gain from eliminating it welfare-neutrally, in each case preserving the vertical performance of the tax, are captured by these indices. Difficulties of implementation arising from the “identification problem” are discussed. A variation in the methodology validates banding the income data to create “close equals” groups. Simulations show that the banding procedure works well. A range of potentially fruitful applications is discussed.