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Mizuho Kida and Simon Paetzold
The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.
Mizuho Kida and Simon Paetzold

1 Introduction This paper examines what happens to a country’s capital flows after it is identified in the Financial Action Task Force (FATF)’s list of countries or jurisdictions with strategic deficiencies in their framework for Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT). The list of countries with such deficiencies is known as the gray list. How much gray-listing affects a country’s capital flows is of interest to policy makers and investors. It is also of interest to the Fund in relation to country surveillance, prioritizing