The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning

37 Pages Posted: 4 Feb 2022

See all articles by Mizuho Kida

Mizuho Kida

International Monetary Fund (IMF)

Simon Paetzold

Goethe University Frankfurt

Date Written: May 1, 2021

Abstract

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.

Keywords: capital flows, AML/CFT, gray list, machine learning, emerging market economies, inferential machine learning technique, gray-listing affect, analysis using machine learning, gray list, coefficient estimate, Capital flows, Capital inflows, Anti-money laundering and combating the financing of terrorism (AML/CFT), Machine learning, Foreign direct investment, Global

JEL Classification: F31, E26, F21, K42, O33

Suggested Citation

Kida, Mizuho and Paetzold, Simon, The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning (May 1, 2021). IMF Working Paper No. 2021/153, Available at SSRN: https://ssrn.com/abstract=4026331

Mizuho Kida (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Simon Paetzold

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

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