IMF Working Papers

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

ByMizuho Kida, Simon Paetzold

May 27, 2021

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Format: Chicago

Mizuho Kida, and Simon Paetzold. "The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning", IMF Working Papers 2021, 153 (2021), accessed 12/6/2025, https://doi.org/10.5089/9781513582436.001

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Anti-money laundering and combating the financing of terrorism (AML/CFT), Balance of payments, Capital flows, Capital inflows, Crime, Foreign direct investment, Machine learning, Technology

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