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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

Copyright Page IMF Working Paper Finance Department The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning Prepared by Mizuho Kida and Simon Paetzold 1 Authorized for distribution by Olaf Unteroberdoerster May 2021 The 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. Abstract