The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
37 Pages Posted: 4 Feb 2022
Date Written: May 1, 2021
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 ï¬‚ows, 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: Suggested Citation