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

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