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. JEL Classification Numbers: F21, F38, G28, K33, K42, L51 the FATF secretariat, and participants at a seminar at the IMF for helpful comments.