The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
Electronic Access:
Free Download. Use the free Adobe Acrobat Reader to view this PDF file
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.
Series:
Working Paper No. 2021/153
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
Frequency:
regular
English
Publication Date:
May 27, 2021
ISBN/ISSN:
9781513582436/1018-5941
Stock No:
WPIEA2021153
Pages:
37
Please address any questions about this title to publications@imf.org