Cover IMF Working Papers
Buy Book in Print
The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
Author:
Mizuho Kida
Search for other papers by Mizuho Kida in
Current site
Google Scholar Close
and
Simon Paetzold null

Search for other papers by Simon Paetzold in
Current site
Google Scholar Close
Series:
IMF Working Papers
Volume/Issue:
Volume 2021: Issue 153
Language:
English
Publication Date:
27 May 2021
DOI:
https://doi.org/10.5089/9781513582436.001
ISBN:
9781513582436
ISSN:
1018-5941
Pages:
37
Keywords:
capital flows; AML/CFT; gray list; machine learning; emerging market economies; inferential machine learning technique; gray-listing affect; analysis using machine learning; coefficient estimate; Capital flows; Capital inflows; Anti-money laundering and combating the financing of terrorism (AML/CFT); Machine learning; Foreign direct investment; Global
Legal-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.
Download PDF (1.5 MB) Download Epub
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.
  • Collapse
  • Expand
Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund