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

Mizuho Kida and Simon Paetzold

No 2021/153, IMF Working Papers from International Monetary Fund

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; coefficient estimate; Capital flows; Capital inflows; Anti-money laundering and combating the financing of terrorism (AML/CFT); Machine learning; Foreign direct investment; Global (search for similar items in EconPapers)
Pages: 37
Date: 2021-05-27
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fdg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=50289 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2021/153

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().