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

Author/Editor:

Mizuho Kida ; Simon Paetzold

Publication Date:

May 27, 2021

Electronic Access:

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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

Frequency:

regular

English

Publication Date:

May 27, 2021

ISBN/ISSN:

9781513582436/1018-5941

Stock No:

WPIEA2021153

Pages:

37

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