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

Author

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  • Mizuho Kida
  • Simon Paetzold

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.

Suggested Citation

  • Mizuho Kida & Simon Paetzold, 2021. "The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning," IMF Working Papers 2021/153, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2021/153
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