The Impact of Gray-Listing on Capital Flows : An Analysis Using Machine Learning /
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 pol...
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| Natura: | Periodico |
| Lingua: | English |
| Pubblicazione: |
Washington, D.C. :
International Monetary Fund,
2021.
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| Serie: | IMF Working Papers; Working Paper ;
No. 2021/153 |
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| Accesso online: | Full text available on IMF |
| Riassunto: | 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. |
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| Descrizione del documento: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Descrizione fisica: | 1 online resource (37 pages) |
| Natura: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Accesso: | Electronic access restricted to authorized BRAC University faculty, staff and students |