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01876cas a2200313 a 4500 |
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|c 18.00 USD
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|z 9781513582436
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Kida, Mizuho.
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|a The Impact of Gray-Listing on Capital Flows :
|b An Analysis Using Machine Learning /
|c Mizuho Kida, Simon Paetzold.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2021.
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|a 1 online resource (37 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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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|a Mode of access: Internet
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|a Capital Flows and Emerging Market Economies
|2 imf
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| 650 |
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|a Foreign Exchange
|2 imf
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| 650 |
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|a Inferential Machine Learning Technique
|2 imf
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| 650 |
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|a Informal Economy
|2 imf
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| 650 |
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|a Underground Econom
|2 imf
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|a Paetzold, Simon.
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|a IMF Working Papers; Working Paper ;
|v No. 2021/153
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2021/153/001.2021.issue-153-en.xml
|z IMF e-Library
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