How Do Member Countries Receive IMF Policy Advice: Results from a State-of-the-art Sentiment Index

By Ghada Fayad, Chengyu Huang, Yoko Shibuya, Peng Zhao

January 17, 2020

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Ghada Fayad, Chengyu Huang, Yoko Shibuya, and Peng Zhao. How Do Member Countries Receive IMF Policy Advice: Results from a State-of-the-art Sentiment Index, (USA: International Monetary Fund, 2020) accessed December 21, 2024

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

This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries’ reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.

Subject: Commodity price fluctuations, Commodity prices, Economic sectors, Financial sector, Financial Sector Assessment Program, Financial sector policy and analysis, Machine learning, Prices, Technology

Keywords: Commodity price, Commodity price fluctuations, Commodity prices, Economic Policy, Financial sector, Financial Sector Assessment Program, Fund advice, Fund policy assessment, Global, IMF article IV staff report, IMF increase fund traction, IMF member countries' reception, IMF member countries' reception of Fund advice, IMF Policy advice, IMF quota, Increase fund traction, Machine learning, Natural Language Processing, Policy advice, Sentiment Analysis, Sentiment index, Surveillance, WP

Publication Details

Pages:

42
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/007
Stock No:

WPIEA2020007
ISBN:

9781513526010
ISSN:

1018-5941

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