Text-Mining Imf Country Reports – An Original Dataset

Abstract

This article introduces an original panel dataset based on the text of country reports by the International Monetary Fund. It consists of a total of 5561 Article IV consultation and program review documents, published between 2004 and 2018 on 201 countries. The text of these reports provide indications of the perceived policy weaknesses, economic risks, ongoing reforms and implemented or neglected policy advice. Thus the content of IMF reports are widely used in the economics, political science and IR literature. To our knowledge this is the first comprehensive dataset that aggregates these country reports.The paper gives a detailed account on the data acquisition and management process. To demonstrate and validate the dataset's application for research we present three validation exercises. We find that Article IV reports can indicate incoming institutional reforms, show changes in IMF policy advice over time and identify potential gains from recently discovered natural resources in certain cases. Taken together, this paper contributes an original dataset of IMF country reports and demonstrates how it can be a useful foundation for further research into the role of international financial institutions.

How Have IMF Priorities Evolved? A Text Mining Approach

“…Text mining of international organizations' documents, however, is a relatively recent development. Mihalyi and Mate (2018) analyze IMF Article IV reports to assess the nature of IMF surveillance and, similarly, IMF (2019) consider how the IMF's social spending surveillance has evolved through a text mining analysis. Amaglobeli et al (2018) construct a database of tax policy measures building on both OECD surveys and tax-related news.…”