An ML Approach to International Financial Institutions: IMF Lending

• In Event: IMF and Its Effectiveness

Abstract

Why do some International Monetary Fund (IMF) programs are larger or have fewer conditions attached while others are laden with a multitude of strings attached? Despite a sizeable literature on IMF lending, there is still an ongoing debate about the main factors explaining the variation in IMF program features. This research develops an innovative approach to constructing a comprehensive model on IMF program design, utilizing a hybrid methodological framework.
Central to this study are two pivotal questions: What are the key factors influencing the terms of IMF programs, and how do these factors collectively impact the design of these programs? To unravel these complexities, the research employs a mixed-method approach. A significant part of the analysis involves sentiment analysis conducted through natural language processing (NLP), applied to a meticulously curated corpus of approximately 50,000 pages of IMF Executive Board meeting minutes. This corpus, annotated by a dedicated team, ensures data reliability and precision in sentiment analysis. The study also incorporates advanced computational techniques, marking a departure from conventional econometric modeling. This methodology provides a more nuanced understanding of IMF program design, highlighting the interplay of various economic, political, and institutional factors.
The research offers substantial contributions to the field of International Political Economy (IPE) and Computational Social Sciences (CSS). It opens new avenues for exploring the decision-making processes in international organizations (IOs), particularly in the context of the IMF. The development of a comprehensive machine learning model, complementing traditional statistical models, stands out as a methodological innovation. This model integrates a larger number of variables and achieves high accuracy in predicting loan sizes and conditionality categories, thus enhancing the predictive power of the outcomes.
Furthermore, the research makes a significant contribution by developing an automated tool for the analysis of Executive Board meeting minutes. This tool facilitates future research on IO document analysis and can be adapted for use in other international settings. The research also explores the implications of different loan sizes and conditions, examining the complex mechanisms behind IMF decision-making. It considers various hypotheses, including the influence of G5 countries on IMF policies, the autonomy of recipient-country bureaucrats, and the impact of economic crises on program design.
In brief, this research not only sheds light on the intricate processes leading to variations in IMF lending but also provides an extensible tool for IPE scholars. It enhances understanding of program design and implementation, offering insights into the broader context of global economic governance. The integration of diverse methodologies and data sources in this study paves the way for a more comprehensive and multifaceted analysis of international organization studies, contributing significantly to both theoretical and practical understandings of IMF program design.

Author

• Merih Angin, Koç University