Merih Angin - IMF Decides, Machine Learns: An AI Approach to IOs
|Summer Research Program - Graduate, full-time
At least 2 reference(s) must be provided.
The International Monetary Fund (IMF), which is considered as “the most powerful international institution in history”, is frequently argued to be an agent of its most powerful shareholders. Challenging the common belief that the strategic allies of the United States and/or G5 countries always get better deals from the IMF, whereas it is the IMF staff who has the main leverage over the design of conditionality when low-income countries are borrowing from the Fund, this project will develop a novel framework, drawing upon a rich body of literature on the IMF, in order to present a comprehensive model that takes into account all actors having an impact on IMF program design. The following questions will be at the core of this research: What factors influence the terms (loan size, number of conditions, and conditionality waivers granted) of an IMF program? And how do those factors play into shaping the design of the programs? In this context, the project will focus on three particular aspects of IMF lending: (1) the size of IMF loans; (2) the number of conditions attached to the loans; and (3) the number of condition waivers granted to borrowing countries during program implementation. By inventing a comprehensive novel methodology for understanding IMF program design, this project will shed light on the processes leading to variation in IMF lending. Through creating an original framework, this project aims to provide an indispensable and extensible tool for international political economy (IPE) researchers, policymakers, governments and IMF staff to model the program design and implementation process with high predictive power of the outcomes. The project aims to make a major contribution to the literature by creating a comprehensive machine learning (ML) model for predicting the loan size, number of IMF conditions and waivers during program implementation, which complements traditional statistical models by integrating a larger number of variables and accuracy of prediction. The research will also create a natural language processing (NLP) tool for automated, fast analysis of the IMF’s Executive Board meeting minutes, which is able to capture elements including individual board member sentiments, alliance between representatives of different countries and G5 stance.