Project description
New tool to assess IMF deal-making
The International Monetary Fund (IMF) is an organisation of 189 countries working to foster global monetary cooperation, secure financial stability, facilitate international trade, promote high employment and sustainable economic growth, and reduce poverty around the world. What factors influence the terms of an IMF programme? And how do those factors play into shaping the design of the programmes? To answer these questions, the EU-funded MLending project will create a tool to model the programme design and implementation process. Specifically, it will create a machine learning model for predicting the loan size, number of IMF conditions and waivers during a programme. It will also design a natural language processing tool for analysis of the IMF’s Executive Board meeting minutes to capture information like individual board member sentiments, alliances between representatives of different countries and G5 stance.
Objective
The International Monetary Fund (IMF) is frequently argued to be an agent of its most powerful shareholders. Challenging the common belief that strategic allies of the US 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 existing 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 of an IMF program? And how do those factors play into shaping the design of the programs? Through creating an original framework, the project will aim to provide an indispensable and extensible tool for international political economy researchers, policymakers, governments and IMF staff to model the program design and implementation process with high predictive power of the outcomes. The project will make a major contribution to the literature by creating a 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 providing high accuracy of prediction. The project 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 with high accuracy. The research will take on board an eclectic approach, using mixed methods involving ML, NLP, statistical analyses, and process tracing of in-depth case studies (namely Romania and Greece) to account for the variation in the terms of IMF programs.
Fields of science
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- social scienceseconomics and businesseconomicspolitical economy
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
34450 Istanbul
Türkiye