What factors influence the terms of an International Monetary Fund (IMF) program? And how do those factors play into shaping the design of the programs? IMF conditionality has always been the most controversial aspect of the programs designed for countries borrowing from the IMF. In order to answer these questions, this workshop will present a project that uses mixed methods including Natural Language Processing (NLP) and process tracing. The overarching objective of the project is to create a comprehensive novel methodology and framework for understanding IMF program design, shedding light on the processes leading to variation in IMF lending. The presented NLP tool for automated, fast analysis of IMF Executive Board meeting minutes, which is able to capture elements including individual board member sentiments, alliance between representatives of different countries and G5 member stance regarding IMF lending, will be at the core of the workshop. Python will be used throughout the second half of the workshop.

The participants at least should have a basic coding skills in Python.