H2020Индивидуална стипендия2020–2022

MLending · An ML Approach to IFIs: Determinants of IMF Lending

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-12-01 → 2022-11-30
Финансиране от ЕС: 145 356 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Факторите, които определят условията и размера на заемите от Международния валутен фонд, се анализират чрез машинно обучение и статистически методи. Това помага да се разбере как икономическият растеж и настроенията на ръководството влияят върху условията по кредитите за отделните държави.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

An ML Approach to IFIs: Determinants of IMF Lending

This MSCA IF project “An ML Approach to IFIs: Determinants of IMF Lending” focuses on a controversial aspect of the International Monetary Fund’s (IMF) primary mission, i.e., lending to its members in need of finance. The research concentrated on the following questions: What factors influence the terms of an IMF program? And how do those factors play into shaping the design of the programs? In order to answer these questions, this research employed mixed methods including machine learning (ML), natural language processing (NLP), statistical analysis and process tracing. According to the project findings, the IMF’s Executive Board (EB) sentiment has a strong impact on the number of conditions that the IMF attaches to its loans. A significant relation was particularly observed in the external and financial sector. Likewise, GDP growth has a significant negative relation with the number of conditions in the external and financial sector. GDP growth also has a strong positive impact on the loan amount, implying that when a borrowing country is in a better economic phase, it can secure a larger loan. Although political fragmentation does not have a significant relation with loan size, it is found to have a positive correlation with the total number of conditions when interacted with EB sentiment. The analysis of the EB meeting minutes provides evidence for antinomic delegation by the EB, which is a path-dependent causal mechanism observed to influence the negotiations of a program with insistence on stricter conditionality by the IMF staff acting precautious in response to criticisms by the EB regarding the design of a previous program. The main findings of the analysis on the case studies, namely Romania and Greece, point towards the influence of economic bureaucracy on all sides of the negotiations on the program design. The overarching objective of this 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. Through creating this framework, this project provides an indispensable and extensible tool for international political economy researchers, policymakers, and IMF staff to model the program design and implementation process with high predictive power of the outcomes.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

KOC UNIVERSITY · IstanbulКоординаторТурция