Words that matter: A machine learning analysis of United Nations General Assembly speeches and their influence on aid allocation

Abstract

This article studies whether countries’ speeches at the United Nations General Assembly (UNGA) matter for international aid allocation. I use a supervised machine learning algorithm called Wordscore and 7,533 different statements from 198 countries made in the period from 1975 to 2018 to measure countries’ preferences from text. Then, by employing panel regression analysis and the synthetic control method, I find considerable evidence that countries’ preferences derived from speeches affect aid allocation. Both the US and Russia provide more aid to countries that present speeches more in line with their current agenda and preferences.