Working Papers

This paper proposes the use of synthetic training data generated by large language models
to improve machine learning SDG classifiers. It shows that supplementing existing training data with
synthetic data produced by the ChatGPT tool improves the performance of the SDGClassy classifier.
This addition of synthetic data is especially useful in building SDG classifiers given the limited availability
of properly labeled data and the complex, interconnected nature of the SDGs. Synthetic data thus enables
more effective machine-learning applications in this context.

The pandemic-induced global economic crisis has contributed to the re-emergence of sovereign default risk, especially for emerging and developing economies, and has directed attention to the impact of the institutions that are tasked with attempting to predict defaults: the international credit rating agencies. This paper describes four main challenges posed by credit rating agencies, especially from a developing and emerging economies perspective: potential bias in ratings, pro-cyclicality of ratings, governance issues and conflicts of interest, and incorporation of climate risk. It concludes with potential policy solutions addressed at ratings agencies, regulators, and policy makers.…

In Article 25 (1) of the Universal Declaration of Human Rights, the United Nations recognized in 1948 the basic human right to “security in the event of unemployment, sickness, disability, widowhood, old age or other lack of livelihood in circumstances beyond ... control.” This paper examines how economic insecurity is related to, yet different from, poverty and inequality, why it matters for human well-being and how it has been changing in different countries around the world in recent years. The paper concludes with discussion of how economic insecurity has been and will be affected by the Covid-19 pandemic/recession.