Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
This paper investigates the dynamic impact of natural resource discoveries on government debt sustainability. We use a ‘natural experiment’ framework in which the timing of discoveries is treated as an exogenous source of within-country variation. We combine data on government debt, fiscal stress and debt distress episodes on a large panel of countries over 1970-2012, with a global repository of giant oil, gas, and mineral discoveries. We find strong and robust evidence of a ‘fiscal presource curse’, i.e., natural resources can jeopardize fiscal sustainability even before ‘the first drop of oil is pumped’. Specifically, we find that giant discoveries, mostly of oil and gas, lead to permanently higher government debt and, eventually, debt distress episodes, specially in countries with weaker political institutions and governance. This evidence suggest that the curse can be mitigated and even prevented by pursuing prudent fiscal policies and borrowing strategies, strengthening fiscal governance, and implementing transparent and robust fiscal frameworks for resource management.