Predicting Debt Distress in Low-Income Countries

Clemens Graf von Luckner, Sebastian Horn, Aart C. Kraay and Rita Ramalho

No 11441, Policy Research Working Paper Series from The World Bank

Abstract: This paper develops an empirical model to predict episodes of debt servicing difficulties (“debt distress”) in low-income countries, with three main contributions to the existing literature. First, it develops more refined measures of external debt distress episodes that allow timing the onset of distress episodes with increased precision. Second, it develops a systematic algorithm to comprehensively assess the out-of-sample predictive performance of more than 550,000 candidate binary prediction models using J-K-fold cross-validation. Third, it tests whether more sophisticated machine learning algorithms can outperform simple probit models. The paper finds that simple single-equation probit models have better predictive power for debt distress than more sophisticated algorithms and are comparable in terms of predictive performance to important policy benchmarks such as the IMF and World Bank debt sustainability framework for low-income countries.

Date: 2026-09-02
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