IMF Working Papers

A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa

ByHany Abdel-Latif, Khalil Bechchani, Antonio David, Thibault Lemaire

January 23, 2026

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Format: Chicago

Hany Abdel-Latif, Khalil Bechchani, Antonio David, and Thibault Lemaire. "A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa", IMF Working Papers 2026, 011 (2026), accessed 1/23/2026, https://doi.org/10.5089/9798229034661.001

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

This paper introduces the first narrative-based dataset on fiscal consolidations for sub-Saharan
Africa (SSA). Drawing on staff reports from the International Monetary Fund (IMF) during the period 1990-2024 and using an approach assisted by artificial intelligence (AI), the dataset systematically identifies fiscal consolidation actions motivated by long-term considerations (rather than cyclical conditions), such as reducing an inherited budget deficit, ensuring long-term public debt sustainability and improving economic efficiency. By focusing exclusively on measures exogenous to the business cycle, the dataset provides a more precise identification of fiscal consolidation actions for the empirical analysis of the macroeconomic effects of fiscal policy in SSA.

Keywords: Artificial intelligence methods, Fiscal consolidation, Fiscal policy, Narrative identification, Sub-Saharan Africa