A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa

IMF Working Papers, January 23, 2026

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Canonical URL: A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa

Introduces the first narrative-based dataset on fiscal consolidations for sub-Saharan Africa (SSA).
Coverage period: 1990-2024.
Data source: staff reports from the International Monetary Fund (IMF).
Identification focus: fiscal consolidation actions motivated by long-term considerations (rather than cyclical conditions), including:
- reducing an inherited budget deficit,
- ensuring long-term public debt sustainability,
- improving economic efficiency.
Key methodological emphasis: focuses exclusively on measures exogenous to the business cycle to provide more precise identification of fiscal consolidation actions for empirical analysis of the macroeconomic effects of fiscal policy in SSA.

Approach assisted by artificial intelligence (AI) to systematically identify narrative fiscal consolidation actions from IMF staff reports.
Narrative identification isolates measures taken for long-term policy objectives rather than cyclical stabilization.

Provides a dataset tailored for empirical analysis of the macroeconomic effects of fiscal policy in SSA by improving the precision of consolidation identification.
Facilitates research on expenditure, fiscal consolidation, fiscal policy, fiscal stance, and revenue administration in SSA.
Keywords signaling topical relevance: Africa; Artificial intelligence methods; Fiscal consolidation; fiscal consolidation action; Fiscal policy; Fiscal stance; Narrative fiscal consolidation dataset; Narrative identification; Sub-Saharan Africa.

Published as an IMF Working Paper; includes the standard IMF Working Papers disclaimer that these describe research in progress and the views expressed are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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International Monetary Fund — A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa (IMF Working Papers 2026, 011).