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A Narrative Fiscal Consolidation Dataset for Sub-Saharan Africa
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.
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.
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