Identifier: BDE-D-000032
This dataset provides comprehensive and harmonised information on General Government debt compiled in accordance with the Excessive Deficit Procedure (EDP) and the European System of Accounts (ESA 2010). The series, produced by the Banco de España, are published monthly with early información and allow for the monitoring of the level, structure and changes in public debt, supporting fiscal surveillance and macroeconomic analysis.
The data include stocks, monthly changes, year on year growth rates and contributions to the total EDP debt. The dataset distinguishes the main financial instruments comprising EDP government debt - such as currency and deposits, short and long term debt securities and loans - and also covers specific items of analytical interest, including foreign currency liabilities, the core Central Government deposits with the Banco de España, and guarantees granted (contingent liabilities).
A detailed institutional and functional breakdown is provided, covering the main subsectors of General Government - the core Central Government, regional governments, local governments, other central government bodies and social security administrations - as well as a breakdown by holder sector, distinguishing between other general government units, resident sectors and the rest of the world, with consolidated figures also available. This dataset is a key reference for public debt sustainability analysis and international comparability, fully aligned with the European statistical requirements.
Categories: General Government
Dataset Details
General government debt according to the Excessive Deficit Procedure (EDP) (Early monthly information)
Update frequency: Monthly
Status: Active
General government debt according to the Excessive Deficit Procedure (EDP) (Early monthly information)
Update frequency: Monthly
Status: Active
General government debt according to the Excessive Deficit Procedure (EDP) (Early monthly information) (BIEST application)
Update frequency: Monthly
Status: Active
Questions about the dataset
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