Improving Sovereign Financing Conditions Through Data Transparency

Jesus Gonzalez-Garcia

No 2022/230, IMF Working Papers from International Monetary Fund

Abstract: Does it pay off to be transparent and, if so, can the benefits of transparency be measured? This paper provides an affirmative answer to both questions, supported by novel evidence on the link between transparency through dissemination of economic data and sovereign bond spreads. It explores changes in sovereign financing conditions when countries join the IMF Data Standards Initiatives—a multilateral framework that promotes data transparency as a global public good. The results from event studies and local projection models show a significant decrease in spreads following the adoption of the standards. In addition, countries with relatively weaker governance benefit the most from signaling their effort toward strengthening transparency.

Keywords: Data transparency; IMF Data Standards Inititatives; sovereign borrowing; EMBIG spreads; event studies; local projection methods; governance.; IMF Data Standards Initiatives; data dissemination standard; GDDS country; Data dissemination; Special Data Dissemination Standard (SDDS); Financial statistics; Global (search for similar items in EconPapers)
Pages: 17
Date: 2022-11-18
New Economics Papers: this item is included in nep-acc
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=525742 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2022/230

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().