Does transparency pay? Evidence from IMF data transparency policy reforms and emerging market sovereign bond spreads. (November 2018)

• Record Type: Journal Article
• Authors: Choi, Sangyup; Hashimoto, Yuko
• Is Part Of: Journal of international money and finance. Volume 88(2018)
• Page Start: 171
• Page End: 190
• Publication Date: 2018-11
• Subjects: F30 -- G10 -- G20; Data transparency -- Structural reforms -- EMBIG spreads -- Event study -- IMF Data Standards Initiatives -- Forecast error; International finance -- Periodicals; Foreign exchange -- Periodicals; Finances internationales -- Périodiques; Change -- Périodiques; Foreign exchange; International finance; Periodicals; 332.04205
• DOI: 10.1016/j.jimonfin.2018.08.002
• Languages: English
• ISSNs: 0261-5606

Abstract

Highlights: We analyze the effect of data transparency reforms on sovereign bond spreads. We use an event study to mitigate endogeneity issues about the adoption of reforms. Using daily data, we show that they are positive news to investors on impact. A decline in the spreads over medium-run is economically and statistically significant. Abstract: Does an effort to enhance data transparency pay? We answer this question by analyzing the effect of the data transparency policy reforms, as reflected in subscriptions to the IMF's Data Standards Initiatives, namely, the Special Data Dissemination Standard (SDDS) and the General Data Dissemination System (GDDS), on the spreads of emerging market sovereign bonds. Employing a short-run event study with daily data, we convincingly show that market participants react to the transparency reform news positively. We then measure its medium-term effect, which is more relevant for economic decisions. By showing that the reform decision is largely independent of a country's macroeconomic development we mitigate endogeneity issues regarding a decision to adopt such reforms. On average, the adoption of the SDDS and GDDS lead to a 13% reduction in the spreads over one year, following such reforms. This finding is robust to various sensitivity tests, including careful consideration of overlapping events, controlling for additional variables, and a placebo test.