Does transparency pay? Evidence from IMF data transparency policy reforms and emerging market sovereign bond spreads

Improving Sovereign Financing Conditions Through Data Transparency

“…It is worth reiterating that the estimation requires that the adoption of the standards is considered an unexpected shock, uncorrelated with other developments. The event studies of the previous subsection and the analysis of Choi and Hashimoto (2018), in which the effect of the adoption of standards is well identified in a short time window, support this working assumption.…”

“…This work was expanded in Cady and Pellechio (2006) to include the effects of participation in the GDDS, and they found that primary market launch spreads show a 19 percent decline for SDDS subscribers and a 9 percent decline for GDDS participants. More recently, Choi and Hashimoto (2018) estimated the overall effect of joining the SDDS or GDDS combining the indicators of participation (dummy variables) into a single series. They found a statistically significant reduction of 13 percent in the EMBIG spreads index over one year.…”

“…Previous studies have shown a favorable effect of transparency on countries' financing conditions in global capital markets that is materialized in lower sovereign bond spreads (Cady, 2005;Cady and Pellechio, 2006;Glennerster and Shin, 2008;Moretti, 2011;and Choi and Hashimoto, 2018). We revisit these findings using new methods to study the relationship between sovereign spreads and participation in the different tiers of the IMF Data Standards Initiatives, which is a multilateral framework to enhance transparency through the dissemination of economic and financial data.…”

“…Potential issues of endogeneity when estimating the effects of joining the IMF data standards on sovereign spreads have been treated byChoi and Hashimoto (2018) using event studies. First, they show that forecast errors obtained from models for sovereign spreads change after joining the different data dissemination standards in a way suggesting that investors reward joining the SDDS and GDDS with lower borrowing costs.…”

In Search of Information:

“…For example, we consider a A series of political reforms started at the beginning of 2011. situation to assess real GDP growth for the year 2016 as of January 2017 when actual real GDP for 2016 was not available, although SVIs for 2016 were available. Control varibles are chosen based on the empirical literature on variables to forecast (e.g., for economic growth regression, Barro, 2015; for the determinants of capital flows, Araujo and others, 2017;Hashimoto and Wacker, 2016;Choi and Hashimoto, 2018), although many of the control variables that are used in the literature are not included due to lack of observations for many LIDCs. For example, including the gross enrollment ratio to secondary education reduces the sample size by one-third, but the estimation results do not change significantly.…”