recommended standards and methods. This score is calculated from the weighted average of 10 underlying indicator scores. The data source indicator (or collection score) measures whether a country conducts data collection activities in line with internationally recommended periodicity, and whether data from administrative systems are available. This score is calculated from the weighted average of 5 underlying indicator scores. The periodicity and timeliness indicator (or availability score) assesses the availability and periodicity of key socioeconomic indicators if data are available to the public users in time. This score is calculated from the weighted average of 10 underlying indicator scores.

The IMF’s Special Data Dissemination Standard (SDDS) measures if a country releases more frequent, timely and accurate macroeconomic statistics. This indicator captures a timing of subscribing to SDDS and/or the data when the subscribing country meets SDDS specification (and first posted its e-GDDS national data summary data page) which varies across countries and is primarily determined by internal IMF procedures that are not associated to events in these countries. Once a country adopts this standard of data transparency, the country has less intensive reason to reverse this system, consequently, this becomes a long-term commitment. In our empirical analysis, we use two binary data transparency indicators from the IMF: (a) a dummy variable that takes the value of 1 for the years after the country subscribes with the SDDS, and (b) a dummy variable that takes the value of 1 when the country comes into compliance with SDDS specifications (and/or posts its first national data summary page).

# **3.3 Basic Statistics**

We first explore basic statistics on sovereign bond spreads and data transparency for the sample of Sub-Saharan African (SSA) countries and of non-SSA developing countries. Sub-Saharan African countries have increasingly tapped international capital markets. Calderon and Zeufack (2018) document the rapid rise in sovereign international bond issuances across SSA countries during the period 2014-18 —which is one of our subperiods of analysis. When looking at the costs of borrowing, SSA countries tend to have larger sovereign spreads compared with other subgroups such as non-SSA countries, East Asia (EA) and South Asia (SA). The WB data transparency has remained almost unchanged for all country groups over the sample period while the IMF data transparency has improved in the period 2014-18.

Table 1 shows the averages of these sovereign bond spreads, World Bank statistical capacity index and its components as well as IMF data transparency variables across these groups such as all developing countries, SSA countries and non-SSA countries for the periods 1995-2018, 2009-2018 and 2014-2018. Sovereign spreads are higher among SSA countries than non-SSA developing countries. Sovereign bond spreads decreased from 561 to 413 and from 504 to 380 for all and nonSSA countries respectively from 1995-2018 to 2014-2018. Bond spreads also decreased from 999 to 561 for SSA from 1995-2018 to 2014-2018. WB data transparency among SSA countries are lower than those of non-SSA countries, especially in the area of adherence to international standards and methods in the production of data. On the other hand, the IMF data transparency ‘standard compliance’ among SSA becomes lower than that of non-SSA countries from 1995-2008 8