# 3. Data, model specification, and basic results

The sovereign debt data used in our analysis (spread-to-benchmark, bond S&P rating, tranche volume, years to maturity, jurisdiction of issue) were sourced from the Dealogic Bondware database. We consider all countries defined by the World Bank as low, lower-middle and upper-middle income, with the addition of the Republic of Korea and the high-income nations of Latin America, Africa and Eastern Europe, as long as they are not within the group of smaller economies viewed as tax havens. We work with a sample of 53 emerging market governments that issued dollar-denominated debt in these two jurisdictions between 1990 and 2015.<sup>8</sup> Following Ratha, De and Mohapatra (2011) we use a reversed rating scale (with 1 denoting the highest rating and 21 the lowest) to convert the letter S&P rating into a numeric grade (Table 4). Within the converted scale, an investment grade dummy identifies all bonds graded 10 or lesser. More series were added to control for the macroeconomic environment, sourced either from Bloomberg (the VIX CBOE expected volatility index and the bond-level collective action clause indicator) or from the World Bank World Development Indicators (annual GDP growth). For secondary market spreads data from the Bloomberg database are used.

The econometric model has spread to benchmark at launch as the dependent variable. The first set of explanatory variables are bond-specific ones, such as S&P rating, an investment grade dummy, the size of the initial tranche, the maturity, and a dummy identifying bonds issued under U.K. governing law. The next set of variables control for macroeconomic conditions, both global and domestic. We include here the VIX index of stock option implied volatility, and the one-year lagged GDP growth rate. Year dummies capture global macroeconomic conditions not reflected in these variables.<sup>9</sup> In addition, a set of regional dummies is included to capture the effect of long established geographic connections with a particular financial center and other region specific unobservables. Then, for a new bond indexed _i_ from country _j_ issued in year _t_ , the main specification becomes: Estimates are obtained by OLS with robust standard errors. The sample used in the estimation exercise covers the period 1990 through 2015. Because the dependent variable refers to a sample of sovereign bonds, the spread to benchmark variable is here identical to spread to treasuries.

In Table 5 we present the estimation results from the benchmark specification (equation 1). Most bond-specific and macroeconomic variables have expected signs. Post-crisis, S&P rating, VIX and governing law are significant. After 2008 the investment grade dummy becomes insignificant. Since we know that some regions have a stronger connection with Britain, we have included regional dummies. Even after controlling for the effect of regional linkages, the governing law dummy coefficient remains large and significant in the post-crisis sample.

> 8 Each observation is a tranche rather than a bond. This creates difficulty since tranches issued after the initial launch do not properly reflect issuance characteristics. Conversely, whenever several tranches are issued at the same time, their characteristics are usually similar, so that multiple records are created for the same bond. We addressed this by including in our sample only the first tranche, and, whenever more than one tranche was issued on the first day, by including the largest one only.

> 9 We use standard explanatory variables such as the maturity and size controls (Eichengreen and Mody 2004) and bond ratings (Jaramillo and Tejada 2011).

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