# IV. **Some Econometric Results:**

The pricing model developed in this article offers an intuitive perspective on the relationship between default risk premium on foreign currency loans to projects in emerging market economies and the country and project risk attributes. To provide evidence on this relationship, we compiled data on a sample of infrastructure projects having reached financial closure between 1994 and 1996 in developing countries. Out of a sample of 68 greenfield infrastructure projects, we were able to compile detailed information regarding financing mixes, terms, size, sector, and country for only 26 of them, which provide the database for our empirical analysis (for more details on the larger project database, see Dailami and Klein, 1997). This information was compiled from published industry trade sources and communication with major developers, investment banks, export credit agencies, ECAs, and multilateral financial institutions.

We measure the credit risk premium as the spread over a benchmark (e.g. LIBOR or US treasuries) of foreign currency private loans to infrastructure projects.<sup>14</sup> We focus on private dollar-denominated loans advanced to projects in the core infrastructure sectors of power, road, telecom, transport, and water in emerging market economies. Since most projects had more than one private foreign currency loan, the spread used was the weighted average of constituted loans. As summarized in Table 4, the spreads vary both across countries and projects, within a range of 100 bps to 530 bps, with higher spreads associated with loans to road projects.

We relate this measure of the risk premium to a set of macroeconomic and project-specific explanatory variables, through a simple linear regression analysis (OLS). The main macroeconomic variables include: rate of inflation, GDP per capita, and ratios of external debt to exports, short-term debt to foreign exchange reserves, and reserves to imports. On the project side, we have included project size, leverage ratio, and a set of dummy variables to distinguish projects in different sectors. Table 6 reports the main regression results under various model specifications, which we view as tentative, due to the limited number of observations.

In interpreting our regression results, the dominant role of inflation in explaining the spread on foreign loans to infrastructure projects emerges with clarity. The estimated coefficient for inflation is statistically significant in all equations. Furthermore, there is evidence that countries with domestic inflation exceeding a benchmark rate have been further penalized -- including a dummy variable for countries with domestic inflation rates higher than 50% per anum proved to be statistically significant. The estimates for other macroeconomic variable, with the exception of short-term debt to foreign exchange reserves ratio (which seems to be weakly significant), are not statistically significant. This appears to be mostly due to data limitation rather than reflecting a genuine economic phenomenon. With regard to project-specific variables, it is evident that road projects have commanded the highest risk premium, followed by power projects.

> 14 Dummy variables are included to capture differences in the magnitude of risk premia for two types of benchmarks: LIBOR and US treasuries.

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