development to measure the development gap.<sup>12</sup> We present models for all countries and for low and middle income countries separately in Table 3. The latter are more reflective of the African experience.

# **A. Benchmarking Results for the Determinants of Financial Development**

The results for regression Equation (1) are presented in Table 3. The signs of almost all of the coefficients are as predicted and many of them are significant. When we use the full sample of countries and the limited set of regressors as a benchmark, (see Columns 1 and 5), only the Per Capita Income Variable is positively associated with both indicators of banking development, while our proxy for Natural Resource intensity is negatively linked to those indicators. Population Density and Offshore Financial Center are significantly positive in the liquid liabilities regression (Column 1) but not the private credit regression (Column 5). When we benchmark to a sample of only low and middle income countries, using the limited set of regressors (Columns 3 and 7), the Offshore Financial Center is highly significant for both indicators, while the Per Capita Income and Population Density variables are positive and significant, but only in the private credit regression.<sup>13</sup> The expanded regression results, where we include macroeconomic, institutional, and other explanatory variables, are presented in Columns 2, 4, 6 and 8. The results show that Real Growth is negative and significant (or very nearly so) for both private credit and liquid liabilities for both samples of countries that we use. This indicates that the fastest growing countries tend to have the

> 12  The same approach has been used, for example, to assess whether the levels of foreign direct investment received by China are abnormally high, or whether they can be explained by fundamentals (Fan, Morck, Xu, and Yeung, 2008).

> 13  These results are similar to those in Beck _et al_ ., (2008). Aside from our proxy for natural resource intensity, our regressions differ from theirs in two ways. First, we use the more conservative cross-sectional approach, whereas they use the full panel. We choose to do this because errors from observations from the same country are likely to be correlated with one another. This could artificially deflate standard errors, thus increasing the significance level of coefficients. For robustness, we ran our models also on the full panel and qualitative results are similar. Second, they include a poverty gap variable in their regressions, which is the proportion of the population under the poverty line, times the average distance from the poverty line (Source: Povcal Net, World Bank _)._ Since the poverty gap is so tightly linked to income levels, they use the residuals from a regression of the poverty gap on income in their regressions. We did the same, but the variable provided little explanatory power and reduced our sample. Also, unlike the other variables in our analysis, the poverty counts are based on surveys that do not occur at regular intervals, and thus the panel is highly unbalanced. For these reasons, we present models without the poverty gap variable.

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