12 (EMs) and other developing countries (ODCs). Our interest is in whether, within each of these groups, the levels of certain variables are associated with differences in average growth rates. Table 1 compares unconditional and conditional growth rates over the period 1975-2004 for countries that are above or below the within-group sample medians for different variables that have been posited as threshold variables. After sorting countries within each group by these group-specific thresholds, we then report cross-sectional averages within each cell.

There are three main results that can be gleaned from this table. First, EMs, which are more integrated into international capital markets than ODCs, have a higher average growth rate than ODCs over the period 1975-2004, but this effect becomes smaller when we control for other standard variables that influence growth. Second, unconditional growth rates in EMs are greater for those countries with higher (within-group above-median) levels of the illustrative threshold indicators for financial depth, trade openness, institutional quality, regulation and macro policies, although this difference is not always statistically significant. These effects are less pronounced in ODCs, except that the institutional quality threshold is even more important for ODCs than for EMs. The picture is less clear when looking at overall development and financial openness as threshold variables. Growth rates are higher for countries with lower initial GDP per capita, reflecting convergence effects. In both groups, growth rates are higher for countries with lower relative financial openness.

Third, for conditional growth rates the patterns are less pronounced, although the positive association of growth with higher values of certain threshold variables persists (e.g., private credit, trade, reduced regulation and lower inflation variability among EMs). Table 1 also suggests that the difference between the growth rates of EMs and ODCs is generally more pronounced at higher levels of the threshold variables (except for institutional quality, GDP per capita and financial openness). These stylized facts are suggestive of systematic threshold or conditioning effects in the relationship between financial openness and growth. We now turn to a more formal empirical analysis of these effects.

# _V.2 Basic empirical analysis_

Our regression analysis is based on five-year averages of the underlying annual data. We begin with a limited set of controls that have been identified in the literature as being relatively robust determinants of long-term per capita GDP growth—initial income (at the start of each five-year period), which picks up convergence effects; the level of investment to GDP; a proxy for human capital; and population growth.

We report the results of baseline growth regressions using these controls in the first panel of Table 2. The first column shows the results of OLS regressions with country fixed effects (FE). The population growth rate does not seem to matter for medium-term growth. However, when we switch to generalized method of moments (GMM) estimation to deal with endogeneity issues (column 2), only the level of investment remains statistically significant. Nevertheless, we retain these four controls in the first stage of our analysis. FE and GMM are the two basic specifications that we will build upon in our further analysis.<sup>12</sup>

> 12 Both specifications always include time effects to capture common factors affecting growth across all countries in each five-year period.