concern given the distribution of the main dependent variable.

The fundamental determinants regressions were run using: (a) instrumental variables (IV); and (b) system GMM. Since both of these methods are designed to address endogeneity issues, we are more confident about attributing causality in these cases. The IV estimates are performed for the 1999–03 cross section, and deployed using the following instruments: for institutions, settler mortality (IV-1) and fraction of European-language speaking population (IV-2) (Acemoglu, Johnson & Robinson 2001);<sup>14</sup> for integration, the gravity-predicted trade volume (Frankel & Romer 1999); for human capital, historical enrollment data from 1900 (Glaeser, La Porta, L ́opez-de Silanes & Shleifer 2004); and for democracy, the predicted level of democracy (Decker & Lim 2008). Both geography and social capital were treated as (plausibly) exogenous.

The same set of instruments is used for the dynamic panel estimates. Here, we exploit additional information available from the panel structure of the dataset to draw additional insight. We choose system GMM as the preferred estimation method, for two reasons: first, since we only have instruments available for one period, panel IV could not be used; second, the inclusion of geography as a primary time-invariant determinant rules out the use of difference GMM.

# **4.2 Benchmark results**

Table 4 reports the results of the benchmark regressions for the proximate correlates of the aggregate growth polarity measure, computed from real GDP growth rates and excluding migration flows. The first three columns report the pooled OLS ( _P1_ ), fixed or random effects ( _P2_ ), and system or difference GMM ( _P3_ ) estimates for the augmented Solow specification proposed in Mankiw, Romer & Weil (1992). The following three columns, ( _P4_ )–( _P6_ ), report coefficients from the same three estimation methodologies using a full specification that includes the major proximate variables of importance as identified by Sala-i-Martin _et al._ (2004).<sup>15</sup> The benchmark results from the parsimonious specification given in ( _P1_ )–( _P3_ ) suggest that education, as captured by enrollment rates, may be an important correlate to growth polarity. Were this specification correct, educational levels would be positively related to the strength of an economy as a global growth pole. While this may be the case, there are reasons to believe that education may be capturing the effect, more generally, of per capita incomes (even after accounting for possible endogeneity via specification ( _P3_ )). This can be seen in specifications ( _P4_ )–( _P6_ ), where the inclusion of GDP per capita results in the enrollment variable falling out of statistical significance. Overall, the fit for the full specification is stronger, and postregression diagnostics for the dynamic panel specifications ( _P3_ ) and ( _P6_ ) attest to the general validity of these estimates.<sup>16</sup>

> 14The latter set of instruments, although somewhat weaker, are often preferable because of greater data availability, which consequently increases the otherwise fairly small sample size.

> 15More precisely, the specification was chosen with an eye toward maximizing sample size and scope. Additional specifications that include variables that compromised this criterion are considered in the annex. 16Residual diagnostics also suggest that concerns about the distribution of the dependent variable were generally

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