dealing with heterogeneity in growth regressions (see, also, Durlauf, Kourtellos, and Minkin 2001), Quantile Regressions provide estimates of the coefficients across the conditional distribution of the sample, for example, by allowing the bottom 25% of observations to be below the predicted values, while at the same time estimating the coefficients conditional on having 50% percent of the sample above and below the predictions, as well as the top 75% of the sample. If the coefficients are sufficiently different, then we can conclude that resources have distinct effects in different country contexts. The regression errors from each quantile are allowed to be correlated and they are bootstrapped (with 100 interactions of random sub-samples), because they are unknown a priori.

The discussion of the Quantile Regressions is followed by the presentation of 3SLS estimations of the basic and the fuller specifications of (9), but adding the AcemogluRobinson institutional variable. We explore the role of institutions, namely constraints on the executive branch, which could be affected by the relative abundance of natural resources according to the resource curse hypothesis. We follow the relevant literature on institutions and growth by complementing the static GDP per capita function with the population densities of countries circa 1500 as the identifying instrumental variable of the 3SLS estimator, which is the approach of Acemoglu and Robinson (2002, 2005) and Glaeser et al. (2004).<sup>13</sup> 3SLS allows for the simultaneous estimation of the growth and institutional equation, with different explanatory variables in each structural equation. For example, imports of natural resources per worker appear in the growth equation, but not as a determinant of executive constraints.

# **VI. Data and Bi-variate Correlations**

Standard publicly available sources offer all the necessary data to implement our empirical strategy. The relevant variables are averaged over the 1980-2005 period. Table A1 in the Appendix lists the variable definitions and sources.

# _VI. A. Variable definitions and data sources_

The dependent variables are the real GDP per capita, its average annual growth rate between 1980 and 2005, and the average annual growth rate in each five year period. The data come from the latest version of the Penn World Tables, which has data until 2004. The year 2005 is derived by applying the real growth rate of GDP per capita in local currency and constant domestic prices between 2004 and 2005.

> 13 There are numerous potential pitfalls in the estimation of the GDP per capita in levels model with 2SLS even when we can conclude that the IV is correlated with the endogenous explanatory variable but not with the dependent variable. One is that the chosen instrument could be weak in the sense of explaining a very low share of the variance of the endogenous variable (executive constraints in this application); see Dollar and Kraay (2003) for a similar application. Another related pitfall is that the variance of the endogenous variable that explains the variance of the dependent variable should be related to the variance of the instrumental variable – see Dunning (2008). As discussed in the results section, these issues became unimportant since we did not find much evidence that executive constraint affects the underlying relationship between our proxy for relative natural-resource endowments and development.

13