based on Gleditsch _et al._ (2002) and drawn from version 4 of the _UCDP/PRIO Armed Conflict Dataset_ , hosted within the Uppsala Conflict Data Program.<sup>9</sup> Whilst these variables constitute the key controls for the analysis, other variables will be controlled for to test the robustness of the results. Following Djankov _et al._ (2006), additional controls include general government consumption as a percentage of GDP and regional dummies for Africa, Latin America, and East Asia.<sup>10</sup> Overall, the dataset will cover the period from 2004 to 2009. Since this largely coincides with the global financial and economic crisis, two additional variables are included to capture countries’ exposure to it: exports and FDI inflows, both as a percentage of GDP. Government consumption, exports, and FDI data were obtained from the _World Development Indicators_ .

The empirical specification follows Djankov _et al._ (2006) and the model to be estimated is expressed as follows: , where _PERFORM_ represents the three measures of economic performance; is the intercept, and are variable coefficients, _RANKING_ represents the individual DBI measures, _X_ is a vector of control variables, and is the error term. Statistical estimation will be based on Ordinary Least Squares (OLS).<sup>11</sup> All analyses will use robust standard errors to account for heteroskedasticity.

Generally, the results for the 10-year average are stronger. This is consistent with the findings of Djankov _et al._ (2006). The magnitude of the coefficient estimates also compares closely to Djankov _et al._ (2006); in the case of table 2, the difference between 5-year and 10-year average growth is most likely explained by the financial and economic crisis which has generally lowered GDP growth during 2007 through 2009. The coefficients are consistently positively signed, indicating that a better investment climate is associated with higher economic growth. Except for model 1, which only controls for initial log GDP per capita, the DBI coefficient is statistically significant at the 1% level of higher. The results are robust to controlling for initial GDP per capita, the initial deviation from the GDP deflator, to civil war, FDI, and exports.

> 9 Civil conflict, as in Djankov _et al._ (2006), is defined as internal conflict with more than 1,000 casualties.

> 10 Data were also obtained for primary school enrollment and secondary school enrollment. As in Djankov _et al._ (2006), these variables are consistently insignificant – they do, however, significantly reduce the sample size. They will thus be omitted from the analysis presented here.

> 11 Djankov _et al._ (2006) also use an instrumental variable approach to test the robustness of their results. Generally, their results uphold with slightly larger coefficients and larger standard errors. Thus, endogeneity bias seems to be relatively negligible. To test the robustness of the results in this paper, the same instruments were obtained, including a country’s legal origin, its principal religion, the percentage of its English speaking population, and its absolute latitude. As in Djankov _et al._ (2006), the results generally uphold. This paper will thus focus on the presentation of the OLS findings.

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