Corruption and economic development in energy-rich economies

Comparative Economic Studies, June, 2009 by Yelena Kalyuzhnova, Ali M. Kutan, Taner Yigit

Regarding our proxies for energy abundance variables, the interaction of Primx with proven oil reserves shows that resource-abundant countries with higher levels of oil reserves are likely to become more corrupt. The results for the principal component for oil and gas-related measures, PC1, have the opposite effect on corruption ranking, suggesting an increase in energy production and reserves alone causes improved rankings in corruption.

Finally, proven oil reserves have a very significant additional effect beyond that captured by the PC1 variable alone. By itself, this variable causes a reduction in the corruption level and leads a country to drop down 14 levels in the corruption rankings. Because the PC1 variable captures both production and reserve effects, the results suggest that they play an important role together in determining the corruption ranks in energy-rich economies.

Note that it is the interaction between Primx and energy reserves, which causes a higher level of corruption. That is, an increase in Primx x oil reserves variable brings about a worsening in corruption rankings, reflecting the 'resource curse' effects. Higher levels of energy production and reserves themselves may, on the other hand, capture improvements in per capita GDP levels because of higher energy production and stocks, hence lowering the corruption level. The estimated model is able to explain about 74% of the cross-country variation in corruption.

We now discuss the results for the growth equation. Traditional variables such as openness, democracy and FDI do affect the growth rate of GDP per capita in energy-rich economies. The FDI variable has the most significant impact on growth: a 1% increase in net FDI flows/GDP brings about a 0.28% increase in the GDP per capita growth rate. Government consumption has a negative impact on growth, perhaps capturing some crowding out effects. Infrastructure has a positive contribution to the growth rate. Education has an unexpected sign, which may be due to low variation in the sample. The corruption variable itself indicates that countries with high corruption tend to have lower growth rates. Both energy abundance variables, Primx x oil reserves and PC1, have the same negative effect on growth, much as in the corruption equation. An increase in energy production and reserves reduces the growth rate based on the coefficient of the Primx x oil reserves variable. The estimated model is able to explain about 41% of the cross-country variation in the growth rate.

Corruption and GDP per capita level regressions

Table 4 reports the level regression results where the level of GDP per capita is the dependent variable in the second equation. Here, the results showed two-way causality and the estimation is based on two-stage weighted least squares. As in Table 3, the instruments used in Table 4 are all the exogenous variables including interactive terms and we report the best-fitting model going from a general to a specific model, looking at the contribution of each variable to the adjusted [R.sup.2].