Table II: Pairwise correlations
Description
Table II shows the Pearson correlation analysis between determinants of Energy use per capita, credit to the private sector as a percentage of GDP, CO2 emissions, income inequality (Atkinson coefficient, Gini index, and the Palma ratio), and economic growth. The correlation matrix showed vital evidence of the relationship between the determinants of energy use per capita, CO2 emissions, credit to the private sector as a percentage of GDP, income inequality indicators (Atkinson, Gini and the Palma ratio), and economic growth variables by providing the direction and strength of the relationship among variables of the study of this section. The size of the correlation coefficient ranges from a negative one (-1) to a positive one (+1). A correlation coefficient of positive or minus one (-/+1) represents a perfect correlation between the variables. If such a condition persists, it raises the problem of multi-collinearity. However, if the correlation coefficient is close to zero (0), it suggests that the variables are linearly independent, and multi-collinearity will not be an issue. In addition, a correlation coefficient less than 0.5 is generally considered acceptable. The table reports that all the correlation coefficients are weakly correlated, showing correlation coefficients below 0.5 in most cases. The study also shows that most of the correlation coefficients are statistically significant 10%. Theoretically, the study expects a positive relationship of Energy use per capita, credit to the private sector as a percentage of GDP, and CO2 emissions per capita; the results meet this expectation. Similarly, the study expects energy consumption per capita to be positively correlated with a net inflow of foreign direct investment, remittances % GDP, and trade % GDP. A negative relationship runs from the inequality variables to energy consumption per capita. The weak correlation between the variables indicates that the study is insulated from the problem of multi-collinearity.