Inequality in total natural resources rents (% of GDP) among post-Soviet countries can be attributed to the uneven distribution of natural resources across the region.Some countries possess substantial resource endowments, which significantly contribute to their GDP, while others have limited or less valuable natural resources. This disparity can lead to economic inequality both within and between countries. Resource-rich post-Soviet countries like Russia, Kazakhstan, and Azerbaijan benefit from their abundant oil, gas, and mineral reserves. As a result, natural resources rents make up a significant percentage of their GDP. However, this dependence on natural resources can create an economic imbalance and lead to a phenomenon known as the “resource curse.”. The resource curse refers to the paradox where countries with abundant natural resources often experience slower economic growth, higher levels of corruption, and weaker institutions compared to countries with fewer natural resources (Basnet et al., 2016).

Figure 1 shows clearly that in the available data, the distribution is skewed with strong outliers, indicating significant inequality between post-Soviet countries when it comes to the accessibility of natural resources in order to increase their GDP. A similar distribution can be seen between the countries when investigating the Environment indicators of the ESG data. While the majority of data in Figure 2 shows CO2 emissions in metric tons per capita below 5, some reach higher than 15 metric tons per capita. Looking at the renewable electricity output in Figure 3, the gap is even wider, with some countries deriving close to none electricity from renewable sources while some reach up to 100%.

A similar picture can be seen when analysing the Social aspect of the ESG data. Figure 4 shows that range of life expectancy at birth, with some countries reaching as low as 60 and some up to 75. Looking at the poverty headcount in Figure 5, the the differences are similiarly strong ranging from almost 0% to 50%.

The Governance Indicators of the ESG data, show in Figure 6, shows that GDP growth is fairly normally distributed between 0% and 10%. Figure 7 shows that control of corrupation estimate for the majority of the data is negative, meaning corruption is increasing.

In the next section, the aim is to derive a comparison between the countries in term of their natural resources rents and their performance in the ESG indicators.

Environment

In Figure 8 and 9, the development over time in regards to Total natural resources rents as well as the CO2 emissions per capita can be compared. Figure 8 shows, that there is an overall contraction across all countries in the observed time-frame, with a small increase in 2018 when it comes to the % of total natural resources rents that contribute to GDP. Meanwhile in Figure 9 it is observable, that the CO2 emissions per capita in metric tons, have increased in Russia, Turkmenistan and Kazakhstan while the rest of the post-soviet countries remained on the same level of emission, with the exception of Ukraine who decreased strongly after 2013.

In Figure 10, it is observable, that the countries which have higher “Total natural resources rents (% of GDP), have lower Renewable electricity output (% of total electricity output). The top three countries in terms of natural resources rents, which are, Turkmenistan with 18%, Azerbaijan with 15% and Russia with 10%, have only 0%, 7% and 16% in electricity output done by renewable energy sources in 2015 (the year 2015 was selected as it is the most recent year that has data for all 10 countries in terms of renewable output). Meanwhile the three highest countries in terms of renewable energy output are Tajikistan with 98%, Georgia with 78% and Armenia with 28%. These countries only produce 2% (Tajikistan) and 1% (Georgia and Armenia) of the their GDP from natural resources rents.

Social

In order to compare the relationship between the natural resource richness of the post-soviet countries with the social pillar of the ESG, the mean life expectancy at birth (years) is calculated per country for the whole observation time frame. The same is done to the Total natural resources rents (% of GDP). In Figure 11, the observations show that, the higher the % of the natural resources rent at the GDP in a country, the lower the life expectancy at birth. Armenia, Georgia and Belarus have the lowest total natural resources rents and show the highest life expectancy, while Turkmenistan, who has the highest %, shows the lowest life expectancy.

The same approach was taken for Figure 12, where the mean rates of total natural resource rents and poverty headcount ratio at national poverty levels were calculated for the available time frame. The data shows, in contrary to Figure 11, that countries with lower natural resources rent tend to worse, with a higher % of population living at national poverty lines (Georgia, Armenia and Tajikistan).

Governance

The Governance pillar of the ESG indicators is analysed by looking at the GDP growth (annual %) of the post-Soviet countries. The GDP growth is averaged over the observation period 1997-2020. Figure 13 shows, that Azerbaijan, Turkmenistan and Tajikistan are the top three countries when it comes to GDP growth.

If compared to figure 14, the top two countries in terms of GDP growth - Turkmenistan and Azerbaijan, are historically also the countries with the highest natural resources rent.But compared to Figure 8 and Figure 9, the dependency on natural resources has drastically reduced in the last 10 years.

Analysing the corruption estimate of the post-soviet countries, we can see from Figure 15 that historically, countries with lower natural resource rent do better (Georgia, Belarus and Armenia) in terms of corruption estimate. But looking at the time-series of Figure 16, it shows that these countries do exceptionally better compared to the rest in the last 10 years and are also historically the three countries with the lowest natural resources rent (see Figure 14).

In order to get a deeper understanding on how all the indicators correspond to each other. It creates a matrix of scatterplots, where each variable is plotted against every other variable. It creates correlation coefficients, which can be usefull to assess the strength and direction of a relationship between variables. In Figure 17, we can see that, the correlation between natural resources rent and corruption is negative significant (the higher natural resources rent the higher corruption), strongly positive with GDP and also with poverty. In this plot the relationship between the indicators can be further explored, for example poverty and life expectancy are strongly negativ correlated to each other, meaning the poorer a country, the lower the life expectancy.