Global income inequalities include both inequalities between countries as well as inequalities within countries, which are affected by market dynamics, but also by waves of ideological change and shifting policy doctrines, such as the Reagan reforms in the United States (US), the transition from plan to market in China and the former Soviet Union countries and the deregulation in India ([4]).

There are various indicators and methodologies to measure inequalities. The Gini coefficient is the most widely used index that counts what percentage of a country’s income each percentile of the population possesses and is converted into an index of 0 to 1, with 0 implying complete equality and 1 implying complete inequality ([20]). The Theil index, on the other hand, is a measure of the discrepancy between the distribution of income and the distribution of population between groups ([14]) and varies between zero and ∞, with zero meaning an equal distribution and higher values a higher level of inequality ([34]).

More recent estimations include the S80/S20 income quintile share ratio that measures the ratio between the share in total national income of the incomes of the richest 20% (the top quintile) against the share in total national income of the incomes of the poorest 20% (the bottom quintile) ([20]), the S40 ratio that counts the share in total national income of the poorest 40% and is used also with the United Nations Agenda 2030 and SDG 10 ([20]), or the Mean Log Deviation (MLD), which is a measure of income inequality as well; when it is zero everyone has the same income and when it has larger positive value incomes become more unequal ([24]).

As it is evident, using different indicators provides slightly differentiated results. The Gini coefficient has been criticised as being too sensitive to changes in the middle class, therefore a fairly new indicator has been proposed, the Palma ratio ([12]). What is important about this indicator is that it captures both the lower and upper ends of the income distribution and is calculated dividing the richest 10% of the population’s share of income by the poorest 40% share ([38]; [37]). One criticism of the Palma ratio is that it implies a relative stability in the middle class, which, however, has been reaffirmed by further research with data on both developed and developing countries ([13]).

Despite the existence of several inequality indicators, most SDGs goals are not likely to be met, such as the Goal of alleviating extreme poverty by 2030, while at the same time the COVID-19 pandemic is expected to increase poverty as well as inequality worldwide ([26]). The existence of inequalities is not economically efficient and concentrates consumption in the top income quintile ([36]), affecting at the same time the use of resources like labor and capital ([39]). As a result, several papers have identified a negative impact of income inequality on economic growth ([8]; [50]; [43]).

Some researchers predict a decrease of global income inequality by 2030 as the young generation from developing countries becomes more educated ([2]). On the other hand, [41] states that the basic forces in a capitalist economy tend to increase inequality, unless unexpected disasters such as wars occur.

Figure 1 presents the share of inequality between countries and the share of inequality within countries estimated with the Theil index by [9]. The index is estimated for the period of two centuries (1820-2020) and shows the shift in importance in different sub-periods between the two components of global inequality. At the end of the period, between country inequality is about 30% of total inequality due to the rapid growth of emerging economies like China and India. This growth has led to a smaller development gap, but has also widened the urban-rural divide within those countries, leading to increasing levels of within country inequality ([5])

Figure 1. Global income inequality composition, 1820-2020 ([10]).

Inequality has varied across countries over the years. In the early 20^th century, most of the inequality observed was attributed to within countries inequality (about 70%), whereas in the early 21^st century within countries inequality was about 50% and rose again to 68% in 2020 ([31]; [9]; [10]). Therefore inequality between countries is decreasing ([6]), due to the strong economic growth in China and other emerging economies ([28]). However, the gap between countries remains remarkable, as for instance, the average income of people in North America is 16 times higher than that of people in Sub-Saharan Africa ([46]).

The economic growth of China and a few other countries has moved people out of poverty and towards the middle class, but in reality this only affects a small number of countries with a large population, thus inequalities still continue to exist to a large extent ([22]). At the same time the increasing number of countries that have been added to the world since 1950, has affected the between countries inequality as well ([33]).

Distinguishing between these two types of income inequality is essential as different drivers are involved and different policies are needed to address them. However, differences in economic growth are highly related to between country inequality and imply that poorer regions or countries would need to grow at a faster rate than the richer ones ([29]). Inequality between countries is still very high in absolute terms and reducing average income disparities between countries is equally important ([10]). The majority of projections predict that between countries inequality will continue to decrease, but they do not take into account climate change, which could reverse the situation ([44]). For instance the pandemic caused the largest rise in income inequality between countries over the past three decades ([48]), indicating that unexpected shocks may affect the actual evolution of between countries inequality.

In this section, inequality between countries is analysed with the use of World Bank (WB) data, using diagrammatic analysis in order to explore the main trends and patterns of change at the global level and across geographical regions and income groups. Data for 159 countries have been used representing 96.9% of the global population and comprising of all income categories as follows: 53 (33.3%) high income, 42 (26.4%) upper middle-income, 42 (26.4%) lower middle-income and 22 (13.9%) low income countries. Also all geographic areas are represented as well, 18 countries from East Asia (11.3%), 45 from Europe & Central Asia (28.3%), 25 from Latin America (15.7%), 18 from Middle East & North Africa (11.3%), 2 from North America (1.3%), 7 from South Asia (4.4%) and 44 from Sub-Saharan Africa (27.7%). Both these classifications come from the World Bank¹ and are calculated using the WB Atlas method.

Using the above data, Figure 2 and Figure 3² present the evolution of the Coefficient of Variation (CV), the weighted Coefficient of Variation (CVw), the top10/bottom10 and top10/bottom50 ratios for every 5 years from 1990 to 2022 using WB data. At the global level, inequalities measured by the CV and the CVw tend to slowly decrease³, but stabilize in the 2015-22 period. The top10/bottom10 ratio is following some fluctuations, increasing, decreasing and then increasing again in the 2015-22 period, ending up with a higher figure in 2022 compared to 1990. The top10/bottom50 ratio is slowly decreasing but it stabilizes in the latest period. The figures at the global level are shocking, whereas the top10/bottom10 figure for 2022 indicates that the 17 more advanced countries have an average income per capita that is 109 times higher than the per capita income of the 17 poorer countries in the world and 26 times higher than the poorer 50% of the planet.

Figure 2. Between countries levels of inequality at the global level, 1990-2022.

The geographical regions with the higher between countries income inequalities in 2022 are the Middle East and North Africa and South Asia. All areas experience declining figures of the CVw, with the exception of North America, which, however has the lowest level of inequalities. Inequalities between countries in 2022 are higher in the low-middle income group and lower in the low income group. During the first two decades the low income group had the second highest level of inequalities. All income groups have experienced a decline of between countries inequality, with the exception of the high income group that has about the same level of between countries inequality in the entire period.

Overall, inequalities between countries are extremely high and are an important contributor to global income inequality. Inequalities have overall declined during the last three decades (the CVw dropped from 130.5 to 110), as a number of lower-middle and low income countries have improved their growth performance significantly. However, the gap between the advanced Global North and the Global South is enormous and triggers massive migration movements and instability in both the origin and destination countries ([15]).

Figure 3.Inequalities between countries by geographical regions and income groups, 1990-2022.

The existing pace of declining inequalities at the global scale may require half a century, or even more before they reach the level of inequality of the more homogeneous advanced income group (CVw equal to 60), which is still high, but tolerable. Even if existing trends will continue, which is not a sure bet given the fluctuation of the top10/bottom10 index, the challenging question is, if the global economy with the existing demographic and geopolitical balances has the luxury to wait for another 50 or more years with these or similar levels of inequality.

Within countries inequality still holds a prominent position, as 71% of the population lives in countries where inequality has increased ([48]). [35] reports that between 2005 and 2012 a great increase in within country inequality was noticed, but since then, inequality has remained fairly constant. Other studies suggest that around 60% of the developing countries are experiencing an increase in within-country inequality ([7]), which tends to increase partly due to the fact that the declining incomes of India were not offset by the rising incomes in China ([18]).

The main factors that appear to have a negative impact on within-country income inequality include the share of industry in GDP, access to electricity, low investment costs, trade globalisation, financial globalisation, higher age dependency ratio, higher mortality rate, while unemployment seems to have a positive effect ([37]). Especially regarding financial development, some evidence indicates that it contributes to economic development and benefits the poor ([1]), while other studies show that financial development, financial liberalisation and banking crises lead to higher income inequality ([17]). A meta-analysis conducted by [25] shows that financial globalisation increases inequality in both developed and developing countries. In terms of globalization, the Stolper-Samuelson mechanism predicts that global integration increases income inequality within developed countries and decreases inequality within developing countries ([19]; [45]).

Using data from the World Inequality Database (WID)⁴, the WB and the UN the evolution and current status of global income inequalities worldwide is mapped, taking into consideration the major crises that have affected the global landscape. In this analysis 175 countries are examined, for years 1980-2022, which represent 99.4% of the global population and are comprised of all income categories as follows: 55 (32%) high income, 45 (25.7%) upper middle-income, 49 (28%) lower middle-income and 25 (14%) low income countries. With regards to their location, 22 countries from East Asia (12.6%), 50 from Europe and Central Asia (28.6%), 26 from Latin America and Caribbean (14.9%), 20 from Middle East and North Africa (11.4%), 2 from North America (1.1%), 8 from South Asia (4.6%) and 47 from Sub-Saharan Africa (26.8%) are included. Figure 4 presents the income shares of the top 1%, top 10% and bottom 50% for 2022 at the global, income and regional level.

It can be noticed that in all income groups in 2022 the top 10% of population holds almost 50% of the total income, apart from the high income group, where the top10% holds almost 40%. On the other hand, the share of income of the bottom 50% of the population, is about 14%, with Upper middle income countries having the lowest figure (12%) and high income countries having the highest (17%). In terms of geographic regions the top 10% of the population with the highest income share is located in Latin America & Caribbean, Sub-Saharan Africa and the MiddleEast and North Africa regions. On the other hand the bottom 50% of the population with the lowest income share are in Latin America & Caribbean, while those with the highest income share are in Europe & Central Asia. Table A1 and Table A2 in the Appendix provide the relevant figures for each income or regional group.

Figure 4. Income shares of different income and geographic groups worldwide, 2022 (Data from WID).

Figure 5 presents the evolution of top and bottom shares at a global level for a period of 43 years. The share of the top 1% and top 10% income groups rose until 2006, but dropped after that period and has been declining ever since. The diagram tends to suggest that in periods of high GDP growth, the income share of top 1% and top 10% increased⁵, whereas in times of recession the bottom 50% share increased slightly. In any case, the income shares of the top1 or top10 income groups are moving in the opposite direction than that of the bottom50. In doing so, we acknowledge that with development, structural adjustments tend to favor capital and high-skill clusters at the direct expense of the lower-income majority part of the population.

Figure 5. Evolution of GDP growth, top 1%, top 10% and bottom 50% income share worldwide, 1980-2022.

As already mentioned Gini is the most widely used inequality indicator. Figure 6 presents the evolution of Gini worldwide (green line) from 1980 until 2022 for the same WID data as previously presented. Gini has fluctuated throughout these years, with a high peak in 2005, a decline after that and an increase again from 2015. The biggest contributors to that increase are shown to be upper middle- and high-income countries in terms of income category; East Asia and Pacific, Europe and Central Asia, North America (especially this region) and South Asia are the main contributors in terms of regions. The highest Gini values in 2022 are noticed in low income and Latin America and Caribbean countries, while the lowest values are located in high income and Europe and Central Asia. Table A3 in the Appendix presents the relevant data.

To further investigate within country income inequalities, the Rich-to-Poor Ratio (RTP) is constructed which is a new indicator representing the ratio of the relative share of income per capita allocated to the top and bottom groups and is more suitable to measure extreme inequality. Figure 7 presents the RTP ratio estimated for the top richest 1% of the population over the bottom poorest 50% of the population (top1%/bottom50%), as well as for the top richest 10% of the population over the bottom poorest 50% of the population (top10%/bottom50%).

RTP_1/50 = [per capita income of the top 1% group]/

[per capita income of the bottom 50% group] (1)

RTP_10/50 = [per capita income of the top 10% group]/

[per capita income of the bottom 50% group] (2)

It can be noticed that RTP_1/50 has only slightly fluctuated and its value in 2022 is almost the same as in 1990. Its lowest value was noticed in 2019 and its highest in 1991. On the other hand RTP_10/50 has dropped about 12 points from 1980 to 2022 reaching its lowest value in 2017 and highest in 1991 as well.

Figure 6.Evolution of Gini according to income and geographic categories, 1980-2022.

Figure 7. Evolution of RTP_1/50 and RTP_10/50, 1980-2022.

Table 1 summarises the RTP_1/50⁶ ratio. The RTP ratio specifically focuses on the “super-rich” thus better capturing the drivers of wealth inequality rather than just income disparity; at the same time it focuses on the bottom 50% of the population, providing a clearer metric for social cohesion. In those regards it is superior to similar indices such as the Palma ratio, as it constitutes a precise measurement of how the gains from growth are exploited by the few at the top at the expense of the many (bottom 50%). A value of RTP equal to 72.8 in 1980 at the global level indicates that the average household in the top1% income group has an income that is 72 times the average household income of the bottom50% of the population.

The figures show that RTP has increased globally since 1980, reaching its peak in 1990. After that it declined for more than a decade, increasing again during the last years. The highest RTP values in 2022 are noticed in Upper middle income and South Asi (highlighted in red), while the lowest values are located in low income countries and North America.

Table 1. Rich to poor ratio globally (RTP_1/50), 1980-2022.

Source: Own estimates from World Inequality Database (WID).

As previously mentioned, the Gini coefficient is the most commonly used indicator for inequality which comes with its strengths and weaknesses. By utilising the same data from WID for the period of 1980-2022, the evolution of inequalities worldwide alongside the relevant GDP growth are examined. Figure 8 presents the overall picture at a global level evaluating the evolution of RTP and Gini to GDP growth. RTP decreased dramatically since the 2008 financial crisis and on the rise again since 2020. Gini has remained fairly stable throughout this period, potentially due to its sensitivity to changes in the income of the middle class, whereas RTP captures the upper and lower tails.

Figure 8. Evolution of GDP growth, Gini and RTP, 1980-2022.

Figures A1-A11 in the Appendix depict the same variables in the regional and income level. What can easily be noticed is that the RTP index is higher in Upper Middle income countries, followed by lower middle income ones. In terms of regions, Latin America, Sub-Saharan Africa and the Middle East and North Africa seem to be the most unequal ones. In high income countries RTP increased dramatically until 2008, whereas Gini also increased overall but with smaller fluctuations. On the other hand, RTP is at higher levels overall in Upper middle income countries, showing an increase until the early 2000s and a decrease after that with a high peak in 2019 and decreasing after 2020; Gini has remained fairly stable throughout this period. In Lower middle income countries again Gini is stable overall, whereas RTP decreases by about 20 points and showing a slight increase since 2020. Finally in low income countries RTP decreases by about 20 points as well and does not show a trend to increase in recent years. As Gini does not seem to fluctuate much, due to its construction limitations, it comes forward that RTP is a better indicator of inequality as it better captures the upper and lower quantiles.

Accordingly regional differences are also noticed especially in the evolution of RTP and its level. Countries in Latin America and Caribbean and Sub-Saharan Africa have the highest RTP globally. In all cases the COVID-19 pandemic led to a decrease in RTP, but since 2021 RTP has been increasing again. The most noticeable change in RTP can be noticed in Europe & Central Asia and North America, especially in the latter whereas RTP increased very sharply. On the contrary RTP decreased by about 25 points in Sub-Saharan African countries. Gini has remained more or less the same with marginal fluctuations during this 43-year period.