Corresponding author: Leonid M. Grigoryev ( lgrigor1@yandex.ru ) © 2019 Non-profit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Grigoryev LM, Pavlyushina VA (2019) Relative social inequality in the world: Rigidity against the economic growth, 1992–2016. Russian Journal of Economics 5(1): 46-66. https://doi.org/10.32609/j.ruje.5.35485
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The study of economic growth and social inequality goes back to the works of S. Kuznets, A. Atkinson, P. Krugman, J. Stiglitz, T. Piketti, and B. Milanovic. Statistical analysis of social inequalities for a large set of countries, divided into seven clusters, was conducted for the period 2000–2016. The share of incomes of the 10th decile was used as a measure of inequality. The hypothesis of the positive impact of economic growth on the reduction of social inequality was tested. Stylized facts on an array of 106 countries for the period under review indicate a high degree of stability of the level of inequality in most groups, especially in the most developed countries, and in particular in the Anglo-Saxon ones. The distribution of key socioeconomic and even political indicators for clusters shows their strong relationship with the structure of cluster inequality. This makes it possible to significantly deepen the analysis, in particular the one concerning the stages of world development.
inequality, economic growth, clusters, income distribution
A great amount of theoretical and statistical research is devoted to analyzing social inequality, which remains a major challenge for most countries, both developed and developing. The question of social inequality’s impact on many aspects of economic activity and social life has been discussed by many academic researchers. The fact that this problem was first highlighted through Marxism seems to have long been an obstacle in the way of research, since the results of studies have often been used in political discourse. A few simple questions can be formulated regarding social inequality: its measurement and significance; origin; long-term and current trends; what it affects; how it is affected by economic growth and social policy; finally, whether it can be reduced and whether we should try to do it. We are going to focus mainly on the key question: does economic growth reduce inequality? To this end, we will consider important aspects of the problem: measuring inequality; actual trends observed over the past twenty-five years; and the connection between inequality and other socioeconomic indicators.
In our previous article, devoted to trends in inequality between countries, we used cluster analysis (174 countries divided into seven clusters
The question of social inequality became especially fascinating during the time of the Industrial Revolution, the emergence of the classical political economy, and the publication of the Communist Manifesto, in which Karl Marx demonstrated that he realized the importance of the middle class but predicted the disappearance of the “old” one (which was true) and did not foresee the emergence of a new, much more numerous one (
During the 20th century, economists continued to study the distribution of income and wealth. The traditional formulation of the question regarding the relationship between economic growth and inequality was elaborated upon in
Table
Shares of household income by quintile in India, Sri Lanka, the United States, and the United Kingdom, 1950 a) and 2016 a) (%).
Country | 1950 b) | 2016 c) | ||||
Total of 1st, 2nd, and 3rd quintiles | 4th quintile | 5th quintile | Total of 1st, 2nd, and 3rd quintiles | 4th quintile | 5th quintile | |
India | 28 | 17 | 55 | 35.5 | 20.6 | 44.0 |
Sri Lanka | 30 | 20 | 50 | 32.4 | 20.5 | 47.0 |
United States | 34 | 22 | 44 | 30.8 | 22.7 | 46.4 |
United Kingdom | 36 | 19 | 45 | 36.5 | 23.0 | 40.6 |
In the 40 years preceding the publication of Kuznets’s paper, the world had lived through two world wars, the Great Depression, and several revolutions (including the October Revolution in Russia). Therefore, Kuznets could not have observed long periods of growth except during the post-war years from 1947 through 1955, but with two crises in the United States.
Kuznets’s applied analysis was characterized by the limited statistics and facts on which he relied in discussing inequality. His hypothesis was largely based on the trends in inequality in the United States between 1913 and 1948. Therefore, he did not observe long periods of growth under any normal conditions. Paradoxically, his optimism with respect to reducing social inequality as a result of economic growth reflected the specific aspects of war and crisis periods. Of course, now we know much more about global social processes, as noted in
In his works, Kuznets noted very high inequality in the Anglo-Saxon countries, but took into account the capabilities of economic tools that could drive higher social inequality. The level of post-colonial inequality in India and Sri Lanka was exceptionally high in the early 1950s and resembled the situation in present South Africa. It should be noted that, in 65 years, the 4th and 5th quintiles retained great advantage in the U.S. and the UK, but in the two aforementioned developing countries the share of the 5th quintile decreased considerably, while the 4th one increased slightly.
Over more than 40 years ago, Anthony Atkinson, one of the most renowned researchers of inequality issues, showed in his works that today’s biggest problem was not that the rich were getting richer, but that the problem of poverty still remained unresolved (
In turn, Stiglitz wrote that inequality was usually associated with more frequent up and down cycles, which make the economy more vulnerable and unstable. Of course, the income gap between the poorest and the richest did not cause the 1920s crisis directly, although it was not a mere coincidence that the last time inequality was so high was just before the Great Depression (
Speaking of inequality, one cannot help mentioning Piketty’s “Capital in the twenty-first century” (
Critical reviews of Piketty’s book focus mostly on the insufficient understanding of the sources of statistical data and calculations, or on searching for processes neglected by the author. For example, The Financial Times economics editor C. Giles compared later data from household surveys with earlier data from tax assessments, and concluded that inequality was declining because the latter data were lower than the values for previous periods. However, this conclusion seems to be incorrect, as using two different sources of data on inequality (tax returns and household surveys) should allow for adjustments, because the figures on income differences obtained from tax returns are higher than those obtained from household surveys (
Many critics of Piketty’s work note that in his calculations, he did not take into account fixed assets depreciation, which reduces a company’s equity. This happens to be incorrect, as there is an adjustment for depreciation in his calculations.
Notably, the topic of social inequality also aroused extensive discussion among Russian economists. They include
According to our previous analysis, income inequality did not decline globally during an active economic growth period (1990–2011), i.e. global economic growth did not change the key parameter: the share of the 10th decile of the population in the incomes of developed and certain developing countries (
The combination of decreasing inequality and growing poverty during a recession is a complex phenomenon. High social inequality in moderately developed countries hinders the development of the consumer market and holds back domestic demand. Highly developed countries demonstrate signs of a weakening middle class (classes), and the aforementioned literature discusses the growing share of total income by the 10th decile and top-1% of the wealthiest people in the United States, other leading countries, and in the world. Notably, these calculations and debates concur with the statement that global inequality is decreasing between people over the world but remains quite high — at around 57% or 58% on the Gini index. We believe that the concept of diminishing global inequality (mostly due to the success of China and India) is an interesting one, inspiring certain (quite vague) hope for humanity. This approach enables us to discuss the nature of humanity’s development in the context of the UN’s Millennium Development Goals and Sustainable Development Goals. However, this concept has at least one flaw: it is inapplicable to any decision-making by the world’s economies on socioeconomic problems. The level of development of global civic society is still very low, while the interests of social strata and politicians (and especially the elites) are still very far apart.
The theoretical discussion regarding the scale and trend in social inequality produces a complex world picture that needs to be described at the level of stylized facts, lest research efforts be scattered due to inconsistencies between periods, different country groups, and methods. Various indicators are used to measure social inequality, the main ones being: the Gini index; the share of income earned by a certain portion of the population; R/P 10% ratio; the Atkinson index;
The most common and frequently used indicator for analyzing social inequality (including non-economic studies) is the Gini index. However, it has a number of serious drawbacks: most importantly, it fails to adequately reflect inequality at the ends of the distribution and remains unchanged if increases in the shares of income earned by the poorest and the wealthiest parts of the population are equal (
The hypothesis that we consider in this section is simple: if, during the period of significant growth in the world economy from 2000 to 2016, patterns and factors had been in operation that led to lower social inequality globally or in large groups of countries (in terms of a given indicator), then they would have been reflected in a reduction in the 10th decile’s share of total income. Recalculating income in a single unit of measurement (in international dollars — Int$, based on PPP) enables income level to be compared — GDP (PPP) per capita — across various countries and clusters. It is important that “cluster centers” based on the level of GDP (PPP) per capita (the average in countries or weighted average) vary by an amount that, in this case, is 1.7–2.0 times. A two-fold gap (e.g., in China) can be overcome in 10 years, provided that the advantage in growth rates is 7.2 p.p. Actual rates of convergence will be determined by the rates of economic growth and population growth. The main parameters, however, are the duration and sustainability of high growth rates.
According to our assumptions, when institutions are built within a country (
For the entire period from 2000 to 2016, we had access to comparable data from 92 countries (Table
We will now examine the trends in the shares of deciles and quintiles over the two periods. In the four Anglo-Saxon countries (on average), the fluctuation in the shares of income is insignificant and appears to be negligible. Accordingly, considerable economic growth was absorbed in proportion to these shares, and produced an amazing result by 2016: the imputed GDP (PPP) per capita (
Average share of income (%) and imputed GDP (PPP) per capita (thousand Int$ per capita) by country groups (92 countries), 2000, 2008, and 2016.
Country group by GDP (PPP) per capita (thousand Int$ per capita) | Share of income | Imputed GDP (PPP) per capita | |||||
2000 | 2008 | 2016 | 2000 | 2008 | 2016 | ||
1st decile | Below 10 | 2.3 | 2.5 | 2.6 | 0.6 | 1.0 | 1.2 |
Developing countries (10 to 25) | 2.2 | 2.4 | 2.5 | 2.1 | 3.4 | 4.0 | |
Over 25 | 3.2 | 3.1 | 3.0 | 10.7 | 12.4 | 12.4 | |
Anglo-Saxon countries | 2.5 | 2.5 | 2.5 | 9.2 | 10.5 | 10.7 | |
2nd decile | Below 10 | 3.5 | 3.8 | 3.9 | 1.0 | 1.5 | 1.8 |
Developing countries (10 to 25) | 3.5 | 3.7 | 3.9 | 3.5 | 5.4 | 6.3 | |
Over 25 | 4.9 | 4.9 | 4.8 | 16.3 | 19.3 | 19.4 | |
Anglo-Saxon countries | 4.2 | 4.2 | 4.1 | 15.8 | 17.6 | 18.2 | |
1st quintile | Below 10 | 5.8 | 6.3 | 6.5 | 0.8 | 1.2 | 1.5 |
Developing countries (10 to 25) | 5.7 | 6.0 | 6.4 | 2.8 | 4.4 | 5.1 | |
Over 25 | 8.0 | 8.0 | 7.8 | 13.5 | 15.9 | 15.9 | |
Anglo-Saxon countries | 6.7 | 6.7 | 6.6 | 12.5 | 14.0 | 14.5 | |
2nd quintile | Below 10 | 9.8 | 10.4 | 10.7 | 1.4 | 2.0 | 2.4 |
Developing countries (10 to 25) | 10.0 | 10.3 | 10.8 | 5.0 | 7.5 | 8.7 | |
Over 25 | 12.9 | 13.0 | 13.0 | 21.4 | 25.5 | 26.0 | |
Anglo-Saxon countries | 11.8 | 11.7 | 11.6 | 22.2 | 24.8 | 25.8 | |
3rd quintile | Below 10 | 14.0 | 14.6 | 14.9 | 2.0 | 2.8 | 3.3 |
Developing countries (10 to 25) | 14.4 | 14.7 | 15.1 | 7.3 | 10.7 | 12.2 | |
Over 25 | 16.9 | 17.0 | 17.2 | 27.9 | 33.2 | 34.2 | |
Anglo-Saxon countries | 16.4 | 16.2 | 16.3 | 31.0 | 34.5 | 36.2 | |
4th quintile | Below 10 | 20.6 | 21.1 | 21.3 | 3.0 | 4.1 | 4.8 |
Developing countries (10 to 25) | 21.2 | 21.3 | 21.5 | 10.8 | 15.4 | 17.3 | |
Over 25 | 22.3 | 22.4 | 22.7 | 36.6 | 43.7 | 44.9 | |
Anglo-Saxon countries | 22.6 | 22.6 | 22.9 | 42.9 | 48.3 | 51.1 | |
5th quintile | Below 10 | 49.8 | 47.5 | 46.6 | 7.2 | 9.1 | 10.3 |
Developing countries (10 to 25) | 48.8 | 47.7 | 46.3 | 24.8 | 33.8 | 36.9 | |
Over 25 | 39.9 | 39.7 | 39.4 | 64.9 | 76.6 | 77.4 | |
Anglo-Saxon countries | 42.7 | 42.9 | 42.6 | 81.4 | 92.3 | 95.7 | |
9th decile | Below 10 | 15.4 | 15.4 | 15.4 | 4.5 | 5.9 | 6.8 |
Developing countries (10 to 25) | 15.7 | 15.6 | 15.6 | 16.0 | 22.4 | 25.0 | |
Over 25 | 14.7 | 14.8 | 14.9 | 48.1 | 57.3 | 58.7 | |
Anglo-Saxon countries | 15.4 | 15.4 | 15.6 | 58.6 | 66.1 | 69.7 | |
10th decile | Below 10 | 34.4 | 32.1 | 31.2 | 10.0 | 12.3 | 13.7 |
Developing countries (10 to 25) | 33.1 | 32.1 | 30.8 | 34.2 | 46.5 | 49.8 | |
Over 25 | 25.2 | 24.9 | 24.5 | 81.7 | 96.0 | 96.1 | |
Anglo-Saxon countries | 27.2 | 27.5 | 27.1 | 104.2 | 118.6 | 121.6 |
Over 16 years, in developed countries with per capita incomes exceeding Int$25,000, the share of income in the 10th decile decreased by 0.7 p.p., while that of the 5th quintile dropped by 0.5 p.p. and the share of incomes earned by quintiles 2 to 4 slightly increased. In the Anglo-Saxon group, inequality is appreciably higher than in developed countries: from the 3rd quintile on, affluent Anglo-Saxon groups are ahead of the rest of the developed countries both in terms of the share of income and, especially, the size of imputed income. The gap between the 10th Anglo-Saxon decile and the 10th wealthy country decile (over Int$25,000 per capita) in 2000, 2008, and 2016 was Int$22,500, Int$22,600, and Int$25,500 per capita, respectively; the gap with the 10th decile for countries with per capita income between Int$10,000 and Int$25,000 was Int$70,000, Int$72,100, and Int$71,800, respectively.
In moderately developed (Int$10,000 to Int$25,000 per capita) and less developed countries (below Int$10,000), the trend is more complex: the share of the 5th quintile decreased in both country groups by roughly 2.5 p.p. over 16 years. These shares are distributed in small “portions” in the share of income earned by the 1st to the 4th quintiles, which inspires certain optimism (Table
Notably, the share of the 5th quintile in developed countries is lower than in developing countries, giving certain grounds for Kuznets’s hopes to reduce inequality. However, it is extremely important to stress the difference between relative and linear shifts, as in the cluster analysis of national growth rates in our previous article (
A certain reduction in relative inequality (the shares of the 10th decile and 5th quintile) during economic growth is accompanied by a growing distance between the incomes (imputed GDP (PPP) per capita) of the wealthiest and the “other wealthy,” middle, and poor groups of the global society. The distance between the 10th decile and the 2nd quintile increased significantly in 2000 and 2016 (see Table
It is safe to say that there have been certain shifts in the shares and gaps across years, deciles, and country groups, but the “linear” gap between the rich and the poor grew considerably over the first 16 years of the 21st century. Certain shifts can be seen in the degree of inequality in developing and moderately developed countries during long-term periods of economic growth. We may have to re-think our understanding of the correlation between the economic growth trend and the nature of social institutions at different levels of development within the context of the inequality problem.
To verify our hypothesis, we conducted an econometric analysis on data from 92 countries (Table
Function | GDP (PPP) per capita | Const | X2 | X3 | X4 | DW | Qu. correlations, adjusted |
Y1 | Below $14,000 | –1.859 | 0.610 | 2.844 | 1.716 | 0.608 | |
t-stat | –0.987 | 2.168 | 5.582 | ||||
Y1 | Over $14,000 | –2.710 | 2.843 | 0.294 | 0.934 | 1.524 | 0.902 |
t-stat | –0.631 | 3.500 | 1.483 | 8.653 | |||
Function | GDP (PPP) per capita | Const | X5 | DW | Qu. correlations, adjusted | ||
Y2 | Below $14,000 | 2.667 | 0.344 | 2.004 | 0.538 | ||
t-stat | 4.641 | 4.812 | |||||
Y2 | Over $14,000 | 1.100 | 0.633 | 2.017 | 0.635 | ||
t-stat | 2.862 | 11.161 |
The interpretation of the results is quite simple. Relative inequality (Y2) , measured as the gap between GDP (PPP) per capita in the 10th decile and the 2nd quintile, i.e., in fact, between the wealthiest groups and a portion of the lower middle class, appears to have been stable and nearly unchanged between 2000 and 2016. Moreover, no additional variables have produced any significant coefficients. For the similar indicator (Y1), but in absolute terms of the gap between 2000 and 2016, the significant indicators were the initial GDP per capita in 2000 and the GDP dynamics over the period, but only for countries with GDP per capita over Int$14,000. In actuality, all of the above equations confirm the central hypothesis that relative inequality was rigid while absolute inequality increased gradually along with economic growth within the range under review: between the rich and the disillusioned “yellow vests.”
For Y2 (the relative gap between the 10th decile and the 2nd quintile in 2016), we did not find a significant correlation with GDP growth factors or the initial indicators of inequality in either group, i.e. the lagging indicator of the function as of 2000 completely dominates the calculations.
This, in fact, means that a stable relative gap between the upper class (the 10th decile may include both the upper and the upper middle class) and the lower middle class (the 2nd quintile in developed countries) leads to a wider absolute gap over time. In less developed countries, this correlates with other factors. In this respect, econometrics doesn’t produce new results as much as it confirms the quite easily observable “inertial departure” of the wealthiest social group from the relatively poor but quite massive stratum, i.e. the lower middle class.
From the perspective of interdisciplinary studies, the above parameters of the income structure and trend for groups enable a more accurate estimate of the status of civic society in the countries under review. The analysis shows that, in most countries, social inequality arises as a result of various historical processes and remains constant afterwards. There is an example of this in Russia, where inequality increased sharply during the rapid privatization program between 1993 and 1996 (
Fig.
Share of the 10th decile, GDP (PPP) per capita, 106 countries, 2016.
Source: Authors’ calculation based on World Bank data.
Social inequality in the world for a particular year can be illustrated by comparing the incomes of their citizens belonging to the 10th deciles (Fig.
Imputed GDP (PPP) for the 10th decile and GDP (PPP) per capita, 106 countries, 2016.
Source: Authors’ calculation based on World Bank data.
The separation of the per capita income of the 10th decile becomes stronger after a country exceeds Int$15,000 per capita. The 10th decile’s exceptionally high share in Luxembourg could be viewed as exotic, but one cannot deny the breakaway of the wealthy from the rest of the population in terms of income at the early stages of development and the entrenchment of these advantages in the course of economic growth within a country. Above, we have demonstrated the lack of perceptible relative shifts in the 10th decile’s share of income from 2000 to 2016. Here, however, we can see the absolute gaps widening, whereas there are no signs of declining inequality as GDP increases, rather quite the opposite.
We will conduct a more detailed analysis of the level and nature of social inequality in key countries and identify its driving forces in later papers. Here, we will dwell on the most interesting groups. In this section, we consider the trends and some specific features of inequality in the European Union, former socialist countries and the BRICS states in recent decades, as they represent important practical cases.
When reviewing the process of convergence, one cannot help mentioning the generally successful (at least for some countries) integration process of the Central and Eastern European countries into the EU. The correlation in growth rates between the “old 15” and the “new 13” is divided into two stages. From 2000 to 2008, growth rates in the new EU member countries (future members before 2004) far exceeded the growth rates of the EU’s core (Table
Population (million), GDP (PPP) per capita (thousand Int$), share of income in the 10th decile (%), 2000–2016.
Country | Population | GDP (PPP) per capita | Share of the 10th decile | |||||
2000 | 2016 | 2000 | 2008 | 2016 | 2000 | 2008 | 2016 | |
EU-15 | ||||||||
Luxembourg | 0.4 | 0.6 | 82.1 | 95.7 | 98.0 | 23.7 | 25.8 | 24.3 |
Ireland | 3.8 | 4.7 | 38.8 | 46.1 | 64.2 | n/a | n/a | 24.9 |
Netherlands | 15.9 | 17.0 | 41.9 | 47.4 | 47.5 | 23.7 | 23.7 | 23.3 |
Sweden | 8.9 | 10.0 | 36.9 | 43.4 | 46.1 | 21.1 | 21.5 | 21.5 |
Germany | 81.5 | 82.5 | 37.6 | 42.2 | 44.9 | 25.2 | 24.6 | 24.9 |
Denmark | 5.3 | 5.7 | 41.2 | 44.7 | 44.7 | 20.6 | 21.0 | 23.7 |
Austria | 8.0 | 8.7 | 38.9 | 44.3 | 44.3 | 23.8 | 24.1 | 24.1 |
Belgium | 10.2 | 11.3 | 37.0 | 41.5 | 41.7 | 25.1 | 22.7 | 22.4 |
France | 58.9 | 64.6 | 36.0 | 38.8 | 39.3 | 24.5 | 26.7 | 26.0 |
United Kingdom | 58.9 | 65.6 | 33.3 | 38.1 | 39.3 | 27.8 | 26.4 | 26.2 |
Finland | 5.2 | 5.5 | 34.6 | 42.3 | 39.2 | 23.2 | 23.0 | 22.0 |
Italy | 56.9 | 60.7 | 36.1 | 37.3 | 34.1 | 26.3 | 25.5 | 25.5 |
Spain | 40.6 | 46.4 | 30.3 | 34.6 | 33.7 | 24.9 | 25.6 | 26.0 |
Portugal | 10.3 | 10.3 | 25.8 | 27.4 | 26.8 | 30.8 | 28.9 | 27.4 |
Greece | 10.8 | 10.8 | 25.6 | 32.9 | 24.9 | 25.5 | 26.0 | 26.1 |
EU-13 | ||||||||
Bulgaria | 8.2 | 7.1 | 9.4 | 16.2 | 18.9 | n/a | n/a | 28.8 |
Malta | 0.4 | 0.4 | 25.4 | 29.3 | 37.0 | |||
Cyprus | 0.7 | 0.8 | 31.4 | 37.8 | 32.4 | 28.8 | ||
Czech Republic | 10.3 | 10.6 | 21.0 | 29.2 | 31.1 | 22.7 | 22.9 | 22.1 |
Slovenia | 2.0 | 2.0 | 22.7 | 31.2 | 29.9 | 20.6 | 20.1 | 21.1 |
Slovakia | 5.4 | 5.4 | 15.5 | 25.3 | 29.1 | 22.1 | 21.4 | 20.3 |
Lithuania | 3.5 | 2.9 | 12.1 | 23.5 | 27.8 | 27.9 | 27.6 | 28.9 |
Estonia | 1.4 | 1.3 | 15.3 | 25.3 | 27.5 | |||
Poland | 38.3 | 38.0 | 14.7 | 20.2 | 25.7 | 26.1 | 26.7 | 25.2 |
Hungary | 10.2 | 9.8 | 17.9 | 23.7 | 25.5 | 24.2 | 22.1 | 24.0 |
Latvia | 2.4 | 2.0 | 11.2 | 21.4 | 23.8 | 28.5 | 27.7 | 26.6 |
Croatia | 4.4 | 4.2 | 15.7 | 21.7 | 21.3 | n/a | n/a | n/a |
Romania | 22.4 | 19.8 | 10.1 | 18.2 | 20.7 | 23.3 | 23.2 | 21.6 |
The weighted average GDP (PPP) for the EU-13 countries was 55.2% and 63.5% for the similar EU-15 indicator in 2008 and 2016, respectively. From 1980 to 1990, the EU average GDP (PPP) per capita increased from Int$25,400 (9 members) to Int$30,000 (12 members).
In 2000, the GDP (PPP) per capita in the EU-15 reached Int$35,300, varying from slightly below Int$26,000 in Greece and Portugal to Int$41,200 in Denmark and Int$41,900 in the Netherlands (the highest value is in Luxembourg — Int$82,100 per capita). Economic growth was intensive during the 2000s, while the weighted average GDP (PPP) per capita in the “old” EU member countries increased from Int$35,300 to Int$39,300. As a result, by 2008 (before the recession), the situation had changed and Ireland, Germany, Finland, and Sweden joined the group of leaders in the EU-15.
GDP (PPP) per capita in the “new” (13 future members) EU member countries was Int$14,700 on average in 2000, but by 2008 it had increased to Int$21,700, or from 41.6% to 55.2% of the EU-15 average. It was a significant success for Cyprus, Slovenia, Czech Republic, and the Baltic states, which can be partly associated with the integration effects. At the same time, the rapid growth by a number of EU-13 countries was natural to a large extent, resulting from the decade-long transformation of economic institutions. Thus, the joining of the “new” members undoubtedly increased the dispersion of GDP (PPP) per capita values across the EU, as the development level of new members was much lower. However, due to the economic growth before and after 2004, the gap between the EU-15 and EU-13 countries decreased slightly.
The general favorable trend towards equalized development levels and high growth rates in many EU countries was disrupted by the crisis in 2008 and 2009. It was relatively easily surpassed by Poland, which is beginning to lead in terms of growth rates among the “new” members. The Czech Republic, Slovakia, and Slovenia are retaining their positions. However, the overall increase in GDP per capita was not very high during the eight years, and the group reached a new mark of Int$25,100 per capita. However, this was enough to increase the relative indicator value to 63.5% of that for the group of developed countries.
The reason is that for the better developed group of “old” members, the situation proved to be more complex than in less-developed countries during and after the crisis in 2008 and 2009. In Spain, the depression lingered for five years (
We can state that the European economies (many of them, at least) demonstrated a certain reduction in relative inequality between 2000 and 2016, in terms of the 10th decile’s share of income (Table
The European country group represents the “EU cluster” in terms of the huge integration efforts, investments, and multiple steps in various areas aimed at strengthening the EU’s Acquis Communautaire. In terms of inequality, the majority of these countries, except for the UK and Portugal, are rather an example of its lower level, while the 10th decile’s share fluctuates insignificantly.
The inequality data for socialist countries in 1992 are difficult to estimate, as a vast majority of these countries underwent dramatic changes during that year. One noteworthy development was the crisis in the post-Soviet states, accompanied by growing inequality both across the entire group and within individual countries, particularly as noted by
The data for the period from 2000 to 2016 reflect a decrease in the share of income in the 10th decile for almost every country. It decreased most dramatically in Moldova (from 28.5% to 22.5%) and Kazakhstan (from 26.7% to 22.3%) and grew in Russia (from 27.6% to 29.7%). Inequality in financial income in Russia emerged during the 1990s and has not changed much since then. The distribution of shares of income by quintile has barely changed since 1999, as has its structure in general (
The BRICS group consists of economies with substantial differences in the level of development and the economic growth models. China, while being comparable in population with India, produces almost 2.5 times as much per GDP (PPP) (Int$23.2 trillion and Int$9.5 trillion, respectively, in 2017). The dispersion of GDP (PPP) per capita in current prices within BRICS was 3.9 times in 2017. The corresponding values for China, Brazil, and South Africa are close: Int$16,700, Int$15,600, and Int$13,500 per capita, respectively. Russia’s figure is almost twice as high (Int$27,800 per capita), whereas India’s is twice as low (Int$7,200 per capita). At the same time, South Africa demonstrates the highest social inequality (the country is not presented in the charts due to the specific nature of gathering statistical data), while being in the middle of the distribution. In 2017, the share of income in the wealthiest (10th) decile was 50.5% in South Africa, 40.4% in Brazil, 31.4% in China, 29.8% in India, and 29.7% in Russia.
It is also noteworthy that in the BRICS countries, the goal of reducing social inequality is set at the political level. For example, Russian Prime Minister Dmitry
At the 18th Congress of the Chinese Communist Party in 2012, Li Keqiang, Premier of the State Council, identified the promotion of social justice as one of the development priorities. Poverty declined considerably in China after 2008, but inequality increased because incomes of urban households grew faster than those of rural households. On the whole, the significant increase in well-being in China coincided with a level of inequality which is high for a socialist country, but relatively low in comparison with other developing countries. Future inequality is currently the subject of analysis and discussion, since reducing it could facilitate a re-focusing of growth on the domestic market. The market boom (between 1980 and 2015) was accompanied by a giant leap in development; a new standard of living was achieved but a new type of social inequality emerged as well as 10th decile incomes distanced themselves from the rest of the population, which was reported in an IMF study (
The cluster analysis of social statistics with respect to 106 countries is fairly reliable (especially in the richest four clusters). Earlier (
Proportion of personal consumption and share of income in 10th decile, 2016 a) (%).
Cluster No. | Cluster limits, thousand Int$ per capita | Durable goods | Non-durable goods | Services | Recreation and leisure | On durable goods for culture and recreation | Share of 10th decile | |
bottom | top | |||||||
1 | 35.1 | – | 14.4 | 25.8 | 52.4 | 8.5 | 0.35 | 24.3 |
2 | 21.1 | 35.1 | 8.9 | 40.4 | 43.5 | 6.4 | 0.09 | 27.2 |
3 | 14.0 | 21.1 | 10.0 | 43.8 | 39.2 | 4.8 | 0.11 | 31.7 |
4 | 7.0 | 14.0 | 7.8 | 46.8 | 39.6 | 3.3 | 0.04 | 29.5 |
5 | 3.2 | 7.0 | 7.6 | 54.8 | 31.6 | 1.5 | 0.05 | 31.4 |
6 | 1.8 | 3.2 | 8.2 | 57.8 | 27.1 | 1.5 | 0.04 | 30.1 |
7 | – | 1.8 | 4.8 | 62.8 | 28.3 | 1.0 | 0.02 | 32.5 |
United States | 8.8 | 17.8 | 66.6 | 9.2 | 0.46 | 30.2 | ||
Germany | 11.2 | 27.3 | 52.3 | 8.7 | 0.26 | 24.9 | ||
Brazil | 10.2 | 39.3 | 43.0 | 4.2 | 0.04 | 40.5 | ||
India | 3.3 | 41.2 | 47.2 | 0.9 | 0.02 | 29.8 | ||
China | 7.3 | 34.7 | 48.5 | 5.5 | 0.08 | 31.8 | ||
Russia | 12.1 | 49.1 | 31.8 | 4.9 | 0.02 | 29.7 |
A high proportion of food in personal consumer expenses effectively acts as a “substitute” for the poverty indicator. The higher the proportion, the further the country is from wealth, intellectual development in terms of a “post-industrial society” or the free development of a person in the Marxist vernacular. The important factors are increased amounts of free time and opportunities for recreation, leisure, and the use of special recreational products. The hierarchy of the clusters is very conspicuous: 8.5% of spending in the first cluster (with the highest income for the 10th decile in Fig.
Looking back at the problem of inequality and the level of development, we note that developed countries (first and second clusters) with their high absolute inequality, still have lower shares of income in the 10th decile, except for the Anglo-Saxon countries. However, no sustainable trend towards lower inequality in developed countries can be observed in the period under review. A slight decrease in some countries is concurrent with an increase in others.
Democracy indices and the proportion of the population with internet access fall into a clear cluster hierarchy (Table
Average values for R&D spending, the democracy index, proportion of population with internet access, proportion of population older than 25 years with tertiary education, life expectancy at birth, and the birthrate, 2016.
Cluster No. | R&D spending | Proportion of population with internet access, % | Democracy index | Proportion of the population older than 25 years with tertiary education, % | Life expectancy at birth, years | Birthrate (per 1,000 of population) |
1 | 2.1 | 88.0 | 7.9 a) | 32.1 | 81.1 | 11.4 |
2 | 1.3 | 70.2 | 6.8 | 25.8 | 76.8 | 13.0 |
3 | 0.7 | 56.8 | 5.6 | 18.8 | 73.2 | 16.5 |
4 | 0.4 | 48.3 | 5.7 | 20.4 | 72.3 | 18.5 |
5 | 0.3 | 30.6 | 4.5 | 32.7 b) | 67.7 | 26.6 |
6 | 0.1 | 17.3 | 4.5 | 2.1 | 67.5 | 32.5 |
7 | 0.3 | 10.6 | 3.8 | – | 59.1 | 37.3 |
United States | 2.8 | 76.0 | 8.0 | 42.3 | 78.7 | 12.4 |
Germany | 2.9 | 89.6 | 8.6 | 25.4 | 81.1 | 9.3 |
Brazil | – | 60.9 | 6.9 | – | 14.2 | |
India | 0.6 | 29.5 | 7.2 | – | 19.0 | |
China | 2.1 | 53.2 | 3.1 | – | 12.0 | |
Russia | 1.1 | 73.0 | – | – | 70.9 | 12.9 |
Social parameters provide a clear picture of differences across clusters. The first thing to mention is the rigid order across clusters in declining birthrates, increasing life expectancy and the proportion of people with higher education as well-being increases. Inequality may be a crucial element in the system of formal and informal institutions that characterize a society’s development at a certain income level.
Catch-up development in terms of the cluster approach implies a comprehensive transition of the entire institutional system to a higher level together with a two-fold increase in per capita income on average (characteristic gap between clusters). Each country has its own specific features here, but the common trends and parameters are still significant: to overcome the two-fold gap between clusters in GDP per capita, a country needs to grow with a greater advantage in growth rates over other countries, and not without adjustment for population growth.
What conclusions can be drawn from analyzing the distribution of a large group of economic and sociopolitical indicators across clusters? First of all, we have noted the stability of the incumbent system during the twenty-five years under review. We can see the general rigidity of inequality, specifically in its Anglo-Saxon and continental models. Moreover, we observe a crisis of relative inequality. Therefore, we believe that the institutions and factors driving the redistribution of income (labor market efficiency, social status, progressive taxes, inheritance tax, etc.) will fail to overcome the “deceleration” underlying the institutions of the modern economy and society. This provides sufficient grounds for improving the theory, rather than for seeking simple income redistribution methods, as the latter does not solve the actual inequality problem.
Of course, the rapid development of China, South Korea and India has pushed population masses upwards along the social ladder, but failed to remove the differences between the countries and the differences between deciles within the countries. In the context of the picture representing inequality and its rigidity across all clusters, the social situation in the world becomes different from that imagined recently: much less grounds for optimism for divergence across the globe. This, in particular, follows from the trends observed in the 21st century. Undoubtedly, any country through extraordinary effort seems to be able to move upwards across the clusters and reduce internal inequality, but a massive shift is hardly possible. In our future papers, we also intend to identify the specific features of the labor market and how they impact social inequality, and to address the problem of inequality in wealth.
We are confident that these conclusions are stable: they can be regarded as stylized facts based on which the theory has to be improved. However, conclusions with respect to the breakdown of the distribution of economic and sociopolitical indicators can be interpreted either from a soft or from a hard perspective. The soft perspective means that we correctly identified the limits of the clusters as the tool of analysis and discovered substantial differences between them, which go far beyond “development levels” in terms of GDP (PPP) per capita. At least, we can state that each cluster corresponds to its own set of parameters for all (most) indicators.
In the course of development, it is not enough to just double GDP per capita to move up a cluster. It is important to change the social system so that social indicators are congruent to a measure between each other and with GDP per capita. Strictly speaking, this specifies the requirements for sustainable development in the context of changing this level. However, in using this approach, we will not risk regarding these clusters as a separate type of society. Perhaps only with respect to the first cluster (with the exception of a number of countries due to the great deviation in their sociopolitical indicators) can it be stated with confidence that it has a number of indicators characterizing the transition to post-industrial development. It should be noted that this approach sets higher and often quantifiable criteria in formulating national development strategies.
Potentially, this set of cluster indicators and characteristics can be interpreted more rigidly, something on which we do not insist. In this case, these clusters will represent qualitatively different types of economies and societies. Then, the transitions between them can be called “qualitative leaps” in the Marxist terminology. We are not suggesting cluster names in the language of social development, particularly because we can see no ideal, especially for the inequality indicator. Nevertheless, we believe there may be people willing to do it.