The choice between economic efficiency and social equity has become a key objection in economic development, since in the current economic system, which has become close to the Pareto optimum, the achievement of both of these goals is mutually exclusive. There is only one way to reach both of these goals - the fundamental change of current system of economic relations and getting access to new curves of production capabilities, which may become quite real within development of Industry 4.0 and 6th technological wave. Nevertheless, nobody can predict the social impact of Industry 4.0 on society, which in the context of future technological changes transforms into Society 4.0. The purpose of this paper is to prepare cluster analysis of countries inequality due to IT development using software package. We researched impact of gross capital formation, research and development expenditure to create innovations, intellectual property and hightechnology exports on inequality of countries using principal component analysis based on open data 2012-2015. We found 4 main clusters of 45 countries which have convergence and divergence attributes due to IT development. It was also revealed the countries which had inequality due to other reasons which are not connected with IT development.
For many centuries, economic science was developing and changing according to the
current challenges. Consequently, the purpose of economic activity was changing as well:
from profit maximization during original accumulation of capital to optimization of
resources in the second half of the XX century, to the social welfare improvement within
the concept of sustainable development. As a result, the choice between economic
efficiency and social equity has become a key objection in economic theory, since in the
current economic system, which has become close to the Pareto optimum, the
achievement of both of these goals is mutually exclusive. There is only one way to reach
both of these goals –the fundamental change of current system of economic relations and
getting access to new curves of production capabilities, which may become quite real
within development of Industry 4.0 and 6th technological wave. Nevertheless, nobody can
predict the social impact of Industry 4.0 on society, which in the context of future
technological changes transforms into Society 4.0. [
The purpose of this paper is to investigate the impact of information technologies and innovations on social inequality for different countries.
The paper has the following structure. Section 2 is devoted to the complex analysis of inequality and its influence with technological process. Section 3 describes how the level of inequality under the influence of IT within different countries in 2012-2015. The last section is the conclusion, which sums up the results of the research. 2 2.1
Usually, category of "inequality" is used for analysis of the social equity during the
distribution of material and social benefits and is identified as a negative phenomenon that
leads to stratification of society, political instability, etc. However, according to the
second law of the dialectics "unity and struggle of contradictions", inequality can be
analyzed, as well from the positive point of view, transforming into the concept of
"constructive inequality" as opposed to "destructive inequality". Moreover, based on
complex approach of inequality analysis, we can talk not only about the distribution of the
income in society, but also about the distribution of opportunities in it, which can
radically change the logic of this topic. To N. Birdsall’s opinion, high inequality might be
regarded as a lesser evil if it has a positive or neutral impact on growth prospects, or if it
is simply a passing phase that successful countries have to endure on route to a prosperous
future [
At micro level the inequality in income distribution in its classical sense has a negative
impact, because it causes demotivation of workers, and may even lead to emigration.
However, if a society has equal distribution of opportunities a so-called "social elevator",
divergence of incomes can have a constructive effect by increasing the motivation and
productivity, gaining new knowledge and skills, self-development, and, consequently,
generating higher incomes by workers. As a real example can be society of United States
[
“Brain drain” and demotivation inter-industry flow of capital and labor
At the industry level, the spontaneous unequal allocation of benefits creates a possibility for floating of capital and labor force from less to more productive industries, contributing the economic growth. However, the deliberately inappropriate interdisciplinary distribution of resources can conserve structural imbalances and slow down the country's economic development.
At macro level, in turn, income inequality may be a necessary condition and a
consequence of the economic development of a country at certain stages. First of all,
according to Keynes's theory of consumption, when income is growing, the marginal
propensity to save (MPS) is growing faster than marginal propensity to consume (MPC),
which consequently lead to higher marginal propensity to save of rich people rather than
poor [
On the other hand, unequal distribution of opportunities and incomes can contribute the emigration of highly skilled labor, deepen social instability and lead to a substantial political crisis that will block the possibility of a country's economic development, as it is in countries with totalitarian political regimes.
At global level, inequalities, according to some scientists, for instance N. Birdsall,
cannot produce positive effects, since: globalization is commonly held to be inherently
disequalising: global markets work better for more productive assets which are
disproportionately owned by better-off individuals in richer countries; globalization
results in new types of externalities and market failures which poorer persons and weaker
nations are ill equipped to handle; globalization creates a need for continuous revision of
the rules governing the global economy which is exploited by rich countries for their own
narrow interests [
Thus, we can note that the inequality in society is objectively determined and in certain
cases, can have a constructive impact on the development of the economic system. This
point radically changes the logic of the study from elimination inequalities itself to
elimination of destructive inequality. However, the veracity of such findings significantly
depends on markets maturity and effectiveness of public institutions, since inequality can
create a constructive effect only in well-developed countries where appropriate social
infrastructure and high mobility of the population may achieve raise of productivity and
efficient resource redistribution. Yet, in developing countries with weak markets, weak
governments, and fragile social structures income gaps can only deepen market failures
through political instability. This is true because media voter, who has a relatively low
level of well-being, will significantly distort political decisions [
The second half of the XX century saw a large number of “economic miracles” that had made dramatic changes in the distribution of global economic impact. First of all, we are talking about Japan and the countries of the first wave of newly industrialized economies (NIE's) – "Asian dragons" that received impressive economic development in 50s-60s and 80s respectively. It is no coincidence that the growth of these countries took place simultaneously when the 4th technological wave with its combustion engine was being changed by the 5th mainly based on microelectronic components. That is why we can make a logical assumption that technological factor and active technology transfer have played a key role in the growth of labor productivity and the rapid development of industries with high added value in these countries. Similarly, now in the process of moving towards to the Fourth industrial revolution we can expect for a new explosion of “economic miracles” " that can alter the ratio of economic power globally. This brings up the question about the possibility of such scenarios implementation and scales of its consequences in the international economy.
Taking into consideration previous industrial revolutions, we can assert that countries with a relatively large amount of capital and production capacity were the first to implement new technologies and inventions and, accordingly, first to receive positive effects from them. That is why it is logical to predict that developed countries with a powerful industrial complex, sufficient amount of capital and developed IT sector will receive greater effects from the new industrial revolution and will continue to dominate the international markets of new high-tech products. However, the development trend of the current economic system is nonlinear which indicates uncertainty of the outputs caused by Industry 4.0 implementation. In our opinion, the future scenario of the international economy development within 6th technological wave can be described by Xmodel and will include four possible scenarios of development (fig. 2). 1. Developing countries thanks to new technologies will play a leading role in the international economy
Of course, as A. Sbardella et al. fairly noted a new sector is not introduced at random,
but only when a productive system has developed the required basket of capabilities, and
in this way gradually more and more complex sectors are introduced [
The case for this scenario: economic development nowadays is exponential, which makes the consequences of the Fourth Industrial Revolution introduction unpredictable and radically different from previous revolutions;
economic agents in developing countries, contrary to developed countries, are ready to take risks and are able to adapt much more quickly to new economic conditions; developing countries through technology transfer can quickly and with relatively low cost make work Smart factories and Cyber-Physical Systems which will eliminate the time lag with developed countries.
2. Developed countries will lose competitive advantage
In the majority of developed countries, especially in the EU, we can see increasing risk
aversion and lack of the entrepreneurial spirit due to their socio-economic systems are
very inertial [
4. Developing countries do not take advantage of Industry 4.0
Without sufficient amount of capital and with weak institutional structure, developing countries cannot fully gain all the opportunities and benefits of a new industrial revolution, further exploiting the resource of price competitiveness of their goods and services.
Finally, the implementation of one of these scenarios will depend on the dominance of
one of two factors - existing production and technological base, or the ability to adapt
quickly and with minimum costs to the new economic environment since the technology
progress is faster than the absorption capacity of the society [
The simplest way to determine the probability of some scenarios is the Hardy–
Weinberg equilibrium according to which there is one abstract feature – countries’
development within Industry 4.0 (table 1). This is determined by two types of
allelesexisting industrial and technological complexes, or the ability to take risks and adapt
quickly [
1=(А+а)2=А2+2 Аа +а2 (1)
However, existing studies highlight a deepening of the income divergence between
countries because of scientific and technological progress. For instance, Papageorgiou et
al. [
Moreover, within Industry 4.0 this gap will just getting deeper because a great part of low-cost jobs will disappear totally even in developed countries – according to the World Bank estimation, automation will put 57% of the jobs in the 35 countries in OECD at risk, including 47% of US jobs and 77% of the jobs in China [8; 11; 16]. Even more, new technologies and platform industries as one of the examples hide their inner inequality because of its natural characteristics – high connectivity and unregulated growth [17].
The ambiguity of the influence of Industry 4.0 on income distribution in the
international economy is also confirmed at macro level. For instance, France, the United
Kingdom, and Spain will meet increasing inequality under the influence of the Industry
4.0 while Germany, vice versa, will see a decrease as a result of technological shifts due
to the leadership of the Industry 4.0 initiative [
There are two main ways to cope with such inequality: tax system to redistribute the gains of machine production or rebuilding of the actual machinery ownership [17]. A necessary condition of obtaining positive effects of Industry 4.0 is choosing an appropriate strategy for the country as a whole. Adapting a corporate approach, we can outline the following variants of strategic management decisions for countries within technological change (fig. 3).
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IV USA Expansion of competencies
I Further operations on the existing way
Germany China
III Optimization of processes and products
II
Starting with changes
African countries Low (scale from 1 to 5) High (scale from 6 to 10)
Need to adjust business strategy
Transition economies Income inequality depends on many factors, such as land distribution and education, initial levels of inequality, mature of secure property rights and institutional system, social capital, and many others. However, in case of dramatic technological changes caused by Industry 4.0, which will inevitably change economic, managerial and social relations, the greatest attention attracts the connection of technological development of the country and the level of income inequality.
We would like to pose following research question. What impact information technologies and innovations have on social inequality for different countries? One of main index of social inequality is Theil index as a statistic primarily used to measure economic inequality and other economic phenomena.
The Theil T index is defined as ∑ ( ̅ ) ̅ ∑ ( ̅ ) ̅ (2) (3) where is individual income of i-th country, ̅ is average income for the country, and is the average number of people in the country. If the average incomes of all individuals are equal, then Theil indexes are zero. If the income of the entire population is concentrated in the hands of one individual, then Theil indexes are equal to .
To compare Theil indexes (TI) for different countries we will use weighted average of TI using GDP: where – gross domestic product of country i, ∑ – world GDP.
Among explanatory variables we can use datasets for 2012-2015 years (after introduction of conception Industry 4.0 in 2011): 1) Gross capital formation % of GDP ( ), which can substitute labor resources [19]; 2) Research and development expenditure (% of GDP) ( ) to create innovations [20]; 3) Intellectual property, payments ( ) to have competitive advantages for know-how [21]; 4) High-technology exports (% of manufactured exports) ( ), which have no domestic analogues [22].
Using software package RStudio requires the following libraries and scripts for 45 countries which have been influenced by explanatory variables: library("dplyr") # data analysis library("psych") # descriptive statistics library("lmtest") # test for linear models library("glmnet") # LASSO + ridge library("ggplot2") # graphs library("sjPlot") # significance of parameters ineqc<-read.csv("_2012.txt", sep="\t", header=TRUE, dec=",") l<-ineqc l$countryname <- as.character(l$countryname) glimpse(l) # l <- select(l, - Y, -id, -countryname) # describe(l) ineqc cor(l)
Correlations between explanatory variables are very low: > cor(l)
It means there are no significant correlations between all explained variables.
To investigate how explanatory variables can impact on countries inequality we will use principal methods after preliminary standardization of variables using data set for Theil index analysis [23] (fig. 4):
Number of country for Theil index
X1 decreases level of inequality. At the same time X2, X3 and X4 increase level of inequality. The first two principal components have a sample variance equal to 66,32% of the total sample variance of 4 indicators:
The cluster for original data set in 2012 includes following axes: pc1 – horizontal axis, PC2 – vertical one (fig. 5).
Cluster 1 ( ) for countries # 1, 21, 22, 23 inequality in Hong Kong, Hungary and India is formed due to gross capital formation in GDP.
Cluster 2 ( , ) – countries # 28, 30, 45 intellectual property and high-technology exports creates inequality for Latvia, Malaysia and USA.
Cluster 3 ( ) – countries # 5, 15, 20, 24, 32, 47 Research and development expenditure form inequality for these countries.
Cluster 4 (0) – other countries. Inequality for these countries (including Ukraine) exists due to other reasons than explanatory variables X1-X4.
The clusters for original data set in 2013 is shown in fig. 6.
Cluster 1 ( ) for countries 21, 22, 23 inequality in Hong Kong, Hungary and India is formed due to gross capital formation in GDP (without changes).
Cluster 2 ( and ) for Latvia (28) and Philippines (33) inequality is induced by gross capital formation (% of GDP) and high-technology exports (new cluster).
Cluster 3 ( ) – 30, 45 intellectual property and High-technology exports creates inequality for Malaysia (30), USA (45) and Estonia (16) (without changes).
Cluster 4 ( ) – combines countries which have strong impact of research and development expenditure 2, 3, 4, 5, 10, 12, 15, 17, 18, 24, 25, 29, 32, 35, 40, 41, 43, 49 (Ukraine).
Cluster 5 (0) – 8, 36, 26, 37, 39, 20, 8 etc. Inequality exists due to other reasons than Industry 4.0
The clusters for original data set in 2014 is shown in fig. 7. There are 2 alternative ways of inequality formation in 2014 and 2015: Cluster 1 includes countries, which increase X1, X4 and X3 (few countries) Cluster 2 consist of countries which increase inequality due to X2 (including Ukraine)
Importance of components:
PC1 PC2 PC3 PC4 Standard deviation 1.2252 1.0404 0.8599 0.8228 Proportion of Variance 0.3753 0.2706 0.1849 0.1692 Cumulative Proportion 0.3753 0.6459 0.8308 1.0000 The clusters for original data set in 2015 is shown in fig. 8.
There are 3 alternative ways for 2014 and 2015. Cluster 1 consists of countries X1 (21, 22, 23, 29). Cluster 2 consists of countries that increase inequality due to X3 and X4 (16, 30, 45, 28, 33). Cluster 3 includes countries that increase inequality due to X2 (including Ukraine). At the same time IT factors and Industry 4.0 are not necessarily deleting jobs, but can act as a transformative agent on the nature of jobs (countries in the center of fig. 8). This fact is confirmed for example by authors [24].
Thus 25% of countries create inequality due to gross capital formation, intellectual property, high-technology exports and 75% of countries form inequality as a result of research and development expenditure (radical innovations gives more welfare and different level of living standards). 4
Industry 4.0 creates a new possibility for digitalization, robotics, automation of all business process, creation of modern product and services. It gives competitive advantages to increase export of countries, increasing of the global level of competitiveness but extend the level of frictional and structural unemployment which decrease the level of income for individual and increase the gap of inequality between different segments of inhabitants.
Thus research and development generated more inequality between different countries. Inequality in Ukraine is growing mainly under impact of research and development expenditure during 2012-2015. Intellectual property and high-technology exports changed its impact from same level to different inequality level. Gross capital formation became more significant for other countries than for initial leaders (Hong Kong, Hungary, India). About 44% of all countries had inequality due to other reasons which are not connected with IT development and diffusion of Industry 4.0 which has different speed of expanding for different countries. 12. Papageorgiou, C., Jaumotte, F., Lall, S.: Rising Income Inequality: Technology, or Trade and Financial Globalization? IMF Working Paper, International Monetary Fund. Available at https://www.imf.org/external/pubs/ft/wp/2008/wp08185.pdf (2008). 13. Card, D., DiNardo, J. E.: Skill Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzle, NBER Working Paper 8769, National Bureau of Economic Research, Cambridge, Massachusetts. Retrieved from http://davidcard.berkeley.edu/papers/skill-techchange.pdf. (2002). 14. Krueger, A.B.: How Computers have Changed the Wages Structure – evidence from microdata, 1984-1989. Quarterly Journal of Economics 108, 33--60 (1993). 15. Kobets, V., Poltoratskiy, M.: Using an evolutionary algorithm to improve investment strategies for industries in an economic system. In: Proceedings of the 12th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, 21–24 June 2016, Vol. 1614, pp. 485--501 (2016). 16. Frey, C., Osborne, M.: The Future of Employment: How susceptible are jobs to computerisation. Available at http://www.oxfordmartin.ox.ac.uk/publications/view/1314(2013). 17. Devlin, S.: (In) Equality in the Digital Society Workshop Summary. New Economics Foundation. Friedrich-Ebert-Stiftung London (2017). 18. Merz, S. L.: Industrie 4.0 – Vorgehensmodell fur die Einfuhrung, Springer-Verlag Berlin, Heidelberg, Germany (2016). 19. Data set for gross capital formation (% of GDP), https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS 20. Data set for research and development expenditure (% of GDP), https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS 21. Data set for charges for the use of intellectual property, payments (BoP, current US$), https://data.worldbank.org/indicator/BM.GSR.ROYL.CD?view=chart 22. Data set for high-technology exports (% of manufactured exports), https://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS?view=chart 23. Data set for Theil index analysis, https://drive.google.com/open?id=1SSb26seUYM2Vj1AjovUN6jnWG3Sx8zMB 24. Braccini, A.M., Margherita, E.G.: Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company. Sustainability 36, 1--17 (2019).