Abstract
Industry 4.0 is likely to be a way to reach both economic efficiency and social equity. Nevertheless, nobody can predict the social impact of Industry 4.0 on society, which 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 and macros application. We researched impact of gross capital formation, research and development expenditure to create innovations, intellectual property and high-technology exports on inequality of countries using principal component analysis based on open data 2012–2015. Cluster analysis allows countries to be divided into homogeneous groups that describe the impact of IT development on these countries in the absence of training samples. An isotonic or isomorphic algorithm is used for cluster analysis. PCA method is used as an isotonic algorithm. The isomorphic algorithm includes in groups countries which are close in structure. We presented advantages of isomorphic algorithm versus isotonic one using Macros Application which give us necessary links between countries to develop dendrite diagram for clusters of countries under impact of IT factors. Thus we have received 7 clusters describing the diverse impact of IT factors on nonuniform income distribution across 45 countries. First group of clusters shows that IT factors motivates to decrease economic inequality. Second group of clusters demonstrate that IT factors lead to inequality. Third group of clusters presents that other factors (not IT factors) generate decreasing of inequality. Fourth group of clusters prove that other factors (not IT factors) generates expanding of inequality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vacek, J.: On the road: from industry 4.0 to society 4.0. Trendy v Podnikání 7(4), 43–49 (2017)
Kobets, V., Yatsenko, V., Mazur, A., Zubrii, M.: Data analysis of private investment decision making using tools of Robo-advisers in long-run period. In: Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, 14–17 May 2018, vol. 2104, pp. 144–159 (2018)
Kuzmenko, O., Roienko, V.: Nowcasting income inequality in the context of the fourth industrial revolution. SocioEconomic Challenges, vol. 1, pp. 5–12, Recuperado en septiembre 2017 (2017). http://armgpublishing.sumdu.edu.ua/wp-content/uploads/2016/12/files/sec/issue1/SEC_1_2017_Kuzmenko.pdf
Pillai, N., Vijayamohanan: You Cannot Swim Twice in the Same River: The Genesis of Dialectical Materialism, MPRA Paper 45011, University Library of Munich, Germany (2013)
Birdsall, N.: The World Is Not Flat: Inequality and Injustice in Our Global Economy, WIDER Annual Lecture 9, UNU World Institute for Development Economics Research (2007). http://cgdev.org.488elwb02.blackmesh.com/doc/commentary/speeches/Birdsall_WIDERpaper.pdf
Šmihula, D.: Waves of technological innovations and the end of the information revolution. J. Econ. Int. Finan. 2(4), 58–67 (2010)
Sbardella, A., Pugliese, E., Pietronero, L.: Economic development and wage inequality: a complex system analysis. PLoS ONE 12(9), e0182774 (2017). https://doi.org/10.1371/journal.pone.0182774
Statistic database. http://statisticstimes.com/economy/projected-world-gdp-ranking.php
Snihovyi, O., Kobets, V., Ivanov, O.: Implementation of robo-advisor services for different risk attitude investment decisions using machine learning techniques. In: Ermolayev, V., Suárez-Figueroa, M.C., Yakovyna, V., Mayr, H.C., Nikitchenko, M., Spivakovsky, A. (eds.) ICTERI 2018. CCIS, vol. 1007, pp. 298–321. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13929-2_15
Kuzmin, O., Sidorov, Y., Kozik, V.: Achievements and Problems of Evolutionary Economics: Monograph. Publishing House of Lviv Polytechnic National University (2011)
Papageorgiou, C., Jaumotte, F., Lall, S.: Rising Income Inequality: Technology, or Trade and Financial Globalization? IMF Working Paper, International Monetary Fund (2008). https://www.imf.org/external/pubs/ft/wp/2008/wp08185.pdf
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 (2002). http://davidcard.berkeley.edu/papers/skill-tech-change.pdf
Krueger, A.B.: How computers have changed the wages structure – evidence from microdata, 1984–1989. Q. J. Econ. 108, 33–60 (1993)
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)
Frey, C., Osborne, M.: The Future of Employment: How susceptible are jobs to computerisation (2013). http://www.oxfordmartin.ox.ac.uk/publications/view/1314
Devlin, S.: (In) Equality in the Digital Society Workshop Summary. New Economics Foundation. Friedrich-Ebert-Stiftung London (2017)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Arnold (2001)
Manly, B.F.J.: Multivariate Statistical Methods: A Primer, 3rd edn. Chapman and Hall, London (2005)
Rencher, A.C.: Methods of Multivariate Analysis, 2nd edn. Wiley, Hoboken (2002)
Data set for gross capital formation (% of GDP). https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS
Data set for research and development expenditure (% of GDP). https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS
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
Data set for high-technology exports (% of manufactured exports). https://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS?view=chart
Data set for Theil index analysis. https://drive.google.com/open?id=1SSb26seUYM2Vj1AjovUN6jnWG3Sx8zMB
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kobets, V., Yatsenko, V., Voynarenko, M. (2020). Cluster Analysis of Countries Inequality Due to IT Development Through Macros Application. In: Ermolayev, V., Mallet, F., Yakovyna, V., Mayr, H., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2019. Communications in Computer and Information Science, vol 1175. Springer, Cham. https://doi.org/10.1007/978-3-030-39459-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-39459-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39458-5
Online ISBN: 978-3-030-39459-2
eBook Packages: Computer ScienceComputer Science (R0)