Global income inequality: A case study of OECD countries and Kazakhstan

Jumambayev, Seisembay; Dzhulaeva, Almazhan; Baimukhanova, Sariya; Ilyashova, Guliya; Dosmbek, Aidana Article Global income inequality: A case study of OECD countries and Kazakhstan Comparative Economic Research. Central and Eastern Europe Provided in Cooperation with: Institute of Economics, University of Łódź Suggested Citation: Jumambayev, Seisembay; Dzhulaeva, Almazhan; Baimukhanova, Sariya; Ilyashova, Guliya; Dosmbek, Aidana (2022) : Global income inequality: A case study of OECD countries and Kazakhstan, Comparative Economic Research. Central and Eastern Europe, ISSN 2082-6737, Lodz University Press, Lodz, Vol. 25, Iss. 4, pp. 179-203, https://doi.org/10.18778/1508-2008.25.35 This Version is available at: https://hdl.handle.net/10419/289723 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ 179 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan Seisembay Jumambayev https://orcid.org/0000-0003-0527-3748 Ph.D. of Economic Sciences, Associate Professor, Al-Farabi Kazakh National University Department of Management, Almaty, Republic of Kazakhstan, e-mail: [email protected] Almazhan Dzhulaeva https://orcid.org/0000-0003-1533-7979 Candidate of Economic Sciences, Acting Associate Professor, Al-Farabi Kazakh National University Department of Management, Almaty, Republic of Kazakhstan, e-mail: [email protected] Sariya Baimukhanova https://orcid.org/0000-0002-9128-4891 Ph.D. of Economic Sciences, Associate Professor, Al-Farabi Kazakh National University Department of Finance and Accounting, Almaty, Republic of Kazakhstan, e-mail: [email protected] Guliya Ilyashova https://orcid.org/0000-0003-2661-0328 Senior Lecturer, Al-Farabi Kazakh National University, Department of Economics Almaty, Republic of Kazakhstan, e-mail: Ilyashov[email protected] Aidana Dosmbek https://orcid.org/0000-0001-8394-7176 Al-Farabi Kazakh National University, Department of Management, Almaty, Republic of Kazakhstan e-mail: [email protected] Abstract This article presents the results of a study into the features of the formation of economic inequal‑ ity in Kazakhstan in the context of global trends in the country’s development. The methodolog‑ ical basis of the study was a comparative analysis of the former Soviet Union (FSU) and OECD countries in terms of economic development and inequality in the context of global chang‑ es and trends, implemented with the help of econometric and economic‑statistical methods. The study revealed a direct statistically significant (p < 0.05) correlation between the level of in‑ come concentration of the 10% group and the economic growth of Iceland (r = 0.67) and the Re‑ public of Belarus (r = 0.65). In the case of the Republic of Kazakhstan, no such correlation was Comparative Economic Research. Central and Eastern Europe Volume 25, Number 4, 2022 https://doi.org/10.18778/1508‑2008.25.35 © by the author, licensee University of Lodz – Lodz University Press, Poland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license CC‑BY‑NC‑ND 4.0 (https://creativecommons.org/licenses/by‑nc‑nd/4.0/) Received: 21.04.2022. Verified: 5.08.2022. Accepted: 11.10.2022 180 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek found. However, in Kazakhstan, the link between the 10% group’s income concentration and gross domestic product per capita has been established. The dynamics of GDP growth and the values of Kazakhstan’s population’s real money incomes have a stable inverse relationship. The correla‑ tion coefficient between them is r = –0.46, and the determination coefficient is R = 0.215, based on data from 2008 to 2020. This suggests that economic growth is still the most important factor that influences the population’s real income. The results of the study will be put into practice by familiarizing government officials with the developed proposals for enhancing the state’s policy of overcoming economic inequality and setting the stage for sustainable economic growth. In ad‑ dition, the results of this study will be of interest to academic science, actualizing new directions for further research. Keywords: development, distribution, institutions, transition economy, wealth JEL: О10, О57, Р16 Introduction Global income inequality has remained stubbornly high fordecades, areflection oftheworld’s existing highly hierarchical economic system. Atthesame time, theshare ofincome re‑ ceived by 10%oftheworld’s population fluctuates between 50–60%oftotal income, while theshare oftheremaining 50% inthelower part is typically 5–10%. Theglobal share oftheworld’s richest 1% is nearly three tofour times that oftheremaining 50%, which is roughly onthesame order ofmagnitude as the0.1%share (Chancel andPiketty 2021). Intoday’s world, theCOVID–19 pandemic andits accompanying economic crisis are thetwo most powerful contributors toglobal poverty andpersistentinequality. As are‑ sult ofthepandemic, between 88 and115million people weretrapped inextreme pov‑ erty in2020, bringing global poverty rates back tolevels seen in2017. Thefigure is ex‑ pected torise to150million by 2021 (World Bank 2020). Global inequality remains widespread in2021, despite three decades oftrade andfi‑ nancial globalization. Inequality is now nearly as bad as when Western countries were attheir pinnacle ofpower. Additionally, theCOVID–19 pandemic has contributed totheescalation ofglobal inequality. Since themid–1990s, roughly 1%ofthewealth‑ iest people have amassed 38%ofall additional wealth. Notably, since 2020, these pro‑ cesses have moved atamuch faster pace (World Inequality Lab 2021a). Because ofthis, income distribution is ofsignificant scientific andpractical importance. Recent years have seen many new economic theories arise that try toascertain why there is so much inequality inincome distribution andhow that can upset economic growth. This research will investigate thetheories that explain inequality. Again, thestudy ofin‑ come inequality intheFSU countries is particularly important because this issue has received insufficient attention. 181 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan New perspectives on income inequality While it is widely acknowledged that income inequality is inherently undesirable, there is considerable debate about its impact oneconomic growth. Thelevel ofequality ofop‑ portunity is responsible fortherelationship between income inequality andeconomic growth. Income inequality has agreater impact onfuture growth insocieties where opportunities are unequally distributed, i.e., where parents’ material circumstances constrain their children’s opportunities (Mijs 2021). By contrast, insocieties where there are more opportunities foreveryone, income inequality can be more easily ig‑ nored andshould not limit investment opportunities or slow down growth. Inthis case, opportunity equality can be equated with intergenerational mobility, or thede‑ gree ofcorrelation between parents’ achievements (income andeducation) andchil‑ dren’s achievements (Aiyar andEbeke 2020). Thedynamics ofincome inequality inthepost‑communist countries ofCentral andEast‑ ern Europe have long been thought tobe linked totheimpact ofinstitutional change. Thegroup analysis results indicate two types ofinstitutional changes: endogenous inthefirst transition period, associated with adeterioration inincome distribution, andexogenous inthesecond transition period, associated with income distribution sta‑ bilization. Thepersistence ofhigh income inequality during thesecond transition period can be explained by post‑transitional tolerance forinequality, which reflects economic evolution but also suggests apossible shift invalues inCentral andEastern European countries (Josifidis, Supic, andGlavaski 2018). Main hypothesis Inequality has adetrimental effect oneconomic growth foranumber ofreasons. Tobe‑ gin with, inequality can result inunderinvestment ineducation, health care, andphys‑ ical capital, all ofwhich contribute toslower economic growth. Ontheother hand, underinvestment can be associated with alack ofresources, i.e., poverty, rather than inequality as aneconomic phenomenon. This favors considering poverty as another fac‑ tor that can stifle economic growth (Breunig andMajeed 2020). Inequality inFSU countries may be linked tothefact that, as aresult ofrapid devel‑ opment andurbanization, there is ahigh concentration ofpopulation inthese coun‑ tries’ capitals, owing tohigher personal incomes. This always favors increased mobility andlong‑term migration from small andmedium‑sized cities tolarge cities, as well as rural depopulation (with theexception ofKazakhstan, which has thehighest degree ofspatial polarization). Thehigh degree ofpersonal income inequality incapitals com‑ pared toprovinces primarily determines labor migrants’ choices:thecapital or outside thecountry (Zubarevich 2018). 182 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek Another aspect oftheproblem is that therelationship between remittances andinequal‑ ity is reversed inmost countries. Remittances, ontheother hand, deepen economic ine‑ quality when they account formore than 20%ofGDP. This situation calls intoquestion theassertion that remittances should only be viewed as aredistribution mechanism that benefits thepoor because additional migrant remittances can actually increase income inequality insome cases (Tokhirov 2021). Theissues captured here are viewed through thelens ofthefollowing hypothesis, which will either be confirmed or refuted: Ahigh level ofincome inequality inFSU countries andKazakhstan can, among other things, affect thedynamics ofeconom‑ ic growth andits sustainability. Furthermore, there is alink between theindicator ofgross internal income per capita andthepercentage ofpeople living onless than thepoverty line. Literature review Today’s world is marked by widespread economic inequality. Rapid economic growth insome developing countries has helped toreduce inter‑country inequality tosome ex‑ tent, but intra‑country inequality remains high and, insome cases, is increasing (Haller andEder 2016). According totheUN Sustainable Development Outlook Report, high levels ofinequality limit human development’s economic andsocial mobility and, as aresult, impede eco‑ nomic growth (United Nations 2019). Inequality is also amajor impediment toachiev‑ ing theSustainable Development Goals. Societies with high levels ofincome inequality develop more slowly than societies with low levels, andthey are less successful atsus‑ taining long‑term economic growth. They are also ineffective atalleviating poverty (De‑ partment ofEconomic andSocial Affairs oftheUnited Nations Secretariat 2020). Theworld ofglobal flows ofgoods, services, andcapital has changed dramatically over thelast twenty‑five years. This has influenced global economic andfinancial power re‑ lations (Čaušević 2017). Thefinancial capitalism era, characterized by increased globalization andbanking, has altered therelationship between labor andcapital, with labor frequently being theweak‑ er party. Ontheone hand, trade unions lost power as aresult ofthelabor‑capital con‑ flict, andlabor market institutions such as worker protection from layoffs, unemploy‑ ment benefits, unemployment subsidy replacement rates, andso onwere weakened. Moreover, workforce flexibility, atypical labor contracts, andtemporary jobs led topre‑ carious employment and, thus, precarious consumption. Inthis context, income ine‑ quality has grown because labor, themost important source ofincome, is viewed from asupply‑side perspective as acost tobe reduced rather than afundamental component ofaggregate demand toexpand production (Fadda andTridico 2016). 183 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan Thus, inrecent decades, theshift away from classical industrial capitalism toward fi‑ nancial capitalism has accelerated thegrowth ofinequality. Inthis regard, economic inequality is common inmany countries around theworld. This socio‑economic phe‑ nomenon is still one ofthemost perplexing scientific mysteries ofthepast andpresent. TheFSU countries only recently faced theproblem ofinequality, about 30 years ago, after abandoning theplanned economy andtransitioning toamarket economy. There‑ fore, this phenomenon has received insufficient attention. There has recently been much interest instudying certain aspects ofthedevelopment ofinequality intheFSU countries. Thereason forthis is that there is asignificant differ‑ ence between countries interms ofinequality, with Kazakhstan standing out. Thepur‑ pose ofthis study is toexamine thecharacteristics ofeconomic inequality inKazakhstan inlight ofglobal trends affecting its growth. This assumes that theresearch objec‑ tives will be met. Tobegin, it is necessary toexamine theissue ofeconomic inequality attheglobal level, specifically between individual countries interms ofGDP per cap‑ ita, as well as between different segments ofthepopulation interms ofincome, using anestablished assessment criterion based ontheGini coefficient andother relevant indicators. Thenext step is todelve deeper intothestudy oftheinequality problem by comparing Kazakhstan toother countries. Finally, thestudyintendstocharacterize similar approaches anddifferences inviewpoints onthemajor global trends andfactors intheformation ofinequality invarious countries worldwide. Methods and materials Research methodology Forthis comparative analysis, 12 FSU countries and38 OECD member countries as of2021 were chosen fortheir distinctive patterns ofsocio‑economic develop‑ ment. Appropriate indicators were chosen based onastudy ofthemain theoretical andmethodological concepts andapproaches tothestudy oftheproblem ofinequal‑ ity. Theuse ofthese indicators inthecomparative analysis would reveal differenc‑ es andtrends inthedynamics ofincome inequality inKazakhstan anddeveloped countries, as well as their impact oneconomic growth. Theresultsallow us toas‑ sesstheimpact ofchanging income inequality trends onKazakhstan’s economic growth rate and, ultimately, thecountry’s real chances ofjoining theworld’s thirty most developed countries. Theindicators representing thedistribution ofhouseholds according totheamount ofaverage per capita cash income andtotal cash income ofthepopulation by 10% groups were used during thestudy ofincome inequality inKazakhstan andsome 184 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek developed countries. They enable thecalculation ofdecile differentiation indicators andtheassessment ofincome concentration, cash income andexpenditure structure ofthepopulation, theGini coefficient, thepoverty rate, etc. Inequality was measured using data from theNational Bureau ofStatistics oftheRepublic ofKazakhstan, OECD. Stat, theWorld Bank Open Data, World Inequality Database (WID.world), theEura‑ sian Union’s statistical database, andother sources. TheP90/P10 ratio, i.e., theratio ofthe upper bound value of theninth decile or the10%ofpeople with thehighest income tothat ofthefirst, was also used here. TheGini coefficient has significant advantages over other indicators, but it also has anumber oflimitations. It has aclear graphical representation, andas with any generalizing measure, it allows general conclusions about inequality trends tobe drawn. It does not, however, determine whether theincrease or decrease ininequal‑ ity is theresult ofchanges atthebottom, middle, or top ofthedistribution. TheGini coefficient is more sensitive tochanges inthemiddle ofthedistribution than other indices andless sensitive tochanges atthevery bottom andvery top ofthedistri‑ bution (Department ofEconomic andSocial Affairs oftheUnited Nations Secretar‑ iat 2020). Correlation analysis was carried out using Microsoft Excel. All calculations were per‑ formed according toMeissner (2013). Results Inthemodern world, economic inequality exists attheglobal level between individu‑ al countries. It is typically measured interms ofGDP per capita, as well as income ine‑ qualitybetween different strata ofthepopulation. Let us take acloser look atthese two situations. When thedynamics oftheFSU countries andtheOECD countries interms ofGDP per capita (current US$) are compared, there is asignificant difference between them (Table1). Table 1. The FSU countries’ achieved Level of GDP per capita (current US$) in relation to the OECD countries’ average indicator (as a percentage) Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Australia 137.5 127.5 148.8 167.1 183.0 182.2 164.7 159.5 138.6 144.5 145.8 139.4 136.0 Austria 143.3 143.0 134.0 137.3 130.7 135.6 136.3 124.1 125.6 126.6 130.8 126.9 126.9 Belgium 133.3 132.9 126.3 126.5 120.2 124.9 125.7 115.2 116.5 118.0 120.9 117.5 117.1 185 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Canada 129.5 121.9 136.0 139.6 141.7 140.7 134.3 122.5 117.4 120.7 118.1 117.3 113.6 Switzer‑ land 206.7 214.9 220.6 243.9 232.9 235.5 236.4 238.2 230.4 223.0 219.6 216.0 228.6 Chile 29.8 30.4 36.6 39.1 41.3 42.3 38.7 38.1 38.2 40.1 40.4 37.3 34.7 Colombia 15.2 15.5 18.1 19.6 21.7 22.0 21.4 17.4 16.3 17.1 17.1 16.3 14.0 Costa Rica 19.1 20.3 23.5 24.7 27.1 28.7 28.6 32.7 33.3 32.7 31.7 32.1 31.7 Czech Republic 63.2 59.2 57.1 58.5 53.5 53.8 52.4 50.1 51.5 55.2 59.5 59.9 60.2 Germany 125.9 123.7 118.8 124.7 118.0 123.7 126.4 115.4 116.8 119.2 121.9 118.4 121.3 Denmark 178.3 173.4 166.0 165.0 157.4 163.6 164.8 149.6 151.6 154.1 156.6 151.3 160.3 Spain 98.0 95.5 87.2 84.6 76.2 77.7 77.6 72.3 73.5 75.2 77.1 74.8 71.0 Estonia 50.5 43.9 41.9 46.7 46.8 50.9 53.3 48.9 50.7 54.5 58.6 59.2 60.4 Finland 148.4 141.0 132.9 136.5 128.4 133.3 132.5 120.2 121.5 123.9 127.0 123.2 128.0 France 125.6 124.0 116.2 117.0 110.0 113.8 113.4 102.9 102.7 103.5 105.7 102.7 102.5 United Kingdom 131.0 115.5 113.1 112.4 114.2 116.0 125.1 126.5 113.9 107.8 109.3 107.2 105.8 Greece 88.7 88.6 76.3 68.0 59.0 58.1 56.9 50.7 49.6 49.7 50.2 48.5 46.4 Hungary 43.7 38.9 37.7 38.0 34.9 36.6 37.6 35.7 36.3 39.1 41.7 42.4 41.7 Ireland 169.3 154.8 139.0 139.3 131.9 137.7 146.3 174.2 174.3 186.2 201.0 204.7 223.8 Iceland 157.8 123.1 123.7 127.5 123.8 133.1 143.8 148.8 172.0 192.6 189.3 174.5 155.6 Israel 82.0 82.6 87.8 89.8 87.5 97.1 99.5 100.6 103.4 108.3 106.0 110.3 114.5 Italy 113.0 110.6 103.0 103.2 94.3 95.0 93.6 84.9 85.8 86.5 88.0 85.0 83.2 Japan 110.5 123.2 128.6 130.3 132.3 109.3 101.4 98.2 109.3 104.0 101.2 103.2 103.8 Korea Republic 59.2 57.1 66.0 67.1 68.5 72.7 77.1 80.7 81.2 84.6 85.0 80.6 82.7 Lithuania 41.4 35.2 34.3 38.4 38.7 42.0 43.6 40.1 41.6 45.1 48.7 49.5 52.5 Luxem‑ bourg 316.7 307.7 300.2 309.4 287.3 303.7 313.2 284.8 289.3 287.2 296.4 290.3 304.2 Latvia 45.5 36.6 32.5 36.8 37.5 40.4 41.4 38.7 39.7 41.9 45.4 45.0 46.3 Mexico 27.8 23.9 26.5 27.3 27.6 28.7 28.8 27.0 24.3 24.8 24.6 25.2 21.9 Nether‑ lands 159.8 156.6 145.7 144.8 134.8 139.5 139.2 126.9 127.6 129.9 134.8 132.8 137.5 Norway 268.7 238.4 250.8 268.9 273.2 275.1 255.7 208.9 195.5 202.0 209.1 191.9 176.9 New Zealand 86.6 84.1 96.3 102.6 107.6 114.9 117.5 108.5 111.2 115.0 110.1 108.2 108.9 Poland 38.8 34.4 36.1 37.1 35.2 36.6 37.6 35.3 34.5 37.1 39.3 39.7 41.1 186 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Portugal 68.9 68.8 64.4 62.0 55.3 57.9 58.2 54.1 55.4 57.4 59.9 58.9 58.9 Slovak Republic 51.8 49.3 47.9 49.1 47.0 48.8 49.2 45.8 45.8 46.8 49.2 48.8 50.3 Slovenia 76.2 73.6 67.2 67.1 60.9 62.8 63.8 58.7 60.1 62.8 66.4 65.7 67.0 Sweden 155.6 140.0 151.2 162.4 156.2 163.4 158.2 144.8 144.1 143.9 138.8 131.5 137.2 Turkey 30.3 27.1 30.7 30.5 31.7 33.7 32.0 30.9 30.2 28.3 24.0 23.1 22.4 USA 134.1 140.4 138.6 133.3 138.9 141.9 145.1 159.8 160.9 160.8 160.3 165.2 166.8 OECD members 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Armenia 11.1 8.9 9.2 9.4 9.9 10.3 10.5 10.1 10.0 10.5 10.7 11.7 11.2 Azerbaijan 15.4 14.8 16.7 19.2 20.2 21.1 20.8 15.5 10.8 11.1 12.0 12.2 11.1 Belarus 17.7 16.0 17.2 17.4 18.7 21.3 21.9 16.7 13.9 15.4 16.1 17.3 16.8 Georgia 9.2 8.4 9.2 10.7 11.9 12.4 12.5 11.3 11.3 11.7 12.0 11.9 11.2 Kazakh‑ stan 23.4 21.4 25.9 31.1 33.3 37.1 33.8 29.5 21.4 24.7 24.9 24.8 23.8 Kyrgyz Republic 2.7 2.6 2.5 3.0 3.2 3.4 3.4 3.1 3.1 3.3 3.3 3.5 3.1 Moldova 5.9 5.7 7.0 7.9 8.2 8.9 8.8 7.7 8.0 9.4 10.8 11.4 11.9 Russian Federa‑ tion 32.2 25.5 30.5 38.2 41.5 42.7 37.1 26.2 24.1 28.7 28.7 29.1 26.6 Tajikistan 2.0 2.0 2.1 2.3 2.6 2.8 2.9 2.7 2.2 2.3 2.2 2.3 2.3 Turkmeni‑ stan 10.8 12.0 12.7 15.1 18.0 19.5 21.0 18.1 17.7 17.6 17.7 19.3 – Ukraine 10.8 7.6 8.5 9.5 10.4 10.8 8.2 6.0 6.1 7.1 7.9 9.3 9.8 Uzbeki‑ stan 3.0 3.6 4.7 5.1 5.8 6.1 6.6 7.3 7.1 4.9 3.9 4.4 4.4 Source: compiled by the author using data from the World Bank Group (2021). Only theRussian Federation, Kazakhstan, andBelarus had thehighest GDP per capita (incurrent US$) among theCIS countries from 2008to2020. Nonetheless, it was near‑ ly 4–5 times lower than theOECD average. This disparity is 40 or more times greater inother FSU countries. Kazakhstan’s GDP per capita (current US$) in2020 was only 23.8%oftheOECD average, avalue that had not seen marked changes since 2008. Fur‑ thermore, thesmallest lag ofthis indicator from OECD countries inKazakhstan was only 37.1%in2013. Theeconomic inequality that exists intheFSU countries can be explained from both thestandpoint ofinstitutional theory andtheraw material model ofeconomic development. When thecauses ofdifferentiation andinequality intheFSU 193 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan ofdetermination is R=0.215, based ondata from 2008to2020 (Figure1). This suggests that economic growth is still themost important factor influencing thepopulation’s real income. Figure 1. Relationship between Gross Domestic Product per capita and share of population living below the poverty line, 2008–2020 Source. calculated by the authors based on data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (2021) Thedynamics ofincome inequality andliving standards are also affected by how diverse thepopulation’s sources ofmonetary income andexpenditures are. It is thought that thehigher thelevel andmore complex thestructure ofincome andex‑ penditure, themore thehousehold sector can influence theprocess ofmaking mar‑ ket‑relevant decisions. According tothedata inTable6, theshare ofreceipts from social payments has been increasing recently: it was 16.6%ofthepopulation’s cash income in2015 and28.6% in2020(Table6). Thegrowing contribution ofsocial payments tofamily income is primarily due totwo factors: thedesire toprovide thepopulation with acertain level ofinclu‑ sive economic growth (theaverage annual growth ofGDP andreal money income ofthepopulation during this period was 3% and2.7%, respectively) andanin‑ crease inthenumber ofpeople intheolder age group. Most ofKazakhstan’s elder‑ ly stop working, relying onstate retirement benefits andassistance from close rela‑ tives. During theperiod inquestion, thenumber ofpension recipients increased by 246,860, a12.46% increase. During thesame period, thepopulation grew atarate of5.53%, while theemployed population grew atarate of4.12%, implying that therate ofgrowth inthenumber ofpensioners was 2.25 and3.02 times faster, re‑ spectively. Theaverage income ofpeople ofretirement age is significantly lower than that ofworking‑age people: theaverage pension toaverage wage rate forthis period 194 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek ranged between 29.7 and33.7%. Inthese circumstances, thestate attempted toslow therise ofinequality by increasing theshare ofsocial transfers. Table 6. The structure of monetary incomes of the surveyed households in Kazakhstan, in % 2015 2016 2017 2018 2019 2020 Cash income – total 100 100 100 100 100 100 Including Remuneration of employees (wages) 69.3 68.0 65.7 63.3 61.9 57.9 Income from entrepreneurial activity and self‑employ‑ ment (except agricultural) 8.7 8.1 8.7 8.9 8.6 7.6 Income from agricultural activities (income from the sale of agricultural products, feed, livestock, etc.) 2.1 2.1 2.0 2.0 1.8 1.6 Social benefits 16.6 18.2 19.7 21.6 23.8 28.6 Retirement benefits 13.7 14.9 16.4 18.3 20.3 23.5 Other income sources 3.3 3.6 3.9 4.2 3.9 4.3 Source: Eurasian Economic Commission (2021). Meanwhile, thefocus ontheclose relationship between social policy andthechoice ofamodel forthecountry’s economic growth is more important. Inparticular, it is im‑ portant topay attention tohow thestate regulates thecountry’s labor market. Incompari‑ son todeveloped countries, adistinguishing feature ofKazakhstan’s current labor market model is its certain adaptation tosharp fluctuations indemand, primarily due towage changes rather than changes inemployment. Thegovernment’s policy goal is tomaintain high employment andlow unemployment inthecountry attheexpense oflow labor pro‑ ductivity andlow wages. Therefore, social policy should emphasize improving thequality rather than thequantity ofavailable labor. Adecisive shift toward stimulating thecrea‑ tion ofhigh‑quality new jobs is required, as it is anecessary condition forthedevelop‑ ment ofastable middle class. Inthelong run, reducing income inequality inKazakhstan can serve as anadditional driver tosustain economic growth andincrease thecountry’s global competitiveness, gradually bringing it closer tothecharacteristics ofdeveloped countries. Discussion Globally, wealth inequality is still severe. TheMiddle East andNorth Africa have thehighest levels ofinequality, with therichest 10%ofthepopulation receiving near‑ ly 60%oftheregion’s total income. Sub‑Saharan Africa ranks second with 57%, Latin 195 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan America ranks third with 55%, andSouth andSoutheast Asia ranks third with 53%. IntheRussian Federation andCentral Asia, about 10%oftherichest citizens receive 48%ofthetotal income, while inNorth America, it is 45%. InEurope, where therich‑ est 10%ofthepopulation account foronly 36%oftotal income, inequality is theleast pronounced (World Inequality Lab 2021a). Global trends undoubtedly have aneffect onhow opportunities andresources are dis‑ tributed. Certain megatrends have thepotential tohelp equalize opportunities, while others have thepotential toexacerbate income inequality, primarily through their impact onlabor markets. However, their impact is not set instone. Inequality lev‑ els andtrends vary even among countries atthesame level ofeconomic development andare affected equally by trade, technological innovation, andeven theeffects ofcli‑ mate change (Department ofEconomic andSocial Affairs oftheUnited Nations Sec‑ retariat 2020). Successful examples ofinequality reduction highlight theimportance ofnational policies andlocal institutions. Identifying theroot causes ofinequality is critical todeveloping effective policy solutions. However, theanswer tothequestion ofwhether or not it is appropriate totake action against income inequality is somewhat influenced by what is consid‑ ered thesource ofinequality (Fadda andTridico 2016). Financial inclusion is amajor driver ofeconomic growth. Therefore, when developing public policy, it is critical topay special attention tofinancial sector reforms toensure long‑term economic growth. Tostimulate economic growth, governments andpolicy‑ makers must address thebarriers tofinancial services access (Sethi andAcharya 2018). Furthermore, understanding thelinks between financial inclusion, poverty, andeco‑ nomic growth will assist policymakers indesigning andimplementing programs that in‑ crease access tofinancial services, thereby reducing poverty andincome inequality. Inequality stems from unequal power. Those who have more assets have more power than those who donot (Yates 2016). Thedifference intheaverage Gini coefficient between thefive richest andfive poorest countries increased by 37.8% inthefinal year ofthetwentieth century compared to1990. Consequently, despite high rates ofreal economic growth inthefastest growing group ofcountries that includes anumber ofdeveloping countries (notably China andViet‑ nam), most other developing countries lagged increasingly behind during thelast decade ofthelast century. This, coupled with thecontinued impoverishment ofpoor andhighly indebted countries andsharp economic declines inthetransition economies that were once part oftheformer Soviet Union, resulted inmarked increases ininequality world‑ wide (Čaušević 2017). Intheaftermath oftheSoviet Union’s disintegration, income inequality increased atanunprecedented level. This is, infact, one ofthereasons formany Russians’ dissat‑ 196 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek isfaction with thecountry’s modern economic system (Libman andObydenkova 2019). Inequality, ontheother hand, is avalid outcome. It results from processes inwhich theunderlying causes andconsequences ofdeficiencies inthepolitical system lead toin‑ creased economic instability (Stiglitz 2015). There has been no long‑term andwidespread economic growth intheRussian Feder‑ ation since its partial market economy transition. Thegains ofeconomic growth are concentrated atthetop oftheincome distribution, trapping large segments ofthepop‑ ulation inlow‑income situations. While extreme poverty has been largely eradicated, approximately 40%ofthepopulation struggles topurchase anything beyond thebare necessities. Inthelowest‑income decile, food accounts fornearly half ofhousehold budg‑ ets. Public social spending, which is becoming alarger proportion oftotal income, is characterized by alack ofprogressiveness (Remington 2019). Most ofit is non‑cash but retains theSoviet‑era categorical structure. Income inequality andinstitutional reforms have astatistically significant andnon‑lin‑ ear relationship. Reforms harmed theincome distribution atthestart ofthetransi‑ tion, but after reaching acrucial point inreform progress, institutional improvements helped stabilize theincome distribution. Thepersistence ofhigh income inequality dur‑ ing thesecond transition period can be explained by theemergence oftolerance forin‑ equality, which coincides with theshift from shock therapy toinstitutional reforms based onimplementing European Union legislation. Increasing income mobility anden‑ couraging meritocratic values are critical factors inpost‑transitional tolerance forin‑ equality (Josifidis, Supic, andGlavaski 2018). Consequently, thedynamics ofinequali‑ ty andredistribution inCentral andEastern European countries should be considered inthecontext ofnot only economic evolution, but also theemergence ofsocial con‑ ventions inwhich ahigh concentration ofincome is not justified but appears tobe ac‑ cepted as anunavoidable part ofthenational economy’s integration intotheEuropean andglobal economies. Theconcept ofproviding aminimum inclusive income attheEuropean level, wheth‑ er unique or not, is aformula forthetrue identity oftheEuropean model, anadaptive model inits evolution toglobal changes ineconomic andsocial needs (Jianu etal.2021). Indeed, concern foraninclusive minimum income is both anexpression oftherelative‑ ly recent concern forcreating aharmonious economic space developed across theEu‑ ropean Union andanother formula aimed ataddressing theconsequences ofsocial in‑ justice. 197 Global Income Inequality – A Case Study of OECD Countries and Kazakhstan Conclusion Thepurpose ofthis study was toexamine thecharacteristics ofeconomic inequality inKazakhstan inthelight ofglobal trends affecting its growth. Toachieve thegoal, amulti‑country quantitative study was designed andimplemented. Themethodologi‑ cal basis ofthestudy was acomparative analysis, which was implemented with thehelp ofeconometric andeconomic‑statistical methods. Twelve countries oftheformer Soviet Union and38 OECD member countries were chosen fortesting. Theresults support thehypothesis that there is alink between theindicator ofgross do‑ mestic income per capita andthepercentage ofpeople living onless than thesubsistence level inKazakhstan. Simultaneously, there was no statistically significant relationship between high levels ofincome inequality andthedynamics ofeconomic growth. Inthis way, specific suggestions about how toimprove public administration’s attitude toward addressing income inequality problems can be issued. Cross‑country analysis also revealed astatistically significant (p < 0.05) relationship between thelevel ofincome concentration inthe10% group andeconomic growth inIceland (r=0.67) andBelarus (r=0.65). Kazakhstan did not show this correla‑ tion, although therelationship between thelevel ofincome concentration inthe10% group andthegross product per capita was confirmed. According tothedata presented above, there is astable inverse relationship between thedynamics ofGDP growth andthevalues ofKazakhstan’s population’s real money in‑ comes. Thecorrelation coefficient between them is r=–0.46, andthedetermination coef‑ ficient is R=0.215, based ondata from 2008to2020. This suggests that economic growth is still themost important factor that influences thepopulation’s real income. Only theRussian Federation, Kazakhstan, andBelarus had thehighest GDP per cap‑ ita (current US$) among theCIS countries from 2008to2020, according tothestudy. However, it remained nearly 4–5 times lower than theaverage fortheOECD coun‑ tries. This difference is 40 or more times greater inother FSU countries. Kazakh‑ stan’s GDP per capita (current US$) in2020 was only 23.8%oftheOECD average, andtheindicator had not seen marked changes since 2008. Furthermore, thesmallest lag ofthis indicator from OECD countries inKazakhstan was only 37.1% in2013. Broadly, thereasons fortheFSU countries’ differentiation andinequality interms ofGDP per capita (current US$) primarily relate totheunderdevelopment ofmarket economic institutions, as well as thehigh share ofresource industries inthestructure ofthese countries’ national economies. Another aspect ofeconomic inequality is thein‑ come disparity between different strata ofthepopulation, which is linked tothis inava‑ riety ofways. 198 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek Asuccessful resolution ofthepopulation’s income inequality is aprecondition forKa‑ zakhstan’s admission to the OECD’s group of economically developed countries. Inthefuture, overcoming inequality could become akey driver ofthecountry’s eco‑ nomic growth. Theprimary directions forreducing income inequality are determined by thestate’s social policy, which is inextricably linked tolabor market regulation andad‑ justment tothecreation ofnew, highly productive jobs. Acritical aspect oftheproblem ofincome inequality is finding theright balance between measures toachieve thede‑ sired level ofinequality andtheincreasing role ofsocial transfers. Inthefuture, theresults ofthestudy will be put intopractice by familiarizing Kazakh‑ stan’s government experts with thedeveloped proposals forenhancing thestate’s poli‑ cy ofovercoming economic inequality andestablishing theconditions forsustainable economic growth. Inaddition, theresults ofthis study are ofscientific interest, pri‑ marily interms oftheformation ofdirections forfurther research, including thestudy ofthemain economic factors that influence thelevel ofinequality andresearch ontheef‑ fectiveness ofpublic policies toovercome inequality. References Aiyar, S., Ebeke, C. (2020), Inequality ofopportunity, inequality ofincome andeconomic growth, “World Development”, 136, https://doi.org/10.1016/j.worlddev.2020.105115 Breunig, R., Majeed, O. (2020), Inequality, poverty andeconomic growth, “International Eco‑ nomics”, 161, pp.83–89, https://doi.org/10.1016/j.inteco.2019.11.005 Bureau ofNational Statistics oftheAgency forStrategic Planning andReforms oftheRepublic ofKazakhstan (2021), Basic socio‑economic indicators oftheRepublic ofKazakhstan, https:// stat.gov.kz/for_users/dynamic (accessed: 10.06.2021) [inRussian]. Chancel, L., Piketty, T. 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World Inequality Lab (2021a), TheWorld Inequality Report 2022, https://wid.world/news‑artic le/world‑inequality‑report‑2022/ (accessed: 10.06.2021). World Inequality Lab (2021b), World Inequality Database (WID.world), https://wid.world/da ta/ (accessed: 10.06.2021). 200 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek Yates, M.D. (2016), TheGreat Inequality, Routledge, London–New York, https://doi.org/10.43 24/9781315645841 Zubarevich, N.V. (2018), Concentration ofthePopulation andtheEconomy intheCapitals ofPost‑Soviet Countries, “Regional Research ofRussia”, 8(2), https://doi.org/10.1134/S207 9970518020107 Globalne nierówności dochodów – studium przypadku krajów OECD i Kazachstanu W artykule przedstawiono wyniki badania sposobu powstawania nierówności ekonomicznych w Kazachstanie w kontekście globalnych trendów w rozwoju kraju. Podstawą metodologiczną pracy była analiza porównawcza krajów byłego Związku Radzieckiego (FSU) i OECD pod kątem rozwoju gospodarczego i nierówności w kontekście globalnych zmian i trendów, realizowana za pomocą metod ekonometrycznych i ekonomiczno‑statystycznych. Badanie wykazało bezpo‑ średnią istotną statystycznie (p < 0,05) korelację pomiędzy poziomem koncentracji dochodów grupy 10% populacji a wzrostem gospodarczym Islandii (r = 0,67) i Białorusi (r = 0,65). W przy‑ padku Kazachstanu nie stwierdzono takiej korelacji. Jednak w Kazachstanie ustalono związek między koncentracją dochodów grupy 10% populacji a produktem krajowym brutto na miesz‑ kańca. Dynamika wzrostu PKB i wartości realnych dochodów pieniężnych ludności Kazachstanu wykazują stabilną odwrotną zależność. Współczynnik korelacji między nimi, obliczony na podsta‑ wie danych z lat 2008‑2020, wynosi r = –0,46, a współczynnik determinacji wynosi R = 0,215. Sugeruje to, że wzrost gospodarczy jest nadal najważniejszym czynnikiem wpływającym na real‑ ne dochody ludności. Wyniki badania znajdą zastosowanie w praktyce dzięki zapoznaniu urzęd‑ ników rządowych z opracowanymi propozycjami wzmocnienia polityki państwa w zakresie prze‑ zwyciężania nierówności gospodarczych i stworzenia warunków dla zrównoważonego wzrostu gospodarczego. Ponadto wyniki tych badań będą interesujące dla nauki gdyż wskazują nowe kierunki dalszych badań. Słowa kluczowe: rozwój, dystrybucja, instytucje, gospodarka przechodząca transformację, bogactwo Global Income Inequality – A Case Study of OECD Countries and Kazakhstan 201 Appendix 1. Top 10% pre‑tax national income share in OECD countries 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Australia 0.2977 0.3119 0.3137 0.3059 0.3182 0.3293 0.327 0.3286 0.3251 0.3356 0.3366 0.336 0.336 Austria 0.3553 0.3424 0.3472 0.3386 0.3189 0.3212 0.3408 0.3331 0.3417 0.3338 0.3352 0.3388 0.3385 Belgium 0.3172 0.3093 0.3135 0.3114 0.3137 0.3163 0.3187 0.3183 0.3211 0.3183 0.3286 0.3289 0.3289 Canada 0.4072 0.3935 0.4022 0.4036 0.399 0.409 0.4128 0.4132 0.3941 0.4095 0.4086 0.407 0.407 Chile 0.5909 0.5889 0.6081 0.6274 0.6174 0.6075 0.6016 0.5957 0.5924 0.5891 0.5891 0.5891 0.5891 Colombia 0.5395 0.5368 0.5342 0.5263 0.5062 0.5123 0.5142 0.5015 0.502 0.5067 0.5146 0.5146 0.5146 Costa Rica 0.529 0.5237 0.4801 0.4977 0.4976 0.4992 0.4997 0.5064 0.5167 0.4983 0.5125 0.501 0.501 Czech Republic 0.3215 0.2984 0.2978 0.29 0.3033 0.2966 0.3029 0.3072 0.2979 0.2953 0.2877 0.2854 0.2857 Denmark 0.3007 0.29 0.3156 0.3165 0.3175 0.3276 0.3349 0.3301 0.333 0.3333 0.3328 0.3364 0.3386 Estonia 0.3753 0.3439 0.359 0.3718 0.3912 0.388 0.3841 0.3579 0.3621 0.3534 0.3595 0.3462 0.3474 European Union 0.3587 0.3588 0.3536 0.3555 0.3561 0.3603 0.3611 0.3593 0.3591 0.3588 0.3572 0.3553 0.3551 Finland 0.3406 0.3228 0.3264 0.3235 0.3177 0.3153 0.3205 0.3293 0.329 0.3386 0.3361 0.334 0.3399 France 0.3369 0.3219 0.3272 0.3336 0.3251 0.3237 0.3243 0.3247 0.3229 0.32 0.3201 0.3225 0.3223 Germany 0.3675 0.3724 0.3668 0.3678 0.3632 0.3776 0.3826 0.3818 0.381 0.3778 0.3729 0.3722 0.3707 Greece 0.3315 0.3251 0.3374 0.3142 0.3177 0.3329 0.3574 0.3538 0.3467 0.3383 0.3276 0.3253 0.3261 Hungary 0.3415 0.332 0.3367 0.328 0.3181 0.3327 0.3288 0.3295 0.3306 0.3357 0.3359 0.3395 0.3384 Iceland 0.2925 0.2768 0.2677 0.2733 0.2772 0.2968 0.2955 0.2949 0.2916 0.2908 0.2908 0.2908 0.2908 Ireland 0.322 0.3247 0.3234 0.3245 0.31 0.3227 0.3264 0.3498 0.3559 0.3488 0.354 0.3516 0.3518 Israel 0.5226 0.5254 0.5282 0.5223 0.5166 0.5066 0.4974 0.4944 0.4915 0.4915 0.4915 0.4915 0.4915 Italy 0.3072 0.3041 0.3089 0.3129 0.3116 0.3084 0.3087 0.3075 0.3215 0.3289 0.3285 0.3255 0.3221 Seisembay Jumambayev, Almazhan Dzhulaeva, Sariya Baimukhanova, Guliya Ilyashova, Aidana Dosmbek 202 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Japan 0.4507 0.4401 0.4495 0.4473 0.4478 0.4493 0.449 0.45 0.4493 0.4489 0.4489 0.4489 0.4489 Korea 0.4614 0.457 0.4659 0.4662 0.4622 0.46 0.4605 0.464 0.4664 0.4671 0.4666 0.4645 0.4645 Latvia 0.3762 0.3802 0.3622 0.3892 0.3841 0.3824 0.3663 0.3558 0.3381 0.3595 0.3533 0.3444 0.3452 Lithuania 0.3725 0.3732 0.3473 0.348 0.3731 0.3797 0.4113 0.3719 0.3702 0.3831 0.3713 0.3652 0.3657 Luxembourg 0.3847 0.3332 0.3668 0.3541 0.3409 0.333 0.328 0.3252 0.3283 0.338 0.333 0.3353 0.3353 Mexico 0.5848 0.5819 0.5789 0.5863 0.5936 0.5907 0.5878 0.5843 0.5807 0.5771 0.5735 0.5735 0.5735 New Zealand 0.2963 0.3151 0.3117 0.326 0.343 0.3324 0.3338 0.3383 0.337 0.3445 0.3465 0.3457 0.3457 Norway 0.3412 0.3092 0.3252 0.3276 0.3297 0.324 0.322 0.3064 0.3052 0.3083 0.3189 0.3011 0.2959 Poland 0.3783 0.3644 0.3662 0.3698 0.3674 0.3658 0.3726 0.3778 0.3743 0.371 0.3746 0.3764 0.3775 Portugal 0.3806 0.3747 0.3794 0.3827 0.3667 0.3725 0.3731 0.3716 0.373 0.3764 0.3674 0.3651 0.3521 Slovakia 0.298 0.2956 0.3177 0.3024 0.2956 0.3252 0.2994 0.3152 0.2929 0.2726 0.274 0.269 0.265 Slovenia 0.3041 0.2978 0.2986 0.2926 0.2947 0.293 0.2996 0.2902 0.2939 0.2946 0.2958 0.2952 0.2958 Spain 0.3476 0.3588 0.3434 0.3406 0.3464 0.3469 0.3488 0.3507 0.3489 0.3496 0.346 0.348 0.3448 Sweden 0.3179 0.3028 0.3144 0.3092 0.298 0.2989 0.3014 0.3096 0.2924 0.3014 0.2945 0.2961 0.3078 Switzerland 0.3118 0.3168 0.3364 0.3389 0.3288 0.33 0.3282 0.3297 0.328 0.3195 0.3189 0.3256 0.3249 Turkey 0.5017 0.5187 0.5124 0.5147 0.5151 0.5086 0.5173 0.524 0.5397 0.5518 0.5593 0.5447 0.5447 United Kingdom 0.3689 0.385 0.3464 0.3554 0.3647 0.3866 0.3669 0.356 0.3562 0.3592 0.3593 0.3564 0.3567 USA 0.438 0.4259 0.4388 0.4447 0.4555 0.4506 0.4567 0.4568 0.4543 0.4535 0.4563 0.4546 0.4546 Source: data from World Inequality Lab (2021b).