Innovative clusters of global trade leadership

Tsymbal, Liudmyla; Moskalyuk, Nataliya; Gromenkova, Svitlana; Chaban, Vitalii Article Innovative clusters of global trade leadership Comparative Economic Research. Central and Eastern Europe Provided in Cooperation with: Institute of Economics, University of Łódź Suggested Citation: Tsymbal, Liudmyla; Moskalyuk, Nataliya; Gromenkova, Svitlana; Chaban, Vitalii (2023) : Innovative clusters of global trade leadership, Comparative Economic Research. Central and Eastern Europe, ISSN 2082-6737, Lodz University Press, Lodz, Vol. 26, Iss. 2, pp. 71-84, https://doi.org/10.18778/1508-2008.26.13 This Version is available at: https://hdl.handle.net/10419/289736 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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/ 71 Innovative Clusters of Global Trade Leadership Liudmyla Tsymbal https://orcid.org/0000‑0002‑0873‑9227 Dr., Prof., Prof. of the Department of International Economy, Kyiv National Economic University Named After Vadym Hetman, Kyiv, Ukraine, e‑mail: [email protected] Nataliya Moskalyuk https://orcid.org/0000‑0003‑3758‑935X Ph.D., Deputy Director of the Institute of Business Education, Assoc. prof. of the Department of International Economy, Kyiv National Economic University Named After Vadym Hetman, Kyiv, Ukraine e‑mail: [email protected] Svitlana Gromenkova https://orcid.org/0000‑0003‑1711‑447X Ph.D., Assoc. Prof. of the Department of International Economy, Kyiv National Economic University Named After Vadym Hetman, Kyiv, Ukraine, e‑mail: svetlanagromenk[email protected] Vitalii Chaban https://orcid.org/0000‑0002‑4353‑4374 Ph.D., Professor of the Department of Business Economics and Entrepreneurship, Kyiv National Economic University Named After Vadym Hetman, Kyiv, Ukraine, e‑mail: pokeragr[email protected] Abstract The formation of a new global system and systemic global interdependence has generat‑ ed new competitiveness factors for market participants, determining their appropriate stra‑ tegic behavior to ensure a highly competitive position and leadership. Therefore, the pur‑ pose of the study is to identify the countries of intellectual leaders in the global market and the factors that influence the positions that countries achieve in terms of leadership. The following research methods were used: multifactor regression models, cluster analysis, and comparative analysis. Based on the authors’ methodology for assessing countries’ intel‑ lectual leadership, the clustering of countries in the global economy is determined. The eval‑ uation algorithm was based on three levels: 1) resources, 2) the intermediate results of intel‑ lectual activity, and 3) the final results of overall progress. Using a multifactor regression model and cluster analysis, four clusters of countries were iden‑ tified according to key indicators of intellectual leadership. For each cluster, the specialization Comparative Economic Research. Central and Eastern Europe Volume 26, Number 2, 2023 https://doi.org/10.18778/1508‑2008.26.13 © 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: 2.08.2022. Verified: 10.11.2022. Accepted: 21.03.2023 72 Liudmyla Tsymbal, Nataliya Moskalyuk, Svitlana Gromenkova, Vitalii Chaban of the two countries in terms of merchandise exports was analyzed: cluster 1 – the United States and Germany; cluster 2 – Israel and Italy; cluster 3 – Brazil and Ukraine; cluster 4 – China and South Korea. Each country is assigned an index of economic complexity, and the change in position of each country within a cluster over ten years is noted. An important goal is to understand the determinants of the leadership of countries in each geo‑ graphic region. The analysis is based on the cluster analysis carried out in previous publications. The clustering of countries was carried out based on the dynamics of macroeconomic indicators over the past 15 years. Keywords: Index of economic complexity, intellectualization, clustering of countries, commodity exports JEL: I23, I25 Introduction Theformation ofanew global system based onknowledge andinformation has been accompanied by rapid change andsignificant stratification ofthecompetitive envi‑ ronment. Systemic global interdependence has generated new competitive factors forthemarket participants, determining their appropriate strategic behavior toensure ahighly competitive position andleadership. It has become akey component ofpub‑ lic economic policies andeffective management andasubject ofspecial interdiscipli‑ nary research. Reorientation toinnovative development is akey feature ofthecurrent stage ofdevelopment oftheworld economy, andit is thebasis oftheFourth Industrial Revolution. Thegoal ofeconomic followers is todevelop non‑linear innovations, which are char‑ acteristic ofthemost developed economies oftheworld. Forming aglobal innova‑ tion space is acomplex process that accompanies thecurrent stage ofdevelopment oftheglobal economy. Thefourth industrial revolution determines thefurther orien‑ tation andactivation oftechnology inproduction, robotics, andnetwork technologies, among others. Only innovation‑oriented economies are highly competitive inthese conditions, andtherole ofintellectual development factors is changing accordingly. Classically, factors oftheintellectualization oftheeconomy are education andscience; however, education ceases tobe theonly driving force forthedevelopment ofsociety (OECD 2011). Characteristics ofthemodern market include theoffshoring ofthela‑ bor market, achange inthestructure ofproduction, thedevelopment ofopportunities through technology andnetwork, andtheactive export ofservices. Expanding thescope ofintellectual leadership as away toensure competitiveness intheknowledge economy inthis aspect is seen as managing thechanging business environment. 73 Innovative Clusters of Global Trade Leadership Themodern understanding ofleadership is based onits perception as amultifaceted, multilevel, factorial, andfunctionally determined phenomenon. When there are qualita‑ tive technological changes, inparticular, super dynamic digital transformations, thenet‑ workization ofeconomic systems, socialization andgreening, global success, andthecon‑ stant progress ofbusiness organizations, countries andregions are served, first ofall, by intellectual leadership. Inthe21st century, anew composition ofits resource, scien‑ tific andtechnological, market, andcivilizational imperatives is being formed, which is most fully reflected inthesustainability andinclusiveness ofeconomic development, structural dynamics, andtheglobal competitive disposition ofcountries. Atthesame time, theworld is inapre‑paradigmatic state, which is determined by separate theories ofintellectual leadership research andhow it is implemented, intheabsence ofacom‑ prehensive conceptual justification. Inthescientific literature, one ofthemost pressing issues is thetheoretical andmethodo‑ logical understanding oftheessence ofinnovation andits drivers, andtherole ofknowl‑ edge, education, andtechnology inensuring theeconomic progress ofsociety. Inthe1950s, Solow substantiated themodel ofexogenous economic growth onthebasis oftheCobb– Douglas production function andtheHarrod‑Domar Keynesian model ofeconomic growth (Solow 1956). Inthefuture, research will begin toconsider inmore detail thevar‑ ious aspects ofthemain factors (labor, capital, land) andscientific andtechnological progress. The21st century is marked by theemergence ofradically new technologies andtrends ininformatization, digitalization, networking, andmore. Inthenew context, thestudy ofeconomic growth factors aims toidentify more segments independencies that ex ‑ plain theimpact ofICT (information andcommunication technology) onthedynam‑ ics andscale ofeconomic development. Jorgenson andVu (2005) described theimpact ofinvestment ininformation technology (IT) onthelevel ofdevelopment oftheworld’s largest economies, it is determined that thedevelopment oftechnology has led torap‑ id economic growth between 1989and2003. Inalater study, Jorgenson andVu (2010) analyzed theperiod 1989–2008. Oliner andSichel (2000) demonstrated that produc‑ tivity growth intheUS since the1990s has been associated with aninvestment inICT. ICT capital comprised 1.1%ofthe4.8% output growth rate from 1996–1999. Colecchia andSchreyer (2002) compared theimpact ofICT capital oneconomic growth innine OECD countries. Although they found significant differences inICT investment, it grew rapidly inall countries. Skorupinska andTorrent‑Sellens (2017) showed that therates ofreturn ondigital in‑ vestment are relatively much higher than those oninvestment inother physical com‑ ponents. Meanwhile, Hong (2017) showed that inSouth Korea, private ICT R&D (Re‑ search anddevelopment) investment had astronger relationship with economic growth compared topublic ICT R&D investment. However, Kretschmer (2012) showed that as‑ 74 Liudmyla Tsymbal, Nataliya Moskalyuk, Svitlana Gromenkova, Vitalii Chaban sessing theimpact ofICT very much depends onthemethodology. Nevertheless, over thelast two decades, anincrease inICT by 10% translated intohigher productivity growth, from 0.5 to0.6%. Yeganegi andNajafi (2022) analyzed theimpact ofinnovations incertain industries onthelevel ofeconomic development ofthecountry, ingeneral, andthespecializa‑ tion ofcountries. Vicente (2022) looked attheinnovative development ofcountries andclusters. Meanwhile, Hanzhi andWang (2022) researched individual economies, thedeterminants oftheir development andtheprerequisites forspecialization. This article’s purpose is toidentify thekey factors andprerequisites fortheformation ofleadership ofparticular countries through economic andmathematical modeling. Thus, animportant goal seems tobe tounderstand thedeterminants oftheleadership ofcountries ineach region oftheworld. Method ofanalysis. Theanalysis is based onthecluster analysis that was carried out inprevious publications. It was conducted based onthedynamics ofmacroe‑ conomic indicators over thepast 15 years. Added value. Based ontheanalysis, thedeterminants oftheleadership ofthecoun‑ tries intheregions were determined, theprerequisites oftheleadership ofthecountries ineach oftheregions were characterized, andthepossibilities offurther development ofthese countries intheregions andintheglobal economic space were determined. Results Theeconomic development ofcountries, its determinants, factors ofthegreatest in‑ fluence are anurgent issue ofthestudy ofeconomic science. Studies ofscientists andauthors inprevious works testify tothesignificant impact, forexample, ofthein‑ tellectualization ofeconomic activity ontheoverall development ofthecountry (Kalenyuk etal.2022). However, it requires astudy todetermine thefeatures ofde‑ velopment andits key determinants indifferent countries, which differ inthestruc‑ ture oftheeconomy, features ofeconomic activity, etc. Determining thecountry’s opportunities fordevelopment intheconditions ofinternation‑ al division oflabor, specialization, andtheformation ofglobal production andlogistics net‑ works remains animportant issue. Thedetermination ofthese prerequisites should be based onstatistical data that allow theidentification ofthecountry’s specialization factors. It was done onthebasis oftheclustering ofcountries by individual indicators. Atotal offorty countries andforty‑four indicators have been selected over ten years, which makes it pos‑ sible toassert themathematical validity ofclustering results. Theauthor’s methodology is based ontheidentification ofkey development factors atthree levels: 1) resource level (accu‑ 75 Innovative Clusters of Global Trade Leadership mulated logistical, financial, human, andintellectual potential); 2) thelevel ofintermediate results ofintellectual activity (scientific‑educational, technological, infrastructural, produc‑ tion, service, market); 3) thelevel offinal results ofthegeneral progress (dynamics ofgener‑ al economic indicators, positions inworld ratings andindices, etc.). It also makes it possible toconduct acomplex estimation andcomparison ofthebasic functional zones ofthestud‑ ied phenomenon andtofollow thedevelopment experience ofinnovative systems ofthein‑ tellectual leader states (Kalenyuk andTsymbal 2021). Intellectual leadership is quite complex indefinition andstructure, so assessing it requires asystematic approach, based onthecharacteristics ofintellectual activity. Our approach is that intellectual leadership today should be determined by levels that characterize certain stages ofintellectual activity andhave their own characteristics. There are three levels of such stages: 1) resources, 2) intellectual performance, and 3) end results. For the sake of simplicity, this study will only consider country or national economy from all possible subjects (e.g., country, region, industry, institution, corporation, etc.). Theresource level is characterized by theavailability ofbasic intellectual resources. Their presence andpotential characterize thegeneral ability ofthecountry (or any other enti‑ ty) toconduct intellectual activity. Although theavailability ofintellectual resources is animportant condition forleadership, it does not mean actual leadership. More realisti‑ cally, it may manifest itself atthenext level, which characterizes theresults obtained by thecountry. Thelevel ofresults ofintellectual activity involves evaluating specific results: patents, licenses, know‑how, andpublications, among others. Theend results concern not only purely intellectual activity, but activity ingeneral –thewhole economy or society. Thenext step intheevaluation should be toidentify key indicators ateach ofthese lev‑ els. Inour opinion, only such asystematic approach toassessing each ofthese levels us‑ ing several indicators makes it possible tocharacterize theintellectual activity andassess theoverall competitive position ofdifferent actors. Therefore, all indicators forassessing intellectual leadership consider either thepotential ofintellectual resources or theresults ofintellectual activity. Amultifactor regression model andcluster analysis were used toproduce four clus‑ ters ofcountries with common characteristics andsocio‑economic development trends inkey (static anddynamic) indicators ofintellectual leadership (Table1). This tech‑ nique allowed us toanalyze theimpact ofintellectualization indicators onGDP ineach cluster. 76 Liudmyla Tsymbal, Nataliya Moskalyuk, Svitlana Gromenkova, Vitalii Chaban Таble 1. The results of clustering countries by indicator of intellectualization Cluster 1 Cluster 2 Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Iceland, Ireland, Japan, Luxembourg, Netherlands, Norway, Sweden, Switzerland, Great Britain, USA Czech Republic, Greece, Israel, Italy, New Zealand, Portugal, Slovenia, Spain Cluster 3 Cluster 4 Brazil, Chile, Colombia, Estonia, Hungary, Latvia, Lithuania, Mexico, Poland, Russian Federation, Slovakia, Turkey, Ukraine China, Hong Kong, India, South Korea, Singapore Source: compiled by the author. As aresult oftheanalysis, it was determined that all clusters differ significantly intheset ofcountries interms ofthelevel ofsocio‑economic development andthefea‑ tures ofeconomic activity. Thefirst cluster unites countries that have ahigh level ofeco‑ nomic development anddemonstrate consistently high dynamics. Thesecond cluster includes countries with stable economies andahigh level ofsocio‑economic devel‑ opment, covering mainly theEuropean region. Thecountries ofthethird cluster are more geographically diverse andshow relatively low rates ofdevelopment. Thegroup‑ ing ofcountries intocluster 4, which includes Asian countries that have certain features ofgeneral business conduct, are characterized by extremely dynamic rates ofgrowth inashort period oftime, seems interesting. Theclustering made it possible tofind common features ofthedevelopment ofthecluster countries, andit became thebasis foridentifying key development factors andprerequisites forachieving leadership po‑ sitions globally, regionally, or sectorally. Thepaper examines theexport specialization ofindividual countries within each clus‑ ter, as well as thelevel ofeconomic complexity oftheproducts they export. Theanalysis used UN trade statistics (United Nations 2018), as well as information from theCenter forInternational Development atHarvard University (TheAtlas ofEconomic Complex‑ ity n.d.). When specializing, countries belonging tothesame cluster have significant common features intheir export profile andhave reached asimilar level ofeconomic com‑ plexity. Toanalyze thespecialization ofcountries from each cluster, two countries were selected that best demonstrate thepeculiarities ofcluster development (Theob‑ jective 2022). Wewill consider features ofthecountries ofthefirst cluster ontheex‑ ample oftheUSA andGermany. Thecommodity exports ofboth countries are dom‑ inated by high‑tech goods (machinery andtransport equipment, chemical products, cars, electronic integrated circuits, andmedicines, among others) (Table2). 77 Innovative Clusters of Global Trade Leadership Таble 2. Merchandise exports of USA and Germany, 2019 SITC* Merchandise exports by SITC USA Germany billion US$ %billion US$ % Total All commodities 1,644.276 100.0 1,493.095 100.0 0 + 1 Food, animals + beverages, tobacco 111.957 6.8 78.108 5.2 2 + 4 Crude materials + anim. & veg. oils 77.353 4.7 24.998 1.7 3 Mineral fuels, lubricants 199.591 12.1 33.128 2.2 5Chemicals 224.279 13.6 230.999 15.5 6 Goods classified chiefly by material 137.472 8.4 175.458 11.8 7Machinery and transport equipment 534.875 32.5 715.426 47.9 8Miscellaneous manufactured articles 165.843 10.1 171.380 11.5 9Not classified elsewhere in the SITC 192.907 11.7 63.598 4.3 * SITC – Standard international trade classification. Source: compiled by the United Nations (2020). TheUnited States is still theworld leader ininnovation, although today, most inno‑ vation goes toproducts made abroad. Theloss ofmanufacturing jobs intheUnited States, especially toChina’s advantage, is not just afocus onproducing cheap consumer goods using cheap labor. Over thepast six years, theshare ofChinese exports ofprod‑ ucts classified as high‑tech goods has grown tomore than 27%, while intheUS, it is less than 18%. Acomparison ofthedevelopment ofcountries’ leadership interms oftheEconomic Complexity Index (ECI), which considers thecomplexity anddiversification ofthecoun‑ try’s exports, determined sufficient proximity ofcountries within one cluster. Thus, fortheUnited States, this figure reached 1.55, andforGermany, it reached 2.09, one ofthebest results intheworld. Thepositions ofthese countries intheranking ofeco‑ nomic complexity are quite close. In2018, Germany ranked 4th out of133countries, andtheUnited States was 11th (Table3). Таble 3. Index of economic complexity, 2018 Country Index of economic complexity Ranking 2018 y. (from 133 countries) Change over 10 years 1st cluster Germany 2.09 4 – 2 USA 1.55 11 +1 78 Liudmyla Tsymbal, Nataliya Moskalyuk, Svitlana Gromenkova, Vitalii Chaban Country Index of economic complexity Ranking 2018 y. (from 133 countries) Change over 10 years 2nd cluster Italy 1.44 14 +3 Israel 1.2 20 +3 3rd cluster Ukraine 0.37 44 0 Brazil 0.21 49 – 1 4th cluster South Korea 2.11 3+8 China 1.34 18 +6 Source: The Atlas of Economic Complexity (n.d.). Thus, countries inthesame cluster have afairly close position onindicators ofeconom‑ ic proximity. If we consider the countries ofthe2nd cluster, which include Italy andIsra‑ el, in2018, they were inthesecond ten ofthe133countries intheranking. Italy ranked 14th on the ECI with 1.44; its rating improved by three places over theprevious ten years. The Israeli economy reached 20th place with an index of 1.2 The dynamics of change are thesame as Italy’s, as their rank also improved by three points. Israel is characterized by afairly high share ofmachinery andtransport equipment (26.9%oftotal exports ofgoods in2018). InItaly, it is dominated by merchandise ex‑ ports (35.9%). According totheSITC, approximately thesame share inItalian exports is occupied by products ofthe“Chemicals” group (13.2%), “Goods classified mainly by materials” (17.8%), and“Various manufactured products” (17.9%). TheTop 10 Ital‑ ian exports include medicines, cars, engine parts, andshoes (TheAtlas ofEconomic Complexity n.d.). InIsraeli exports, asignificant place is taken by goods belonging tothe“Chemicals” group (24.7%), “Goods classified mainly by materials” (29.1%), and“Various manufactured goods” (17.9%). Themain commodity items ofIsraeli ex‑ ports are processed andunprocessed diamonds, medicines, electronic integrated cir‑ cuits, andmedical equipment andinstruments. Таble 4. Merchandise exports of Israel and Italy, 2019 SITC Merchandise exports by SITC Israel Italy billion US$ %billion US$ % Total All commodities 58.489 100.0 532.684 100.0 0 + 1 Food, animals + beverages, tobacco 1.789 3.1 45.669 8.6