Reindustrialization, innovative sustainable economic development, and societal values: A cluster analysis approach

Maslyukova, Elena; Volchik, Vyacheslav; Strielkowski, Wadim Article Reindustrialization, innovative sustainable economic development, and societal values: A cluster analysis approach Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Maslyukova, Elena; Volchik, Vyacheslav; Strielkowski, Wadim (2024) : Reindustrialization, innovative sustainable economic development, and societal values: A cluster analysis approach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 12, pp. 1-14, https://doi.org/10.3390/economies12120331 This Version is available at: https://hdl.handle.net/10419/329258 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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Economies 12: 331. https://doi.org/10.3390/ economies12120331 Received: 23 October 2024 Revised: 20 November 2024 Accepted: 28 November 2024 Published: 3 December 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach Elena Maslyukova 1, Vyacheslav Volchik 2and Wadim Strielkowski 3,* 1Department of Economic Cybernetics, Faculty of Economics, Southern Federal University, Bolshaya Sadovaya str. 105, 344006 Rostov-on-Don, Russia; [email protected] 2Department of Economic Theory, Faculty of Economics, Southern Federal University, Bolshaya Sadovaya str. 105, 344006 Rostov-on-Don, Russia; [email protected] 3 Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic *Correspondence: [email protected] Abstract: Our paper explores the challenges of industrial development by examining two contrasting theoretical frameworks: (i) the mainstream economic approach which emphasizes capital accumulation, innovation, and profit maximization within a globalized context, and (ii) the classical institutional economics perspective, rooted in the so-called “Veblen Dichotomy”. The latter contrasts monetary institutions focused on short-term profits with those centered on organizational, social, environmental, and business values that foster long-term technological progress. We analyze reindustrialization processes and assess the hypothesis that the conflict between these divergent institutions and social values plays a critical role in creating an institutional environment conducive to innovation and economic development. Our research highlights how technological advancements contribute to the physical and geographical fragmentation of industrial activities, thereby driving the spatial restructuring of economies. Furthermore, this paper addresses the growing prominence of intangible production, noting how deindustrialization, driven by shifts toward service-based economies, is closely related to the broader processes of globalization. The paper employs cluster analysis to explore the links between reindustrialization, institutional dynamics, and societal values across four groups (clusters) of countries. Our results reveal differences and similarities between these groups of countries and offer policy implications and suggestions for improvements. These results might be of special interest for relevant stakeholders and policymakers, as well as for researchers focused on innovative industrial and economic development. Keywords: reindustrialization; industrial development; sustainable development; institutional economics; societal values; cluster analysis 1. Introduction In modern economic theory, the problems of industrial development are most often analyzed within the framework of the dominant mainstream approach based on the analysis of the impact of capital accumulation and the introduction of innovations in the context of globalization (Romano and Traù2024;Qu and Yang 2023). The main incentives in this approach to industrial development lie in the plane of the “monetary economy”, focused on obtaining maximum profit (Hadži´c and Zekovi´c 2022;Moczadlo 2020). However, in classical or original institutional economics (OIE), the approach based on the theoretical concept of the Veblen Dichotomy (an approach that distinguishes between two types of economic activities—pecuniary and industrial—through highlighting a fundamental tension within economic behavior in capitalist societies) has traditionally retained great influence (Waller 2021). This approach contrasts “monetary” institutions focused on short-term goals with institutions associated with organizational, social, environmental, and business values, and ensuring conditions for long-term technological development (Valentinov 2023). Economies 2024,12, 331. https://doi.org/10.3390/economies12120331 https://www.mdpi.com/journal/economies Economies 2024,12, 331 2 of 14 Reindustrialization processes have become increasingly relevant in conditions where dysfunctions in the development of national industry have become obvious in terms of issues of economic security and sustainable development (Nagy et al. 2020;Sato and Kuwamori 2024). And here, examining the problems of the Veblen Dichotomy could be productive for identifying institutional factors influencing modern reindustrialization processes. The problematic of the Veblen Dichotomy in a historical context is associated with the analysis of the influence of social factors on the industrial structure of the economy and economic development in general. In this context, the ideas of Karl Marx and Marxists had the greatest influence in the pre-Veblen period since they predate Veblen’s work on the influence of social and historical factors, influencing the industrial and even theoretical structure of the economy and economics (see Marx 2024;Howard et al. 1988). The problematic of the Veblen Dichotomy is devoted to the issue of contradictions between the short-term incentives of the monetary economy and the long-term prospects for the development of regulatory institutions, education, and the innovation system that ensure sustainable development (Mindel et al. 2024;Kivimaa and Rogge 2022). Modern studies using the concept of the Veblen Dichotomy emphasize the limitations of contractual market mechanisms and the profit motive and the need to analyze the development of modern industrial production along with institutional factors and social values (Hodgson 2023a,2023b;Inglehart 2020). As Ramazzotti (2014, vol. 89) puts it, the dichotomy applies not just to a business’s choices about product volume and composition but also to its management of labor, technology, and overall knowledge. Regarding institutional and technological changes in the modern globalized economy, it is also necessary to clearly understand where approaches based on underestimating the role of public policy can lead (Volchik et al. 2023). It is the actions of interest groups and elites that largely determine the formation of the trajectory, which, due to increasing returns and network effects, can significantly affect the effectiveness of the institutional structure and technological solutions (Gareev and Eliseeva 2020). Institutional and technological changes lead to increased productivity and economic growth, but in cases where they are highly adaptive, better results in sustainable economic development are observed (Pi and Fan 2021;Veselov and Yarkin 2022). The adaptability of technologies and institutions in this context is considered through the prism of the time required for actors to adapt to new institutional structures and technological solutions (Xu et al. 2024, vol. 3). Industrial development, especially in developing countries, is largely determined by the quality and consistency of state policy (Kitieva et al. 2020). The implementation of state industrial policy is associated with significant information asymmetry, which can be viewed through the prism of the principal–agent problem (Ouyang 2006). The existence of information asymmetry also creates additional risks in terms of forecasting and evaluating various options for technological solutions. In today’s economy, an increasing portion of GDP is produced in the areas of intangible production. The growth of intangible production significantly affects the processes of creating infrastructure for material and intangible production, as well as solving complex issues of adapting to public policy (Haskel and Westlake 2018). The increasing role of non-material production over the past decades has been accompanied by the processes of deindustrialization in the economies of developed and developing countries (Rowthorn and Coutts 2013;Stojˇci´c et al. 2019; Moczadlo 2020). Institutional and technological changes are closely linked to culture. Based on extensive historical studies of culture, technology, and institutions, Mokyr (2016) comes to an interesting conclusion that innovation-driven growth relies on the direct link between culture and technology, shaped by views on nature and human–environment interactions (Mokyr 2016, vol. 16). In our paper, deindustrialization is perceived as the reduction in manufacturing industry’s share within an economy compared with other sectors. It especially impacts heavy industry or manufacturing industry and involves both social and economic aspects. It also Economies 2024,12, 331 3 of 14 leads to an increase in the importance of other sectors (e.g., services) marking significant implications for employment and economic structure, as well as social dynamics. In addition, it highlights the importance of adapting to climate change by enhancing sustainable economic development and moving towards renewable energy (RE). Figure 1below illustrates the most frequent keywords used in the definition and the concept of deindustrialization using a word cloud diagram. The search for the most frequent keywords was carried out using the definitions and concept descriptions found in six papers cited within this very text as well as in the IMF Working Paper by Rowthorn and Ramaswamy (1997); the United Nations Working Paper by Haverkamp and Clara (2019); definitions retrieved from Cambridge Dictionary (2024); Oxford Reference (2024); and relevant information from the extensive collection of content by Elsevier’s Research Topics (ScienceDirect 2024). The resulting diagram depicts a visual representation of text data that showcases the frequency of words within a given body of text with the size of each word being proportional to its frequency of occurrence (larger words are more common). Economies 2024, 12, x FOR PEER REVIEW 3 of 15 culture and technology, shaped by views on nature and human–environment interactions (Mokyr 2016, vol. 16). In our paper, deindustrialization is perceived as the reduction in manufacturing industrys share within an economy compared with other sectors. It especially impacts heavy industry or manufacturing industry and involves both social and economic aspects. It also leads to an increase in the importance of other sectors (e.g., services) marking significant implications for employment and economic structure, as well as social dynamics. In addition, it highlights the importance of adapting to climate change by enhancing sustainable economic development and moving towards renewable energy (RE). Figure 1 below illustrates the most frequent keywords used in the definition and the concept of deindustrialization using a word cloud diagram. The search for the most frequent keywords was carried out using the definitions and concept descriptions found in six papers cited within this very text as well as in the IMF Working Paper by Rowthorn and Ramaswamy (1997); the United Nations Working Paper by Haverkamp and Clara (2019); definitions retrieved from Cambridge Dictionary (2024); Oxford Reference (2024); and relevant information from the extensive collection of content by Elseviers Research Topics (ScienceDirect 2024). The resulting diagram depicts a visual representation of text data that showcases the frequency of words within a given body of text with the size of each word being proportional to its frequency of occurrence (larger words are more common). Figure 1. Word cloud diagram for deindustrialization concept. Source: own results. Deindustrialization has become one of the main objective economic processes since the end of the 20th century (Destek 2021; Shevchenko and Zhao 2022). However, the ideological foundations of deindustrialization can be found in mental models that were created by intellectual elites and used by politicians. The importance of mental models for institutional change is increasingly in the focus of institutional economists, who emphasize the problem of power (Altman 2023). Industrial production since the Industrial Revolution has depended not only on the invention of new technological solutions but also on emerging market incentives (Wisman and Smith 1999). Market incentives allow entrepreneurs to attract and focus their activities on existing and emerging markets, as well as attract financial resources to implement new technological improvements in production processes (High 2020; Di Berardino et al. 2021). The same existence of market incentives is associated with entrepreneurial initiative. The absence of entrepreneurial initiative or limitation of it in the Soviet economy had a negative impact on innovative development in those industries that were associated with broad consumer demand. Figure 1. Word cloud diagram for deindustrialization concept. Source: own results. Deindustrialization has become one of the main objective economic processes since the end of the 20th century (Destek 2021;Shevchenko and Zhao 2022). However, the ideological foundations of deindustrialization can be found in mental models that were created by intellectual elites and used by politicians. The importance of mental models for institutional change is increasingly in the focus of institutional economists, who emphasize the problem of power (Altman 2023). Industrial production since the Industrial Revolution has depended not only on the invention of new technological solutions but also on emerging market incentives (Wisman and Smith 1999). Market incentives allow entrepreneurs to attract and focus their activities on existing and emerging markets, as well as attract financial resources to implement new technological improvements in production processes (High 2020;Di Berardino et al. 2021). The same existence of market incentives is associated with entrepreneurial initiative. The absence of entrepreneurial initiative or limitation of it in the Soviet economy had a negative impact on innovative development in those industries that were associated with broad consumer demand. The deregulation of financial markets has led to a rapid growth in the share of financial services in the GDP of developed countries. This has created a situation where investments in financial assets have been accompanied by deindustrialization processes (Capello and Cerisola 2023;Lar and Taguchi 2023). Processes associated with industrial changes significantly affect not only economic relations but also transform established social practices and institutions (Jonek-Kowalska 2024;Scheiring and King 2023). For example, the process of deindustrialization has a Economies 2024,12, 331 4 of 14 significant impact on the identity and physical and mental well-being of workers associated with industrial production (Strangleman and Rhodes 2014). In modern conditions, Veblen’s dichotomy remains relevant because it addresses the question of how market incentives and institutional structures (including culture and social values) influence technological progress and, consequently, economic development. (Volchik and Maslyukova 2024). The main novelty and scientific value-added of our paper lie in its application of cluster analysis to explore the intersection of reindustrialization, institutional dynamics, and societal values across a diverse set of countries. By combining economic indicators of industrial development with attitudinal data on science and technology, the study provides a unique perspective on how value systems influence technological progress and reindustrialization. The value-added comes from the identification of country clusters which highlight distinct pathways of industrialization and offer tailored policy recommendations, helping to bridge the gap between theoretical institutional frameworks and practical economic policy. This paper is structured as follows. Section 2presents materials and methods where the main methods of research and the selection of main variables are presented. Section 3 outlines the main results of the cluster analysis. Section 4offers the discussion of results. Finally, Section 5features main conclusions and limitations, as well as the implications of the study. 2. Methodology Our methods include the analysis of the processes of reindustrialization for confirming or refuting the hypothesis stating that the conflict of different types of institutions and social values serves as one of the sources of the formation of an institutional environment that promotes the innovative development of the economy. Thanks to technological development, new technologies appear that are used in various sectors of the economy, which accelerates the growing physical and geographical fragmentation of economic and industrial activity. In other words, the processes of reindustrialization make a significant contribution to the spatial restructuring of the economy and industry. The increasing role of intangible production over the past decades has been accompanied by deindustrialization processes in the economies of developed and developing countries. Deindustrialization is also closely related to other important economic processes, such as globalization and the transition from the production of goods to the service sector. The features of reindustrialization, including the problem of the dependence of this process on the development of technologies, were considered in the works of Rothwell (1985) and Stevenson (1981). Reindustrialization is a process that is based on the close connection between science, technology, and production. The term “reindustrialization” has various meanings, but most often it refers to an increase in the share of manufacturing in added value or employment; therefore, in our study, the following quantitative indicators of reindustrialization by country for 2021 were selected as variables reflecting the level of reindustrialization of the economy: •High-technology exports (% of manufactured exports); •Manufacturing, value added (% of GDP); •Manufacturing, value added (annual % growth). These factors reflect the current level of development of technologies and the innovation environment that ensures the country’s competitiveness. The growth of technological innovation is associated with the formation of scientific and educational institutions. Changes associated with the growth of technological innovation and the formation of a knowledge society are associated with radical changes in the social organization of society and its key institutions. At the same time, new types of connections are formed, which are stimulated by both new technologies and emerging informal norms and institutions that stimulate the creative behavior of actors. Therefore, Economies 2024,12, 331 5 of 14 to identify cross-country differences, in addition to reindustrialization indicators, we use variables that characterize value differences between countries in terms of attitudes toward science and technology in society. The list of value variables is presented in Table 1. To analyze value differences across countries, we used data from population surveys from the WVS Database (https://www.worldvaluessurvey.org/, Wave 7 (2017–2021) (accessed on 20 October 2024)). Table 1. List of variables characterizing attitudes towards science and technology in society. Variable Question from WVS Database Answers Comfortable Science and technology are making our lives healthier, easier, and more comfortable. A scale from 1 to 10, where 1 is completely disagree and 10 is completely agree Opportunities Because of science and technology, there will be more opportunities for the next generation. A scale from 1 to 10, where 1 is completely disagree and 10 is completely agree Science faith We depend too much on science and not enough on faith. A scale from 1 to 10, where 1 is completely disagree and 10 is completely agree Bad effects One of the bad effects of science is that it breaks down people’s ideas of right and wrong. A scale from 1 to 10, where 1 is completely disagree and 10 is completely agree Importance It is not important for me to know about science in my daily life. A scale from 1 to 10, where 1 is completely disagree and 10 is completely agree World better All things considered, would you say that the world is better off or worse off, because of science and technology? A scale from 1 to 10, where 1 is much worse and 10 is much better Source: WVS Database (https://www.worldvaluessurvey.org/, accessed on 20 October 2024), Wave 7 (2017–2021). 3. Results For the analysis, average values for each value variable by country were used. Our sample of countries included those for which information was available at the time of the study. Unfortunately, it was not possible to include other countries in the analysis due to lack of data. Descriptive statistics for the selected variables are presented in Table 2 that follows. Table 2. Descriptive statistics for variables. Variable Mean Median Std. Dev. Min Max High_tech 14.7 8.38 15.6 0.381 64.2 Manuf_GDP 14.7 14.2 6.38 1.43 27.2 Manuf_growth 6.03 6.18 6.28 −12.2 18.6 Comfortable 7.39 7.40 0.636 5.90 8.70 Opportunities 7.52 7.50 0.589 6.30 9.00 Science faith 5.73 5.90 0.872 3.90 7.40 Bad effects 5.70 5.70 0.694 4.00 7.10 Importance 4.72 4.75 0.820 2.90 6.10 World better 6.88 6.90 0.762 5.50 8.50 Source: own results. Reindustrialization indicators are expressed through such statistical indicators as high-technology exports (% of manufactured exports), manufacturing, value added (% of GDP) and manufacturing, and value added (annual % growth) (Microsoft Excel was employed to depict the values for various countries in the figure). In modern conditions, these indicators can be considered as characteristics of the trajectory of innovative and technological development of the country. The greatest range of values is observed for the high-technology exports (% of manufactured exports) indicator, with the leaders in this indicator being the Philippines (64.2%), Singapore (60.0%), and Malaysia (51.7%), while the outsider countries, where this indicator does not exceed 2%, are Iran, Islamic Rep., Zimbabwe, Pakistan, and Jordan. The largest share of added value of production in GDP (manufacturing and value added (% of GDP)) have Republic of Korea (25.5%), Myanmar (25.6%), and Thailand (27.2%), while the lowest values for this indicator are in Economies 2024,12, 331 6 of 14 Lebanon (1.4%) and Ethiopia (4.6%). The highest growth rates of value added (annual % growth) for the period under review were observed in Argentina (15.7%), Peru (18.4%), and Turkey (18.6%), while negative growth was observed in Myanmar ( − 12.2%), Lebanon ( − 6.9%), Arab Republic of Egypt ( − 5.9%), Mongolia ( − 0.9%), and Cyprus ( − 0.8%) (see Figure 2below). Economies 2024, 12, x FOR PEER REVIEW 6 of 15 Reindustrialization indicators are expressed through such statistical indicators as high-technology exports (% of manufactured exports), manufacturing, value added (% of GDP) and manufacturing, and value added (annual % growth) (Microsoft Excel was employed to depict the values for various countries in the figure). In modern conditions, these indicators can be considered as characteristics of the trajectory of innovative and technological development of the country. The greatest range of values is observed for the hightechnology exports (% of manufactured exports) indicator, with the leaders in this indicator being the Philippines (64.2%), Singapore (60.0%), and Malaysia (51.7%), while the outsider countries, where this indicator does not exceed 2%, are Iran, Islamic Rep., Zimbabwe, Pakistan, and Jordan. The largest share of added value of production in GDP (manufacturing and value added (% of GDP)) have Republic of Korea (25.5%), Myanmar (25.6%), and Thailand (27.2%), while the lowest values for this indicator are in Lebanon (1.4%) and Ethiopia (4.6%). The highest growth rates of value added (annual % growth) for the period under review were observed in Argentina (15.7%), Peru (18.4%), and Turkey (18.6%), while negative growth was observed in Myanmar (−12.2%), Lebanon (−6.9%), Arab Republic of Egypt (−5.9%), Mongolia (−0.9%), and Cyprus (−0.8%) (see Figure 2 below). Figure 2. Reindustrialization indicators by country (2021). Source: WVS Database (https://www.worldvaluessurvey.org/, accessed on 20 October 2024), Wave 7 (2017–2021). The formation of value guidelines of economic agents occurs under the influence of various factors. Such factors include the qualitative characteristics of the institutional environment that promotes the innovative development of the economy. At the same time, the existing value attitudes formed in a particular country influence its innovative development and promote reindustrialization processes. The second block of factors reflects the specifics of countries in terms of attitudes towards science and technology in society. For example, among the value variables, the greatest spread of average values is observed for the variable Science_faith: the lowest level of agreement with the statement that “We rely too much on science and not enough on faith” is observed in Japan and the highest is -20 -10 0 10 20 30 40 50 60 70 ArgentinaArmenia Australia Bolivia Brazil Chile Colombia Cyprus Germany Ecuador Egypt, Arab Rep. Ethiopia Greece Guatemala Indonesia Iran, Islamic Rep. Jordan Japan Kazakhstan Kenya Kyrgyz Republic Korea, Rep. Lebanon Morocco Mexico Myanmar Mongolia Malaysia Nigeria Pakistan Peru Philippines Russian Federation Singapore Thailand Tunisia Turkiye United States Viet Nam Zimbabwe High-technology exports (% of manufactured exports) Manufacturing, value added (% of GDP) Manufacturing, value added (annual % growth) Figure 2. Reindustrialization indicators by country (2021). Source: WVS Database (https://www. worldvaluessurvey.org/, accessed on 20 October 2024), Wave 7 (2017–2021). The formation of value guidelines of economic agents occurs under the influence of various factors. Such factors include the qualitative characteristics of the institutional environment that promotes the innovative development of the economy. At the same time, the existing value attitudes formed in a particular country influence its innovative development and promote reindustrialization processes. The second block of factors reflects the specifics of countries in terms of attitudes towards science and technology in society. For example, among the value variables, the greatest spread of average values is observed for the variable Science_faith: the lowest level of agreement with the statement that “We rely too much on science and not enough on faith” is observed in Japan and the highest is observed in Armenia. The smallest spread of average values is observed for the Opportunities variable, while Armenia has the highest average score for this variable, i.e., the greatest number of respondents expressed a high level of agreement with the statement that thanks to science and technology, the new generation will have more opportunities. The analysis of the relationship between the indicators under consideration was carried out using correlation coefficients (the correlation matrix is presented in Figure 3). Figure 3represents a heat map that allows us to quickly assess the strength and direction of the correlation between variables. The color scale enables us to visually distinguish between positive and negative correlations (for example, bright red may indicate a strong positive correlation, while bright blue may indicate a strong negative correlation). The highest correlation is observed between the Comfortable and Opportunities variables, as well as Science faith and Bad effects. Economies 2024,12, 331 7 of 14 Economies 2024, 12, x FOR PEER REVIEW 7 of 15 observed in Armenia. The smallest spread of average values is observed for the Opportunities variable, while Armenia has the highest average score for this variable, i.e., the greatest number of respondents expressed a high level of agreement with the statement that thanks to science and technology, the new generation will have more opportunities. The analysis of the relationship between the indicators under consideration was carried out using correlation coefficients (the correlation matrix is presented in Figure 3). Figure 3 represents a heat map that allows us to quickly assess the strength and direction of the correlation between variables. The color scale enables us to visually distinguish between positive and negative correlations (for example, bright red may indicate a strong positive correlation, while bright blue may indicate a strong negative correlation). The highest correlation is observed between the Comfortable and Opportunities variables, as well as Science faith and Bad effects. Figure 3. Correlation matrix. Source: own results. The analysis of inter-country value differentiation and reindustrialization was carried out using clustering by the Ward method using the normalized Euclidean distance as a distance metric. Cluster analysis is a method of grouping multidimensional objects based on the presentation of the results of individual observations by points of a suitable geometric space with the subsequent allocation of associations and clusters. By “clusters” we mean homogeneous groups of objects, i.e., countries according to a selected set of parameters, such as the level of reindustrialization and value characteristics from the point of view of attitudes towards science and technology in society. Thus, the use of cluster analysis will help to obtain homogeneous groups of objects, i.e., countries according to the selected set of parameters, such as the level of reindustrialization and value characteristics from the point of view of the attitude towards science and technology in society. The sample includes 40 countries, which were divided into four 0.2 0.0 -0.1 0.6 0.6 -0.2 -0.2 -0.3 1.0 0.0 -0.1 0.1 -0.5 -0.4 0.5 0.4 1.0 -0.3 -0.1 -0.1 0.1 -0.1 0.2 0.7 1.0 0.4 -0.2 -0.0 -0.1 0.2 -0.1 0.1 1.0 0.7 0.5 -0.2 -0.2 0.1 -0.2 0.8 1.0 0.1 0.2 -0.4 0.6 -0.1 0.0 -0.3 1.0 0.8 -0.1 -0.1 -0.5 0.6 0.2 0.1 1.0 -0.3 -0.2 0.2 0.1 0.1 -0.1 0.4 1.0 0.1 0.0 0.1 -0.1 -0.1 -0.1 0.0 1.0 0.4 0.2 -0.1 -0.2 -0.0 -0.1 0.0 0.2 World_better Importance Bad_effects Science_faith Opportunities Comfortable Manuf_growth Manuf_GDP High_tech High_tech Manuf_GDP Manuf_growth Comfortable Opportunities Science_faith Bad_effects Importance World_better -1 -0.5 0 0.5 1 Correlation matrix Figure 3. Correlation matrix. Source: own results. The analysis of inter-country value differentiation and reindustrialization was carried out using clustering by the Ward method using the normalized Euclidean distance as a distance metric. Cluster analysis is a method of grouping multidimensional objects based on the presentation of the results of individual observations by points of a suitable geometric space with the subsequent allocation of associations and clusters. By “clusters” we mean homogeneous groups of objects, i.e., countries according to a selected set of parameters, such as the level of reindustrialization and value characteristics from the point of view of attitudes towards science and technology in society. Thus, the use of cluster analysis will help to obtain homogeneous groups of objects, i.e., countries according to the selected set of parameters, such as the level of reindustrialization and value characteristics from the point of view of the attitude towards science and technology in society. The sample includes 40 countries, which were divided into four clusters, which are more suitable than other options for research as homogeneous groups. (Figure 4). Each selected cluster represents groups of countries with similar value characteristics and levels of reindustrialization. The average values of variables by clusters and then presented in Table 3. The first cluster includes seven countries: Bolivia, Chile, Colombia, Ecuador, Guatemala, Mexico, and Peru (highlighted in blue). The countries in this cluster are characterized by the highest annual growth rates of added value of production, but the level of high-technology exports from these countries is the lowest. That is, these countries can be characterized as countries with a generally low level of innovation. The key reason may be the minimum level of measures taken to create conditions for reindustrialization in these countries. As for the value characteristics, it should be noted that the countries in this cluster have the minimum average values for the Comfortable and World better variables, but the maximum average values for the Science faith, Bad effects, and Importance variables, that is, these Economies 2024,12, 331 8 of 14 countries underestimate the importance of science and technology in terms of their impact on the quality of life, and there is a high level of distrust in scientific knowledge in terms of its importance in everyday life. Economies 2024, 12, x FOR PEER REVIEW 8 of 15 clusters, which are more suitable than other options for research as homogeneous groups. (Figure 4). Each selected cluster represents groups of countries with similar value characteristics and levels of reindustrialization. The average values of variables by clusters and then presented in Table 3. Figure 4. Results of hierarchical clustering. Source: own results. Table 3. Average values of variables by clusters. Cluster C1 C2 C3 C4 Count 7 5 13 15 High-technology exports (% of manufactured exports) 8.50 46.35 8.80 12.08 Manufacturing, value added (% of GDP) 13.39 22.90 15.89 11.52 Manufacturing, value added (annual % growth) 9.54 8.74 2.97 6.15 Comfortable 6.47 7.06 7.65 7.70 Opportunities 7.09 6.98 7.64 7.81 Science faith 6.66 5.76 4.89 6.02 Bad effects 6.33 5.7 5.05 5.98 Importance 5.49 4.82 4.01 4.95 World better 5.91 6.82 6.92 7.31 Source: own results. Figure 4. Results of hierarchical clustering. Source: own results. Table 3. Average values of variables by clusters. Cluster C1 C2 C3 C4 Count 7 5 13 15 High-technology exports (% of manufactured exports) 8.50 46.35 8.80 12.08 Manufacturing, value added (% of GDP) 13.39 22.90 15.89 11.52 Manufacturing, value added (annual % growth) 9.54 8.74 2.97 6.15 Comfortable 6.47 7.06 7.65 7.70 Opportunities 7.09 6.98 7.64 7.81 Science faith 6.66 5.76 4.89 6.02 Bad effects 6.33 5.7 5.05 5.98 Importance 5.49 4.82 4.01 4.95 World better 5.91 6.82 6.92 7.31 Source: own results.