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Article

Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach

by
Elena Maslyukova
1,
Vyacheslav Volchik
2 and
Wadim Strielkowski
3,*
1
Department of Economic Cybernetics, Faculty of Economics, Southern Federal University, Bolshaya Sadovaya str. 105, 344006 Rostov-on-Don, Russia
2
Department of Economic Theory, Faculty of Economics, Southern Federal University, Bolshaya Sadovaya str. 105, 344006 Rostov-on-Don, Russia
3
Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Economies 2024, 12(12), 331; https://doi.org/10.3390/economies12120331
Submission received: 23 October 2024 / Revised: 20 November 2024 / Accepted: 28 November 2024 / Published: 3 December 2024
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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.

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 Traù 2024; 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 (Hadžić and Zeković 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).
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; Stojcic 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 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).
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 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 2 presents 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 4 offers the discussion of results. Finally, Section 5 features 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, 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)).

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.
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 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).
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 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.
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 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.
The second cluster is represented by five countries: South Korea, Malaysia, the Philippines, Singapore, and Thailand (highlighted in red). These countries are leaders in the level of high-tech exports and the growth rate of added value of production in GDP. As for the value characteristics, it should be noted that the countries united in this cluster have the lowest average values for the Opportunities variable, confirming the existence of positive externalities that manifest themselves in underestimating the importance of science and technology in terms of their impact on new opportunities for future generations. Actors in countries with a high level of innovative development perceive positive externalities from the development of science and technology as something self-evident and familiar in everyday life. The example of some on-going and existing tech-related developments in the countries in the second cluster are the following: South Korea invests in digital transformation (AI, 5G networks, and semiconductor manufacturing), robotics, and green technologies, aiming to become carbon neutral by 2050. Singapore is a global hub for fintech, Blockchain, and AI, focusing on smart cities and sustainable urban development and leveraging IoT and renewable energy. Malaysia is active in the electronics and automotive sectors and is moving towards Industry 4.0 with investments in automation, smart manufacturing, and green energy solutions. Thailand is strengthening its position in smart electronics and electric vehicles, with policies supporting R&D in renewable energy and bioplastics. Finally, the Philippines represents a growing as a hub for business process outsourcing and software development, while also exploring opportunities in renewable energy and sustainable agriculture technologies. Their mutual international R&D collaboration, knowledge sharing, and regional integration (ASEAN initiatives) can leverage from partnership and green technologies.
The third cluster includes 13 countries: Australia, Brazil, Germany, Egypt, Indonesia, Iran, Jordan, Japan, Morocco, Myanmar, Tunisia, the United States, and Zimbabwe (highlighted in green). This cluster is characterized by the lowest values of annual growth rates of added production value with average values of high-tech exports and the share of added production value in GDP. Among the value characteristics, the lowest values for the variables Science faith, Bad effects, and Importance should be highlighted, which indicates that actors in the countries of this cluster have a positive perception of externalities from the development of science and technology. It can be assumed that with a higher level of support from formal institutions for innovation, a process of greater production of know-how and subsequent increasing returns will be launched.
The fourth cluster is the most numerous; it includes 15 countries: Argentina, Armenia, Cyprus, Ethiopia, Greece, Kazakhstan, Kenya, the Kyrgyz Republic, Lebanon, Mongolia, Nigeria, Pakistan, the Russian Federation, Turkey, and Vietnam (highlighted in orange color). This cluster is characterized by the lowest share of added production value in GDP with fairly low annual growth rates of added production value and the level of high-tech exports. A feature of this cluster is the highest values of the value variables Comfortable, Opportunities and World better, i.e., actors associate a higher level of development of science and technology with an improvement in the quality of life. In this cluster, actors are ready to support innovations and their implementation, but the production itself (both grassroots initiatives and government orders and so on) has not been launched in sufficient volume.

4. Discussion of Results

In general, our results reveal that across all four clusters outlined above, policies should align with global sustainability goals. Reindustrialization should be viewed as an opportunity to incorporate green technologies, reduce emissions, and develop industries focused on renewable energy and circular economies.
Another major implication is that protecting intellectual property is crucial for fostering innovation. All clusters should reform and strengthen their IPR frameworks to encourage innovation, safeguard technologies, and attract high-tech investments.
In addition, we find that encourage international cooperation through joint R&D initiatives, technology-sharing agreements, and trade partnerships that promote innovation and industrial development. Leveraging cross-border collaborations can accelerate reindustrialization efforts.
When it comes to the specific results in each cluster, the following patterns were identified: the first cluster encompassing low-innovation economies with high growth potential (Bolivia, Chile, Colombia, Ecuador, Guatemala, Mexico, and Peru) needs to invest in technological infrastructure. The countries in the first cluster exhibit high growth rates but low levels of high-tech exports. Governments should prioritize investments in digital and technological infrastructure, such as broadband internet, 5G networks, and innovation hubs, to enable industries to adopt advanced technologies.
In addition, the economies in the first cluster should establish PPPs that focus on fostering innovation in manufacturing and service sectors. This can be achieved by providing financial incentives for businesses that invest in research and development (R&D) and high-tech production.
The mismatch between workforce skills and industrial needs is a common barrier to reindustrialization. Governments of the countries in the first cluster should implement vocational training programs that target technical and engineering skills to better align with emerging industrial needs.
Finally, the first cluster of countries needs to create sector-specific industrial policies (e.g., for agriculture, mining, or renewable energy) that integrate technological advancement and sustainable practices to support long-term industrial growth.
As far as the second cluster combining high-tech leaders with moderate reindustrialization needs (South Korea, Malaysia, the Philippines, Singapore, Thailand) is concerned, the following recommendations can be offered: the countries in the second cluster are already leaders in high-tech exports but can further strengthen their positions by creating more dynamic R&D ecosystems. Their governments should continue to support the development of science parks, incubators, and accelerator programs to promote high-tech startups.
Given their advanced technological capabilities, these countries are well-positioned to lead in the development and adoption of green technologies. Policy incentives such as tax breaks, subsidies for green energy, and carbon credits could encourage businesses to invest in sustainable manufacturing.
To sustain technological progress, these countries should create policies that enhance the spillover effects of knowledge and innovation between academia, industry, and government. This could include grants for collaborative research projects and incentives for multinational corporations to establish R&D centers.
Furthermore, in order to remain competitive, trade policies in the second cluster should promote the export of advanced technologies and intellectual property. Facilitating free trade agreements (FTAs) with innovation hubs, coupled with IP protection reforms, will further strengthen their global position in high-tech industries.
The countries in the third cluster, which consists of the medium-innovation economies with low reindustrialization growth (Australia, Brazil, Germany, Egypt, Indonesia, Iran, Jordan, Japan, Morocco, Myanmar, Tunisia, the United States, and Zimbabwe), need to design sector-specific innovation policies that target industries where they have competitive advantages (e.g., manufacturing, agri-tech, or renewable energy). These policies should incentivize innovation in product development and supply chain optimization.
Reskilling and upskilling programs for workers, especially in manufacturing and technology sectors, should be prioritized. Policymakers should partner with industries and educational institutions to create training programs focused on digitalization, automation, and artificial intelligence (AI).
Small and medium-sized enterprises (SMEs) are crucial for driving reindustrialization, particularly in less developed regions. Governments should provide financial and regulatory support to help SMEs adopt new technologies, access international markets, and scale their operations.
In order to unlock potential growth, these countries should streamline bureaucratic processes and reduce corruption, particularly in the public procurement sector. Transparent and accountable governance can foster a business-friendly environment that attracts foreign direct investment (FDI).
Finally, the countries in the fourth cluster, which comprises economies with low industrial and technological capacity (Argentina, Armenia, Cyprus, Ethiopia, Greece, Kazakhstan, Kenya, the Kyrgyz Republic, Lebanon, Mongolia, Nigeria, Pakistan, Russia, Turkey, and Vietnam), need to focus on institutional reforms that improve governance, legal structures, and reduce corruption. Transparent institutions are essential for attracting FDI and fostering long-term industrial development.
In addition, many countries in this cluster rely heavily on resource-based industries. Policies should encourage diversification into sectors like manufacturing, renewable energy, and ICT (Information and Communication Technologies). This can be facilitated by offering incentives for domestic and foreign investment in underdeveloped sectors.
Furthermore, governments of the countries in the fourth cluster should promote the development of regional industrial clusters that bring together manufacturers, suppliers, universities, and research institutes. Industrial clusters facilitate knowledge sharing, innovation, and supply chain efficiencies. For instance, promoting specific technology or energy clusters could help generate regional economic growth.
These countries should invest heavily in education, particularly in STEM (science, technology, engineering, and mathematics) fields. Establishing international partnerships for technical education and creating scholarship programs for students in high-tech sectors can help build a capable workforce.
Last but not least, the countries from the fourth cluster might want to implement programs that provide easier access to financing for small and medium enterprises (SMEs) to adopt advanced technologies. Government-backed loans, grants, and subsidies should be introduced to reduce the financial barriers for businesses seeking to upgrade technology.

5. Conclusions

The results stemming from our analysis allowed us to identify the spatial differentiation of the distribution of economic activity in terms of reindustrialization, as well as value differences in terms of attitudes towards science and technology in society. The identification of specific models of regional reindustrialization and their evolution over time and study of their relationships with productivity dynamics should also be supplemented by the analysis of the existing conflict of technological and institutional components of modern sustainable economic development.
This paper aimed to analyze reindustrialization processes across different countries, using a cluster analysis to assess how institutional factors and social values contribute to the creation of an environment conducive to technological progress and economic development. Our research highlights the spatial restructuring of economies driven by reindustrialization and the increasing prominence of intangible production in the global economy. By examining institutional dynamics through the lens of the Veblen Dichotomy, we identified the tension between short-term profit motives and long-term industrial and technological development.
Moreover, our paper identified four distinct clusters of countries based on their reindustrialization indicators and societal attitudes toward science and technology. These clusters reflect varying levels of industrial development, high-tech exports, and growth in manufacturing value-added. We found a significant correlation between societal attitudes toward science and technology and the level of industrialization across countries. In countries with stronger support for science and innovation, industrial growth and technological development were more robust.
Our findings suggest that institutional structures, including governance, public policy, and social values, play a critical role in shaping reindustrialization efforts. Countries with adaptive institutions tend to show stronger capabilities in fostering innovation and reindustrialization. In addition, our study demonstrates how technological advancements contribute to the physical and geographical fragmentation of industrial activity, affecting the spatial distribution of economic activities globally.
When it comes to the limitations of our study, it needs to be acknowledged that we relied on a limited set of variables for both reindustrialization and value attitudes. Expanding the analysis to include additional economic and institutional variables, such as R&D investment, entrepreneurial activity, and labor market flexibility, could provide more comprehensive insights. Additionally, our analysis is based on data from 2017 to 2022, which limits the temporal scope of the study. Future research could benefit from a longitudinal analysis to track how institutional and technological dynamics evolve over time.
Another limitation is that although this study includes a diverse set of countries, it does not fully account for regional specificities, such as trade agreements, geopolitical factors, and region-specific industrial policies, which could have significant effects on reindustrialization processes.
When it comes to the policy implications and suggestions, our findings indicate that countries require tailored reindustrialization policies depending on their cluster. High-tech leaders should focus on fostering sustainable technologies and enhancing R&D ecosystems, while low-innovation economies need to prioritize investments in technological infrastructure, vocational training, and institutional reforms. Across all clusters, strengthening governance, reducing corruption, and improving the efficiency of bureaucratic processes are critical for supporting reindustrialization. Institutional adaptability plays a pivotal role in fostering environments conducive to innovation and long-term economic growth.
Furthermore, small and medium-sized enterprises (SMEs) are essential for industrial diversification and innovation. Policies that provide access to finance, facilitate the adoption of advanced technologies, and foster public–private partnerships are crucial for enabling SMEs to drive industrial growth.
Therefore, it becomes clear that reindustrialization should be aligned with global sustainability goals. Green technologies and circular economy principles should be embedded in industrial policies to ensure that economic growth is compatible with environmental sustainability.
Overall, our results demonstrate that reindustrialization remains a critical pathway for modern economies to maintain competitiveness, foster innovation, and achieve long-term economic resilience and sustainable development. This study demonstrates the importance of considering institutional dynamics, technological change, and societal values when designing industrial policies. Moving forward, countries must adopt tailored and flexible approaches to reindustrialization that harness technological advancements while promoting sustainable development. By fostering institutional adaptability and nurturing innovation, economies can successfully navigate the challenges of the 21st century and build a robust industrial foundation for future growth.

Author Contributions

Conceptualization, V.V. and E.M.; methodology, V.V. and E.M.; software, E.M. and W.S.; validation, E.M. and W.S.; formal analysis, V.V., E.M. and W.S.; data curation, E.M.; writing—original draft preparation, V.V., E.M. and W.S.; writing—review and editing, W.S.; visualization, E.M. and W.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the grant of Russian Science Foundation No. 24-28-01167, https://rscf.ru/en/project/24-28-01167/ (accessed on 29 November 2024) “Reindustrialization and institutional change in the context of Veblenian dichotomy” from the Southern Federal University.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be obtained from the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Economies 12 00331 g001
Figure 1. Word cloud diagram for deindustrialization concept. Source: own results.
Figure 1. Word cloud diagram for deindustrialization concept. Source: own results.
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Figure 2. Reindustrialization indicators by country (2021). Source: WVS Database (https://www.worldvaluessurvey.org/, accessed on 20 October 2024), Wave 7 (2017–2021).
Figure 2. Reindustrialization indicators by country (2021). Source: WVS Database (https://www.worldvaluessurvey.org/, accessed on 20 October 2024), Wave 7 (2017–2021).
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Figure 3. Correlation matrix. Source: own results.
Figure 3. Correlation matrix. Source: own results.
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Figure 4. Results of hierarchical clustering. Source: own results.
Figure 4. Results of hierarchical clustering. Source: own results.
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Table 1. List of variables characterizing attitudes towards science and technology in society.
Table 1. List of variables characterizing attitudes towards science and technology in society.
VariableQuestion from WVS DatabaseAnswers
ComfortableScience 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
OpportunitiesBecause 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 faithWe 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 effectsOne 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
ImportanceIt 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 betterAll 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).
Table 2. Descriptive statistics for variables.
Table 2. Descriptive statistics for variables.
VariableMeanMedianStd. Dev.MinMax
High_tech14.78.3815.60.38164.2
Manuf_GDP14.714.26.381.4327.2
Manuf_growth6.036.186.28−12.218.6
Comfortable7.397.400.6365.908.70
Opportunities7.527.500.5896.309.00
Science faith5.735.900.8723.907.40
Bad effects5.705.700.6944.007.10
Importance4.724.750.8202.906.10
World better6.886.900.7625.508.50
Source: own results.
Table 3. Average values of variables by clusters.
Table 3. Average values of variables by clusters.
ClusterC1C2C3C4
Count751315
High-technology exports (% of manufactured exports)8.5046.358.8012.08
Manufacturing, value added (% of GDP)13.3922.9015.8911.52
Manufacturing, value added (annual % growth)9.548.742.976.15
Comfortable6.477.067.657.70
Opportunities7.096.987.647.81
Science faith6.665.764.896.02
Bad effects6.335.75.055.98
Importance5.494.824.014.95
World better5.916.826.927.31
Source: own results.
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Maslyukova, E.; Volchik, V.; Strielkowski, W. Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach. Economies 2024, 12, 331. https://doi.org/10.3390/economies12120331

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Maslyukova E, Volchik V, Strielkowski W. Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach. Economies. 2024; 12(12):331. https://doi.org/10.3390/economies12120331

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Maslyukova, Elena, Vyacheslav Volchik, and Wadim Strielkowski. 2024. "Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach" Economies 12, no. 12: 331. https://doi.org/10.3390/economies12120331

APA Style

Maslyukova, E., Volchik, V., & Strielkowski, W. (2024). Reindustrialization, Innovative Sustainable Economic Development, and Societal Values: A Cluster Analysis Approach. Economies, 12(12), 331. https://doi.org/10.3390/economies12120331

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