2 The Big Players: Analyzing Traditional Geopolitical Powerhouses through Data

Throughout history, certain nations have wielded disproportionate influence over global politics, economics, and security. These traditional geopolitical powerhouses—the United States, China, Russia, and the European Union—have shaped international relations for decades, if not centuries. Understanding how these states maintain and project their power in the contemporary era requires a combination of historical analysis and modern data science techniques. This chapter explores how the influence of these nations can be quantified and visualized using tools such as economic metrics, military spending data, and diplomatic networks.

Globalization has evolved in phases, driven by technological innovations, the removal of trade barriers, and the diffusion of information and communication technologies (ICT). The first phase of globalization was primarily marked by the industrialization of Northern economies, leading to significant economic divergence between the North and South. Northern countries leveraged innovation, economies of scale, and technological progress to enhance their productivity, while Southern countries experienced stagnation. This phase entrenched economic disparities between developed and developing countries (Baldwin et al., 2001).

The second phase of globalization emerged with the rise of ICT, which facilitated the outsourcing and relocation of manufacturing and service sectors to the Global South. Emerging economies such as China, India, and Brazil became manufacturing hubs due to their access to cheap labor and the reallocation of industrial capacities from the North. Technology transfers, the reduction of trade barriers, and the increase in cross-border flows of goods and capital significantly reshaped global production and consumption patterns. These shifts are evidenced in the rising share of GDP attributed to international trade in these regions, as depicted in World Bank data on trade as a percentage of GDP (OECD, 2006).

Data from the World Bank illustrates the increasing importance of trade in services, particularly among advanced economies and emerging markets. The integration of global value chains has also transformed production networks, with multinational corporations driving intra-firm trade across borders. The relocation of production processes, facilitated by developments in transportation and telecommunication technologies, has allowed emerging economies to industrialize and reduce their productivity gap with developed nations. For instance, the rise of containerization in sea transport and declining air transport costs have revolutionized the logistics industry, making it cheaper and faster to move goods globally (Bordo et al., 1999).

The dominance of BRICS countries in global trade is further reflected in their increasing share of world GDP. Countries like China, India, Brazil, and Mexico have witnessed sustained economic growth, contributing to the rebalancing of the global economy. These countries now account for over 40% of the world’s population and hold a growing share of global economic output, with their GDP consistently outpacing that of advanced economies. This is especially evident in data tracking GDP growth rates of high-income versus low and middle-income countries (Ghemawat, 2016).

Global financial flows have also expanded, with the role of emerging economies becoming increasingly prominent in capital markets. Before World War I, global financial integration was limited, with foreign capital flows primarily directed toward railways and governments. Today, capital flows are broader and involve more sectors, with emerging markets playing a key role in international capital investments. However, these flows are subject to macroeconomic risks, as emerging economies remain vulnerable to fluctuations in global markets, as seen during financial crises (Baldwin, 2012).

As the global economy continues to evolve, the need for global governance and cooperation becomes more apparent. Institutions such as the World Trade Organization (WTO), International Monetary Fund (IMF), and World Bank have played pivotal roles in facilitating the integration of global markets and addressing trade-related challenges. Bilateral and multilateral trade agreements have reduced tariffs and trade barriers, fostering a more interconnected global economy. However, the sustainability of this interdependence is being tested by rising protectionism and regionalization, which challenge the future trajectory of globalization (OECD, 2006).

Technological advancements, particularly the Internet and digital platforms, have further accelerated globalization by facilitating real-time communication, enabling cross-border trade in services, and transforming global financial systems. The spread of digital technologies has also led to the globalization of information flows, with data now being a crucial element of global trade. Emerging markets, particularly in Asia, have capitalized on these technologies to enhance their competitiveness in the global market, driving new waves of innovation and economic growth (World Economic Outlook, 2019).

The post-COVID-19 landscape presents new challenges to globalization. The pandemic has exposed vulnerabilities in global supply chains, prompting a reevaluation of international business models and risk management strategies. The shift towards digitalization and the rise of artificial intelligence (AI) are reshaping global economic dynamics, requiring firms and governments to adapt to new forms of competition and innovation. At the same time, the crisis has accelerated trends towards de-globalization, as countries turn inward to protect their economies and manage the health and economic impacts of the pandemic (Berjeijk, 2019).

These shifts highlight the complexity of measuring globalization and its impacts. Indicators such as the KOF Globalization Index track the economic, social, and political dimensions of globalization, revealing variations in how countries engage with global markets. Advanced economies tend to be more globalized than developing countries, although the latter are rapidly integrating into the global economy. Emerging markets, particularly in Asia, have become key drivers of global growth, leading to a multipolar global economy where regional powers like China and India play increasingly dominant roles (Ghemawat, 2016).

2.1 Economic Metrics and Global Power

Economics has long been central to geopolitical dominance. Nations that control vast economic resources and wield financial power have historically been able to exert significant influence on global affairs. Gross Domestic Product (GDP), trade balances, and foreign direct investment (FDI) are common metrics used to measure economic power. For example, the United States has maintained its status as a global economic leader due to its sizable economy, technological innovation, and influence in international financial institutions like the International Monetary Fund (IMF) and World Bank (Helleiner, 2014).

China’s rise to prominence, particularly after its entry into the World Trade Organization (WTO) in 2001, showcases how rapid economic growth can reshape global power structures. China has used its vast manufacturing base, investment in infrastructure, and the Belt and Road Initiative (BRI) to expand its influence in Africa, Asia, and beyond (Cai, 2017). Data science can help quantify China’s expanding global footprint by analyzing trade flows, foreign investments, and the increasing number of diplomatic agreements signed under the BRI (Huang, 2016).

The European Union, despite being a complex and multifaceted entity, remains one of the largest economic blocks in the world. Its power is derived from its unified market and regulatory influence. Through data analysis of intra-European trade, common regulatory frameworks, and the EU’s role in global environmental and trade negotiations, we can see how its economic metrics contribute to its standing in the global hierarchy (Jones, 2019).

2.2 Military Spending and Geopolitical Influence

Military spending is another key metric used to measure the geopolitical power of a nation. The United States has long maintained the world’s largest military budget, spending hundreds of billions of dollars annually. Data science techniques, such as time-series analysis, can track trends in military spending, revealing how countries like China and Russia are rapidly increasing their military investments in response to regional security concerns and international ambitions (Kalkman, 2020).

Geospatial analysis is particularly useful in military studies. Researchers can use data on military bases, troop deployments, and defense agreements to map the global reach of traditional powerhouses. For example, the United States has an extensive network of military bases spanning several continents, giving it a unique ability to project power far beyond its borders (Cooley & Nexon, 2013). Similarly, Russia’s military interventions in Ukraine, Syria, and its naval presence in the Arctic highlight its strategic use of military resources to assert its influence (Giles, 2019).

Data science tools like network analysis can visualize these military connections and alliances, revealing the intricate web of relationships that underpins global security. NATO, for instance, serves as a critical node in the global security architecture, connecting Western powers through formal defense agreements and military coordination.

2.3 Diplomatic Relations and Network Analysis

Diplomatic influence is another critical dimension of geopolitical power. Nations exert influence not only through force or economic might but also through diplomacy. The United Nations, World Trade Organization, and numerous regional organizations provide forums for states to exercise diplomatic power. The United States and the European Union have historically been dominant players in these institutions, shaping global norms and multilateral agreements (Ikenberry, 2011).

Network analysis offers a powerful tool for visualizing the complex web of diplomatic relationships that define global politics. By examining voting patterns in the United Nations General Assembly, for instance, data scientists can identify blocs of countries that tend to align on key issues, such as trade, security, or human rights (Voeten et al., 2009). These networks reveal how traditional powerhouses maintain their influence by forming strategic alliances and coalitions.

The rise of China as a diplomatic force is particularly noteworthy. China has used its growing economic and military power to build new alliances in the Global South, particularly through investments in Africa and Latin America (Alden, 2007). Data science can help track how China’s diplomatic outreach and investments have shifted global alliances, showing how Beijing has successfully reshaped regional power dynamics (Shambaugh, 2013).

2.4 Quantifying Global Influence with Data Science

Using data science methodologies, geopolitical influence can be more precisely quantified and analyzed. Network theory allows researchers to understand the strength and nature of diplomatic, economic, and military ties between nations. Predictive analytics, meanwhile, can model how changes in one domain—such as a significant increase in military spending or a shift in trade policy—might ripple through the global system, altering geopolitical alliances and triggering new power dynamics.

For instance, machine learning algorithms can be employed to predict the impact of geopolitical events such as sanctions, trade wars, or military conflicts. Researchers have developed models that analyze vast datasets of historical conflicts, military expenditures, and diplomatic negotiations to forecast potential flashpoints for future conflict (Cederman & Gleditsch, 2009). This predictive capacity gives policymakers valuable insights into the potential outcomes of their decisions and helps nations navigate the complexities of global power.

Data science also provides insights into soft power—the ability of nations to influence others through cultural appeal and diplomatic persuasion rather than coercion or payment. For example, using social media data, researchers can analyze how narratives of global powers are framed internationally and how public opinion is shaped through state-sponsored media and cultural diplomacy efforts (Nye, 2004). This provides a more nuanced understanding of how traditional powerhouses maintain their global influence in an era of rapid information dissemination.

2.5 Conclusion

The traditional geopolitical powerhouses of the world continue to exert influence through a combination of economic strength, military might, and diplomatic networks. However, the rise of data science provides new ways of quantifying and understanding this power. By leveraging techniques such as network analysis, predictive modeling, and geospatial data analysis, this chapter has explored how traditional powers maintain their influence and how their global reach can be visualized and understood through data. This approach not only enhances our understanding of geopolitical dynamics but also equips policymakers with tools to anticipate shifts in global power.

2.6 References

Alden, C. (2007). China in Africa. Zed Books.
Baldwin, R. E. (2012). Global Supply Chains: Why They Emerged, Why They Matter, and Where They Are Going. In National Bureau of Economic Research Working Paper Series (No. w17716). National Bureau of Economic Research. https://doi.org/10.3386/w17716
Baldwin, R. E., Martin, P., & Ottaviano, G. I. (2001). Global Income Divergence, Trade, and Industrialization: The Geography of Growth Take-Offs. Journal of Economic Growth, 6(1), 5–37. https://doi.org/10.1023/A:1009820207410
Berjeijk, P. A. (2019). De-globalization 2.0. CESifo Working Paper Series No. 7752. https://doi.org/10.2139/ssrn.3480134
Bordo, M. D., Eichengreen, B., & Irwin, D. A. (1999). Is Globalization Today Really Different than Globalization a Hundred Years Ago?. NBER Working Paper No. 7195. National Bureau of Economic Research. https://doi.org/10.3386/w7195
Cai, P. (2017). Understanding China’s Belt and Road Initiative. Lowy Institute for International Policy.
Cederman, L.-E., & Gleditsch, K. S. (2009). Introduction to special issue on “Disaggregating civil war.” Journal of Conflict Resolution, 53(4), 487–495.
Cooley, A., & Nexon, D. (2013). The Empire Will Compensate You: The Structural Dynamics of the U.S. Overseas Basing Network. Perspectives on Politics, 11(4), 1034–1050.
Fischer, S. (2003). Globalization and Its Challenges. American Economic Review, 93(2), 1–30. https://doi.org/10.1257/000282803321946750
Ghemawat, P. (2016). The Laws of Globalization and Business Applications. Cambridge University Press. https://doi.org/10.1017/CBO9781316558522
Ghemawat, P. & Altman, S. A. (2016). DHL Global Connectedness Index: Analyzing Global Flows and their Power. DHL Express Global Study.
Giles, K. (2019). Moscow Rules: What Drives Russia to Confront the West. Brookings Institution Press.
Helleiner, E. (2014). The Status Quo Crisis: Global Financial Governance After the 2008 Meltdown. Oxford University Press.
Huang, Y. (2016). Understanding China’s Belt & Road Initiative: Motivation, framework and assessment. China Economic Review, 40, 314–321.
Iansiti, M., & Levien, R. (2004). The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Harvard Business Press.
Ikenberry, G. J. (2011). Liberal Leviathan: The Origins, Crisis, and Transformation of the American World Order. Princeton University Press.
IMF (2020). World Economic Outlook: Global Growth and Inflation Rates. https://www.imf.org/en/Publications/WEO
Jones, E. (2019). The Economic Crisis and the European Integration. Oxford University Press.
Kalkman, J. (2020). Military Spending and Democracy: A Review. Defense and Peace Economics, 31(4), 389–408.
KOF Swiss Economic Institute (2019). Globalization Index Data. https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html
Nye, J. S. (2004). Soft Power: The Means to Success in World Politics. PublicAffairs.
OECD (2006). The Rise of China and India: What’s in it for Africa?. OECD Development Centre Studies. OECD Publishing. https://doi.org/10.1787/9789264024424-en
Shambaugh, D. (2013). China Goes Global: The Partial Power. Oxford University Press.
United Nations Conference on Trade and Development (2020). World Investment Report 2020: International Production Beyond the Pandemic. https://unctad.org/webflyer/world-investment-report-2020
Voeten, E., Strezhnev, A., & Bailey, M. (2009). Voting in the UN General Assembly. Harvard Dataverse.
World Bank (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
World Economic Forum (2019). The Global Competitiveness Report 2019. http://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf
World Trade Organization (2020). World Trade Statistical Review. https://www.wto.org/english/res_e/statis_e/wts2020_e/wts2020_e.pdf

# The Big Players: Analyzing Traditional Geopolitical Powerhouses through Data Throughout history, certain nations have wielded disproportionate influence over global politics, economics, and security. These **traditional geopolitical powerhouses**—the United States, China, Russia, and the European Union—have shaped international relations for decades, if not centuries. Understanding how these states maintain and project their power in the contemporary era requires a combination of historical analysis and modern data science techniques. This chapter explores how the influence of these nations can be quantified and visualized using tools such as **economic metrics**, **military spending** data, and **diplomatic networks**. Globalization has evolved in phases, driven by technological innovations, the removal of trade barriers, and the diffusion of information and communication technologies (ICT). The first phase of globalization was primarily marked by the industrialization of Northern economies, leading to significant economic divergence between the North and South. Northern countries leveraged innovation, economies of scale, and technological progress to enhance their productivity, while Southern countries experienced stagnation. This phase entrenched economic disparities between developed and developing countries (Baldwin et al., 2001). The second phase of globalization emerged with the rise of ICT, which facilitated the outsourcing and relocation of manufacturing and service sectors to the Global South. Emerging economies such as China, India, and Brazil became manufacturing hubs due to their access to cheap labor and the reallocation of industrial capacities from the North. Technology transfers, the reduction of trade barriers, and the increase in cross-border flows of goods and capital significantly reshaped global production and consumption patterns. These shifts are evidenced in the rising share of GDP attributed to international trade in these regions, as depicted in World Bank data on trade as a percentage of GDP (OECD, 2006). Data from the World Bank illustrates the increasing importance of trade in services, particularly among advanced economies and emerging markets. The integration of global value chains has also transformed production networks, with multinational corporations driving intra-firm trade across borders. The relocation of production processes, facilitated by developments in transportation and telecommunication technologies, has allowed emerging economies to industrialize and reduce their productivity gap with developed nations. For instance, the rise of containerization in sea transport and declining air transport costs have revolutionized the logistics industry, making it cheaper and faster to move goods globally (Bordo et al., 1999). The dominance of BRICS countries in global trade is further reflected in their increasing share of world GDP. Countries like China, India, Brazil, and Mexico have witnessed sustained economic growth, contributing to the rebalancing of the global economy. These countries now account for over 40% of the world’s population and hold a growing share of global economic output, with their GDP consistently outpacing that of advanced economies. This is especially evident in data tracking GDP growth rates of high-income versus low and middle-income countries (Ghemawat, 2016). Global financial flows have also expanded, with the role of emerging economies becoming increasingly prominent in capital markets. Before World War I, global financial integration was limited, with foreign capital flows primarily directed toward railways and governments. Today, capital flows are broader and involve more sectors, with emerging markets playing a key role in international capital investments. However, these flows are subject to macroeconomic risks, as emerging economies remain vulnerable to fluctuations in global markets, as seen during financial crises (Baldwin, 2012). As the global economy continues to evolve, the need for global governance and cooperation becomes more apparent. Institutions such as the World Trade Organization (WTO), International Monetary Fund (IMF), and World Bank have played pivotal roles in facilitating the integration of global markets and addressing trade-related challenges. Bilateral and multilateral trade agreements have reduced tariffs and trade barriers, fostering a more interconnected global economy. However, the sustainability of this interdependence is being tested by rising protectionism and regionalization, which challenge the future trajectory of globalization (OECD, 2006). Technological advancements, particularly the Internet and digital platforms, have further accelerated globalization by facilitating real-time communication, enabling cross-border trade in services, and transforming global financial systems. The spread of digital technologies has also led to the globalization of information flows, with data now being a crucial element of global trade. Emerging markets, particularly in Asia, have capitalized on these technologies to enhance their competitiveness in the global market, driving new waves of innovation and economic growth (World Economic Outlook, 2019). The post-COVID-19 landscape presents new challenges to globalization. The pandemic has exposed vulnerabilities in global supply chains, prompting a reevaluation of international business models and risk management strategies. The shift towards digitalization and the rise of artificial intelligence (AI) are reshaping global economic dynamics, requiring firms and governments to adapt to new forms of competition and innovation. At the same time, the crisis has accelerated trends towards de-globalization, as countries turn inward to protect their economies and manage the health and economic impacts of the pandemic (Berjeijk, 2019). These shifts highlight the complexity of measuring globalization and its impacts. Indicators such as the KOF Globalization Index track the economic, social, and political dimensions of globalization, revealing variations in how countries engage with global markets. Advanced economies tend to be more globalized than developing countries, although the latter are rapidly integrating into the global economy. Emerging markets, particularly in Asia, have become key drivers of global growth, leading to a multipolar global economy where regional powers like China and India play increasingly dominant roles (Ghemawat, 2016). ## Economic Metrics and Global Power Economics has long been central to geopolitical dominance. Nations that control vast economic resources and wield financial power have historically been able to exert significant influence on global affairs. **Gross Domestic Product (GDP)**, **trade balances**, and **foreign direct investment (FDI)** are common metrics used to measure economic power. For example, the United States has maintained its status as a global economic leader due to its sizable economy, technological innovation, and influence in international financial institutions like the International Monetary Fund (IMF) and World Bank (Helleiner, 2014). China’s rise to prominence, particularly after its entry into the World Trade Organization (WTO) in 2001, showcases how rapid economic growth can reshape global power structures. China has used its vast manufacturing base, investment in infrastructure, and the **Belt and Road Initiative (BRI)** to expand its influence in Africa, Asia, and beyond (Cai, 2017). Data science can help quantify China’s expanding global footprint by analyzing trade flows, foreign investments, and the increasing number of diplomatic agreements signed under the BRI (Huang, 2016). The European Union, despite being a complex and multifaceted entity, remains one of the largest economic blocks in the world. Its power is derived from its unified market and regulatory influence. Through data analysis of intra-European trade, common regulatory frameworks, and the EU's role in global environmental and trade negotiations, we can see how its economic metrics contribute to its standing in the global hierarchy (Jones, 2019). ## Military Spending and Geopolitical Influence Military spending is another key metric used to measure the geopolitical power of a nation. The United States has long maintained the world's largest military budget, spending hundreds of billions of dollars annually. **Data science techniques**, such as time-series analysis, can track trends in military spending, revealing how countries like China and Russia are rapidly increasing their military investments in response to regional security concerns and international ambitions (Kalkman, 2020). **Geospatial analysis** is particularly useful in military studies. Researchers can use data on **military bases**, **troop deployments**, and **defense agreements** to map the global reach of traditional powerhouses. For example, the United States has an extensive network of military bases spanning several continents, giving it a unique ability to project power far beyond its borders (Cooley & Nexon, 2013). Similarly, Russia’s military interventions in **Ukraine**, **Syria**, and its naval presence in the **Arctic** highlight its strategic use of military resources to assert its influence (Giles, 2019). Data science tools like **network analysis** can visualize these military connections and alliances, revealing the intricate web of relationships that underpins global security. NATO, for instance, serves as a critical node in the global security architecture, connecting Western powers through formal defense agreements and military coordination. ## Diplomatic Relations and Network Analysis Diplomatic influence is another critical dimension of geopolitical power. Nations exert influence not only through force or economic might but also through diplomacy. The **United Nations**, **World Trade Organization**, and numerous regional organizations provide forums for states to exercise diplomatic power. The United States and the European Union have historically been dominant players in these institutions, shaping global norms and multilateral agreements (Ikenberry, 2011). **Network analysis** offers a powerful tool for visualizing the complex web of diplomatic relationships that define global politics. By examining voting patterns in the United Nations General Assembly, for instance, data scientists can identify blocs of countries that tend to align on key issues, such as trade, security, or human rights (Voeten et al., 2009). These networks reveal how traditional powerhouses maintain their influence by forming strategic alliances and coalitions. The rise of **China** as a diplomatic force is particularly noteworthy. China has used its growing economic and military power to build new alliances in the **Global South**, particularly through investments in Africa and Latin America (Alden, 2007). Data science can help track how China’s **diplomatic outreach** and investments have shifted global alliances, showing how Beijing has successfully reshaped regional power dynamics (Shambaugh, 2013). ## Quantifying Global Influence with Data Science Using **data science methodologies**, geopolitical influence can be more precisely quantified and analyzed. **Network theory** allows researchers to understand the strength and nature of diplomatic, economic, and military ties between nations. **Predictive analytics**, meanwhile, can model how changes in one domain—such as a significant increase in military spending or a shift in trade policy—might ripple through the global system, altering geopolitical alliances and triggering new power dynamics. For instance, **machine learning algorithms** can be employed to predict the impact of geopolitical events such as sanctions, trade wars, or military conflicts. Researchers have developed models that analyze vast datasets of historical conflicts, military expenditures, and diplomatic negotiations to forecast potential flashpoints for future conflict (Cederman & Gleditsch, 2009). This predictive capacity gives policymakers valuable insights into the potential outcomes of their decisions and helps nations navigate the complexities of global power. Data science also provides insights into **soft power**—the ability of nations to influence others through cultural appeal and diplomatic persuasion rather than coercion or payment. For example, using social media data, researchers can analyze how narratives of global powers are framed internationally and how public opinion is shaped through state-sponsored media and cultural diplomacy efforts (Nye, 2004). This provides a more nuanced understanding of how traditional powerhouses maintain their global influence in an era of rapid information dissemination. ## Conclusion The **traditional geopolitical powerhouses** of the world continue to exert influence through a combination of economic strength, military might, and diplomatic networks. However, the rise of data science provides new ways of quantifying and understanding this power. By leveraging techniques such as **network analysis**, **predictive modeling**, and **geospatial data analysis**, this chapter has explored how traditional powers maintain their influence and how their global reach can be visualized and understood through data. This approach not only enhances our understanding of geopolitical dynamics but also equips policymakers with tools to anticipate shifts in global power. ## References - Alden, C. (2007). *China in Africa*. Zed Books. - Baldwin, R. E. (2012). **Global Supply Chains: Why They Emerged, Why They Matter, and Where They Are Going**. In *National Bureau of Economic Research Working Paper Series* (No. w17716). National Bureau of Economic Research. https://doi.org/10.3386/w17716 - Baldwin, R. E., Martin, P., & Ottaviano, G. I. (2001). **Global Income Divergence, Trade, and Industrialization: The Geography of Growth Take-Offs**. *Journal of Economic Growth*, 6(1), 5–37. https://doi.org/10.1023/A:1009820207410 - Berjeijk, P. A. (2019). **De-globalization 2.0**. *CESifo Working Paper Series No. 7752*. https://doi.org/10.2139/ssrn.3480134 - Bordo, M. D., Eichengreen, B., & Irwin, D. A. (1999). **Is Globalization Today Really Different than Globalization a Hundred Years Ago?**. *NBER Working Paper No. 7195*. National Bureau of Economic Research. https://doi.org/10.3386/w7195 - Cai, P. (2017). Understanding China’s Belt and Road Initiative. *Lowy Institute for International Policy*. - Cederman, L.-E., & Gleditsch, K. S. (2009). Introduction to special issue on “Disaggregating civil war.” *Journal of Conflict Resolution, 53*(4), 487–495. - Cooley, A., & Nexon, D. (2013). The Empire Will Compensate You: The Structural Dynamics of the U.S. Overseas Basing Network. *Perspectives on Politics, 11*(4), 1034–1050. - Fischer, S. (2003). **Globalization and Its Challenges**. *American Economic Review*, 93(2), 1–30. https://doi.org/10.1257/000282803321946750 - Ghemawat, P. (2016). **The Laws of Globalization and Business Applications**. *Cambridge University Press*. https://doi.org/10.1017/CBO9781316558522 - Ghemawat, P. & Altman, S. A. (2016). **DHL Global Connectedness Index: Analyzing Global Flows and their Power**. DHL Express Global Study. - Giles, K. (2019). *Moscow Rules: What Drives Russia to Confront the West*. Brookings Institution Press. - Helleiner, E. (2014). *The Status Quo Crisis: Global Financial Governance After the 2008 Meltdown*. Oxford University Press. - Huang, Y. (2016). Understanding China's Belt & Road Initiative: Motivation, framework and assessment. *China Economic Review, 40*, 314–321. - Iansiti, M., & Levien, R. (2004). **The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability**. Harvard Business Press. - Ikenberry, G. J. (2011). *Liberal Leviathan: The Origins, Crisis, and Transformation of the American World Order*. Princeton University Press. - IMF (2020). **World Economic Outlook: Global Growth and Inflation Rates**. https://www.imf.org/en/Publications/WEO - Jones, E. (2019). *The Economic Crisis and the European Integration*. Oxford University Press. - Kalkman, J. (2020). Military Spending and Democracy: A Review. *Defense and Peace Economics, 31*(4), 389–408. - KOF Swiss Economic Institute (2019). **Globalization Index Data**. https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html - Nye, J. S. (2004). *Soft Power: The Means to Success in World Politics*. PublicAffairs. - OECD (2006). **The Rise of China and India: What's in it for Africa?**. *OECD Development Centre Studies*. OECD Publishing. https://doi.org/10.1787/9789264024424-en - Shambaugh, D. (2013). *China Goes Global: The Partial Power*. Oxford University Press. - United Nations Conference on Trade and Development (2020). **World Investment Report 2020: International Production Beyond the Pandemic**. https://unctad.org/webflyer/world-investment-report-2020 - Voeten, E., Strezhnev, A., & Bailey, M. (2009). Voting in the UN General Assembly. *Harvard Dataverse*. - World Bank (2020). **World Development Indicators**. https://databank.worldbank.org/source/world-development-indicators - World Economic Forum (2019). **The Global Competitiveness Report 2019**. http://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf - World Trade Organization (2020). **World Trade Statistical Review**. https://www.wto.org/english/res_e/statis_e/wts2020_e/wts2020_e.pdf