Thesis Gravity and Social Network Analysis

Introduction The study of international trade mechanisms represents one of the main points of economic research, providing us with understanding on the ways how the global trade works. Previously, the search to have understanding about this phenomenon results to the discovery of several theoretical frameworks such as the Wealth of Nations by Adam Smith to the development of the Gravity Model and Social Network Analysis today. The changing nature of world trade, whereby digital economies are entering the scene, geopolitical interactions are in revival, and sustainability and ethic issues become the main drivers of change, keep challenging the world trade frameworks to maintain their relevance and applicability. The Turkic States emerge specially intriguing in the context of the case study. This developing region is cradled with the intersecting ties of common linguistics, culture, and history, and once upon a time, it was crisscrossed with the long-lived trade routes, mainly the Silk Road. In spite of the prosperous common heritage, the economic and political trajectories of the states in the region have gone different ways in the post-Soviet period, which gives an additional chance to study what lies behind the inter-lateral trade within this area. Among the literature being devoted to the bilateral trade dynamics in the Turkic States, is the mentioned literature insufficient, or else there is room for more study to fill in lacking scholarly exploration of regional trade patterns. Research Objectives The primary purpose of this research will be filling the gap by analyzing and finding bilateral trade determinants among the Turkic States. Specifically, the study aims to: Evaluate the adaptability of the Gravity Model and Social Network Analysis as the foundations for the trade flows among the Turkic States, given that both traditional economic factors and the influence of history, culture, and politics are taken into account . State the specific aspects in this regional bloc that strongly influence trade flows which could include but not be limited to geographical proximity, economic size, there policies and the existence of shared linguistic and cultural heritage. As the regional and international trade agreements becomes more and more influential for the Turkic trade relations, the role of initiatives for the cooperation such as the Turkic Council in the facilitation of trade, require to be evaluated with a special focus. Bundle-up predicted zones for trade expansion within the area, build upon the basis of past trade data analysis and the factors of trade identified. These goals of the research are intended to lay a foundation for a complete analysis of the factors that influence economic integration and trade relations within the Turkic States, therefore, one can better comprehend the level of integration within this region. Significance of the Study This study leads to the establishment of very profound impacts which run on across multiple fields, which in turn, hold significant contribution and views especially in academic circles, policy formulation and development economic strategies to the understanding of culturalpolitical dynamics. Attempts to advance a relatively unexplored topic of the academic world, which deals with the details of the functioning of trade relationships among the Turkic States, are made. Converging this subject with the lack of existing studies clearly denotes the most significant gap in the scientific literature, which this research intends on overcoming. Thus, through utilizing the analytical lenses of the Gravity Model and Social Network Analysis, the study uncovers the specific attributes of intra-regional trade in the present bloc as well as makes a valuable contribution to the ongoing academic debate on the role of those methods in revealing trade intraregionalism's nature. The accomplishment of this double task ensures not only the filling of the gap in an under-researched topic, but it also gives because the new information could be a guide for future inquiries into similar unexplored regions. This finding has an impact on trade policy and provides this area with a conceptual framework that when combined with the strategies used by policymakers both from the Turkic States and the neighboring countries, allow refining the strategies of the people involved in it. The unveiling of major drivers of trade in the study lays the solid-ground base for policymakers to initiate projects in their regions through strategic crafting of policies to boost cooperation and integration. Such policy enlightenment has the potential to yield off reciprocal developments and enhanced economic resilience within the region at large by striking a win-win formula that fully exploits trade as an engine for overall development. This research designs itself as such a compass for the Turkic States, set to embrace farreaching economic chances and pitfalls. It provides businesses and investors with investment insights into the components of dynamics that shapes trade relations between partnering countries and developers sector ripe for investment and growth. Besides, focusing on intraregional connectivity, the research gives a clear pathway on how to utilize the economic opportunity hidden in the rise in intra-regional trade, which is among the factors that are developing the world economy. Beyond its tangible monetary benefit, the report also delivers the more delicate, but equally critical, categories of culture as well as politics. Via analyzing the role that the common historical and cultural traits play in trade relations, the study highlights the fact that even non-economic elements have an impact on the trade outcomes. It goes beyond the usual trade dynamics description. In addition, cultural and political contexts are equally assumed to be essential elements of analyzing economic affairs. Therefore, the research has made a contribution for a more comprehensive view about trade, one that increases our knowledge about the range of social, cultural, and political dimensions which shape international trade. Literature Review Overview of International Trade Theories Inclusive economic growth undoubtedly requires the comprehensive model of economy which mainly focuses on the achievement of the Sustainable Development Goals (SDGs) (United Nations, 2015). Specifically, Adam Smith, allegedly father of modern economics, provides that there are certainly forces governing the relations in economy (Myint, 1977). Thus, Smith’s one of the main approaches to the economics is related to the foreign or international trade. To put it differently, “Trade can be good for the environment because it creates wealth that can be used for environmental improvement, and the efficiency gains from trade can mean fewer resources used and less waste produced” (Ruppel, 2018, p. 771). While a variety of theories centering on international trade have been continuously developed, the main purpose of these theories is to give the specialists some instrument helping them predict and explain international trade flows. The three theories that played a significant role in the evolution of our understanding of the trade dynamics are: the topic of comparative advantage theory, the new trade theory, and Porter’s diamond model. While each theory, in its turn, either creates new lines of thinking within the forces behind international trade, or, on the other hand, reflects some inconsistencies in terms of their applicability to reality, some of these presumptions can be used as robust analytical tools. Comparative Advantage Theory Comparative advantage theory is projected by David Ricardo at the beginning of the 19th century and is considered one of the fundamentals of international trading. Ricardo proposed the model where a country could even be in a more disadvantageous situation in respect to its overall efficiency at producing goods in comparison with competitors, but it will still give an advantage to specialize on and exporting the goods, in which it can produce most efficiently (that is, at the lowest opportunity cost), and importing the goods, in which it is not very good at (Ricardo, 2001; Ricardo, 1817). An illustration of comparative advantage asserts that countries pursue their export portfolio due to the fact that there is a heterogeneity in labor productivity and opportunity costs in the ratio of the two countries towards producing goods. Burgstaller (1986) argues that this theory lays the foundation of the reasoning as to why free trade exists and which holds the notion that trade is a positive-sum game that can gather a win-win result, where parties will profit when specialize into what they have competitive advantages about. Nevertheless, there are some drawbacks, but this theory is still useful. It appears in the form of an exhibiting two countries and two products: this echo be the really complicated nature of today's international trade. In the same vein, it overlooks such issues including technology, capital, economies of scale and transport costs. Also, it does not address the alternative sideeffects of trade on income distribution, ownership of assets etc. which can have serious economic and social problems (Costinot and Donaldson, 2012). N ew Trade Theory One of the new trade theories, namely the New Trade Theory, was pioneered by economists like Paul Krugman in the 20th century and emphasizes economies of scale and network effects in explaining observed trade patterns. The theory differs from traditional models in that it does not just attribute constant returns to scale but rather postulates that increasing returns to scale and network effects connecting similar countries produce similar goods which causes trade (Krugman, 1979). New trade theory is the reason that even though a country has the same standards of economy and is with the identical capability, it can still conduct largescale trade with others. In it, the writer of the study takes into account the intra-industry trade phenomenon when countries can both import and export similar kinds of goods but different varieties. Bhattacharjea (2008) emphasizes that a core idea of the theory is the fact of how marginal costs are decreasing while average costs are reducing due to production on the scale that dominates the globe. One of disadvantages that new trade theory has is using imperfect competition, this includes monopolistic competition. Another theory that it espouses is the insufficient reflection of the role of comparative advantage and other old factors, like factor endowment. On the one hand, the theory targets to the industries with really high economies of scale. Considering that, the theory does not include some sectors, e.g. the service sector or the newly developed products (Bhattacharjea, 2008). Porter's Diamond Model Michael Porter's Diamond Model (1990) fundamentally stood out as it offered an all encompassing analysis of a nation's competitiveness. This model is not only emphasizing the domestic determinants of national advantage but also very much for adjusting the thinking relating to international trade pattern (Porter, 1990). Porter identifies four interrelated determinants that collectively contribute to the competitive strength of industries within a country: Factor conditions, the domestic demand conditions, supporting and related industries, and the firm locations, strategies, etc. (Dunning, 1993). Factor determinant lies behind the increase country's wealth. It can be a natural endowment like natural resources and unskilled labor, or can be high-tech one such as skilled labor, infrastructure, and technological capability. However innovative factors, which were basic to advanced factors and required large institutional investment, notably well-prepared workers and government creation of needed facilities, were ultimately the most important competitive feature. The moral and countrywide scale of the home market demand are the most important points which determine properties of the number of internally manufactured products. The complex Northern Europe customers with an elevated level of expectations that the companies compete with each other on the global markets. It brings along the improvement of their competitiveness and standing level. Further, the professor highlights the role of quality suppliers of innovative industries and supporting industries as an enhancing factor to the production (Dunning, 1993). Improved worldwide performance and constant creation of new ideas by different suppliers strengthen the internationalization and innovation of downstream industries via knowledge diffusion. The specific method of how these firms come into being, the way they are structured and managed as well, and the resulting competition at home are all the conditions needed for successful development of competitive industries. Cut-throat domestic competition triggers constant search for competitive advantages, providing companies with an opportunity to keep their rivals behind, due to their own innovations, efficiency improvements, and customers-oriented orientations. Although the model has been highly praised for its clarity and totality in explaining the nature of the competitive powers of nations, Porter's Diamond Model has been subjected to several criticisms that underscore its shortcomings regarding the dynamics of global competitiveness. One of the major disadvantages of Porter's Diamond model is that it entails determinism. Critics such as Grant (1991), Asmussen et. Al. (2009) and others bother that the model implies an absolute directionality, through which only the four determinants—factor conditions, demand conditions, related and supporting industries, and firm strategy and rivalry—rules the nation's competitive advantage. By emphasizing these two factors only, one may unintentionally undermine the importance of the many other equally important aspects that make global trade such a complicated and multifaceted nature, such as international market dynamics, global trade policies, and cultural tangles. This somewhat deterministic approach may not be able to express the multivariate and interconnected nature of international business where there are no straights entering the pictures while analyzing the reasons leading to the success of the nation. The model, besides explaining why industry within particular countries might be successful, provides rather deceitful predictions, as for example what industries are going to be successful in the future. Hence, water resources scarcity can prevent the making of clear-cut policies for public officials and corporate leaders who are contributing to strategic planning of their respective sectors. Without the predictive capacity, the model provides a minimal contribution to the decision-making process, which in the prospective planning perspective, is thinking of a hedge on the national strengths. It can be a point of criticism for the model that it provides a static picture of the competitive scene. It fails to address many area like the continuous change of the global economy, for example innovation or geopolitical, and many more. The high turbulent environment means that processes, industries, and performance in the specific company can drastically change during some short period without any reliable forecast. A model, which omits an element of fluidity, may not have complete instrument that key stakeholders must deal with dynamic markets issues efficiently (Asmussen et. Al., 2009). Factors Influencing Trade Development Economic Factors Economic development signifies a rise in country's volume of production of goods and services which is growth tracked by Gross Domestic Product. Economic growth really speeds up trade development since it brings more market opportunities with new traders and consumer demand for local goods (Kuznets, 1955). A higher GDP growth rate is usually related to the expanded export and import volumes mainly as the economies that show dynamism usually possess the purchasing ability and demand for the goods that are produced abroad. According to several research economic growth is given a large weight by the level of trading development. For example, Frankel and Romer (1999) observed that nations with higher growths in their gross domestic product (GDP) usually experience escalations in their level of engagement in global trade and increased integration into the international economy. Further, according to Imbs and Wacziarg (2003), there was a positive relationship between trade and economic growth. These authors also showed the key factor which determines the trade for a constant growth. In conjunction with it, it is no doubt that modern trade relations are interrelated with trade agreements. Trade agreements are the joint acts of countries that aim to lower artificial trade obstacles and promote economic relations (Rodrik, 2018). The agreements can be realized in different forms that include free trade agreements (FTAs), customs unions, and economic partnerships. from an international perspective, foreign policies and diplomatic relations between countries play a crucial role in shaping the world. Trade treaties are, undoubtedly, the cornerstone of the trade development picture as they are supposed to eliminate trade barriers (tariffs), and the regulation agreements. Hence, trade laws and regulations may create new trade agreements and directions that provide for enhanced trade volumes among countries. In their study, Baier and Bergstrand (2007) found that the trade flows between countries were positively correlated with how large the trade agreements were while also Freund and Ornelas’ research (2010) showed that regional integration in trade was strongly linked to the intensity of trading within the region. One of the most obvious consequences of NAFTA is important growth in trade volumes between member states. This was mainly due to the elimination of tariffs and facilitating trans-border investments. In order to have a solid fundament in the development of trade relations, it is imperative that countries are encouraged to have the proper infrastructures. Hence, infrastructure designates to the material and institutional layout along with facilities required for society and enterprise operation of all types, which include transportation networks, communication systems, utilities, and architectural structures. Infrastructure development can be considered as a basic requirement for trade facilitation due to the fact that it helps to cut down on transportation expenses, improve linkages and efficiency of logistics. Research on interlinkage between infrastructure development and trading expansion has brought to light their strong correlation. For example, transport network and port infrastructure studies by Limao and Venables (2001) showed that the economic development is largely influenced by the infrastructure improvement; and Estache and Fay(2007) indicated a positive impact of infrastructure investments on trade expansion. Appropriate port facilities and transport infrastructures will eventually increase the trade cost and improve your possibility of having a competitive market in a global scale. In the meantime, technologies introduce an entirely new dimension in the global trade environment, as they make the trade process more efficient, interconnected, and innovative. The advances in e-commerce, worldwide trade, industry, and automation humanity now experiences has changed dramatically what people consume and how products and services are made and traded. The article is based on the numerous studies, done in the field of trade development after the technological improvements. To illustrate this, the empirical research by Eaton and Kortum (2002) on technology and trade has proven such a technology-driven innovation leads to the increasing productivity and specialization of manufacture at the international level. E-commerce platforms have played a role in making markets more accessible to Small and Medium-Scale Enterprises (SMEs). At the same time, digitalization has simplified the chore of supply chain management as well as trade financing processes. Non-Economic Factors There are a number of non-economic factors influencing the nature of trade relations among nations. Thus, the political stability which is the lack of political unrest or instability, is a key factor in creating the environment for trade development. Countries with stable political environments are the ones that have a high chance to attract foreign investment, to create business confidence and to facilitate the international trade relations. Political stability is the main factor that creates a suitable environment for the long-term planning and investment, which are the key components of the sustainable economic growth. The studies have in fact proven that the connection between political stability and trade openness is very strong. To illustrate, De Soysa and Oneal (1999) carried out a detailed investigation of political stability and its effect on trade openness in different countries. They discovered that the countries with stable political systems usually have higher levels of trade openness, as indicated by trade-toGDP ratios. In the same way, Li and Resnick (2003) examined the connection between political stability and foreign direct investment (FDI), and found out that political stability greatly affects the confidence of investors and FDI inflows. Moreover, political stability creates the environment of trade liberalization and the development of the policies that are favorable to trade. Governments that are stable are more likely to implement trade reforms, curb trade barriers, and negotiate trade agreements with other countries. To give an example, the countries with the stable political systems are in a better position to do the bilateral and multilateral trade negotiations, thus, the market access is increased and the trade is expanded. Beside the political stability, it is necessary to have a solid legal framework. A solid legal framework is crucial for the creation of a predictable and secure environment for the trade transactions and business operations. The main legal institutions that are needed for trade to exist are contract enforcement mechanisms, property rights protection, and dispute resolution systems. A working legal system assures businesses of the possibility to engage in trade activities, enter into contracts and to enforce the justice in the case of disputes. Studies have proved that the quality of the legal institutions of a country greatly affects the trade competitiveness and the investor's attraction to the country. Djankov and his colleagues (2003) did significant research on the ease of doing business and trade, which was a study on the impact of legal systems on trade development. It was proved that countries with wellequipped legal systems and suitable regulatory conditions receive more investment and have greater trade openness. In addition, a strong legal system, being self-regulated, increases market transparency, cuts the costs of transactions, and eliminates the risks of trade and investment. Property rights protection is the best way to make sure that businesses can safely invest in physical and intellectual assets which in turn will result in better innovation and entrepreneurship. Contract enforcement mechanisms are the ones that are very effective in the case of contract breaches. They give businesses the required legal actions so that they can claim their rights in such cases, which in turn creates a lot of trust and reliability in the trade relations. Additionally, there are further socio-cultural factors which influence the social, cultural and behavioral variables so that they play a crucial role in shaping trade patterns, consumption habits, and market dynamics. Policies, social values, and lifestyle preferences are the main elements that influence consumer choices, market demand, and business practices (Vira et. all ., 2015). Socio-cultural factors are the key factor for businesses to find the best way to penetrate different consumer segments and to adapt their products and marketing strategies to the different cultural contexts. Hence, many academic works have dug deeper into the ways in which the socio-cultural factors affect trade behavior and market development. For instance, Hofstede (1980) was the first to conduct research on cultural dimensions and their influence on organizations' behavior and management methods. His groundbreaking research on cultural dimensions like individualism-collectivism, power distance, and uncertainty avoidance, which have a great impact on international trade and business operations identified. Besides, Levitt (1983) studied the worldwide spread of consumer culture and the consequences it has for international marketing methods. He stated that globalization makes consumers all over the world have similar preferences and the consumer cultures become almost the same. Nonetheless, Levitt also realized the significance of cultural differences and local tastes in the influence of consumer behavior and the market situation. Besides this, socio-cultural aspects have an effect on trade patterns by influencing consumer taste, product design, and marketing methods. As a case in point, the choice of certain tastes, colors or package designs in the foreign markets may affect the success of a product. Just like, cultural taboos or sensitivities may make businesses re-design their marketing messages and communication strategies to go with the local audiences. Gravity Model in Trade Analysis Extensive research has related to the impacts on bilateral trade that are uncovered after studying different countries, regions, and blocks of trade. Nevertheless, we may witness that this sector of vital significance is rather underrepresented in the scientific literature since our efforts to collect information reveal that the academic literature is relatively limited on certain countries, despite significant research efforts. Antonuccia and Manzocchi (2006) made gravity model the instrument to analyze the import flows of Türkiye from 1967 to 2001. This analysis showed how appropriated the gravity model was for the geographic trade patterns of Türkiye. They learned that the gravity model correctly described trade movements between Türkiye and mostly all trade partners and, surprisingly, found that the Agreement of Association and the customs union had no observable trade boost between Türkiye and the EU despite the fact that it was first introduced in 1963 or 1996. Another study regarding the Gravity model has been conducted by Julian and Hakan (2017), and it is more inclined towards Türkiye's exports and imports within the clusters of regions and using the panel data gravity model concluded on the bilateral trade costs. Their research covers 180 countries between 1960 to 2012 in which they made use of the DOTS data. Sectoral level analysis is also carried from the year 1994 to 2010 for 176 countries, and the information is drawn from the BACI database. They also validate the fact that the gravity model operates effectively in explaining trade flows across any region of Türkiye used in this analysis, particularly demonstrating the strong impact of neighboring regions, such as the EU27, on Türkiye’s trade flows. The Customs Union of the EU turned to be both in favor of and against Turkish exports and imports, though certain trades, industries, and certain time frames were obviously more for than against. While the gravity model used by Oh et. Al., (2018) examine Uzbekistan's trade patterns, the policy implications obtained as a result could serve as a guide. They also recognized that the predicted export volumes for Uzbekistan and the main partners were considerably higher than the real flows counting, therefore, there is a space for Uzbekistan to develop trade with its neighbors with strategic agreements or capitalizing on geographical advantages. Allayarov et al. (2018) examined the determinants of Kazakhstan-bound exports and, eventually, its trading potential. The previously mentioned gravity model was used. The research found that the country's GDP and the GDPs of its trading partners tend to grow in tandem while the trade among the partners tends to decrease with the number of their population and distance between them. Scheduled trades showed a promising trade potential with countries like Russia, China, and few Central Asian nations which can be leveraged. The study conducted by Khitakhunov et al. (2017) focused on the consequences of the creation of the Customs Union (comprising Belarus, Kazakhstan, and Russia) for the regional patterns of trade by applying the gravity model. However, their study indicated an apparently negative but no significant influence on world trade due to the internal factors of the countries, their difficult external economic environment, as well as the fact that the customs union is just being established. In the work of Zeynalov (2017) the author revealed economic size and institutional similarity of bilateral trade. Referring to databases about trade between Azerbaijani and the 50 other countries in the time span between 1995 to 2012, the study emphasized the role of institutional trustworthiness and the rule of law in trade conduct. It thus found out that countries of similar institutional caliber like to engage in trade business more and that many differences in the institutional qualities may be a barrier in the way of trade relationship between the two countries. Last but not the least, Choi and co-authors (2019) worked on the research of sustainable commerce involving China and Kazakhstan, based on an upgraded gravity model which includes the spatial factor and the specialities of that trade relationship. Their studies highlight the feedback effects of distance and liberalization of free trade areas to affect trade magnitude. Their results show a sophisticated view of the way variables impact trade sustainability. Social Network Analysis in Trade Studies It is an important point that today economic integration is substantially interrelated with the trade and investment. In an application of Social Network Analysis which is focused on understanding the complex interactions and dynamics that shape global trade networks, it is no doubt that trade and investment needs to be analyzed (Borgatti, Everett and Johnson, 2018). Underneath, the SNA is a set of schemes which describe the networks of relations and the actors who stand at the base of International Commerce and Investments (Newman, 2010). Among the major ways that SNA is used in trade and investment is through the study of trade networks and the organization of supply chains. Through such things as linking countries, regions and economic groups, SNA shows where the most influential hubs are, as well as the most important players, and networks (Wasserman & Faust, 1994). Furthermore, SNA enables researchers to not only examine the function of social networks and connections in influencing trade and investment interactions, but it also allows them to understand the way in which different constituents of the networks, namely the individuals, the firms as well as the governments, work together in fostering trade and investment. Through the investigation of the ties between companies, investors, government staff and other interested parties, a SNA may reveal hidden informal networks, trust based relations and information flows which affect trading and investment decisions (Borgatti et al., 2018). These social aspects are the ones that policymakers and businesses should perceive as a fundamental element needed to develop economic cooperation, increase investment, and to secure trade involvement. Principles of Social Network Analysis The identification of the pivot players or the hubs within the globe trade network forms one of the key areas upon which the team will focus when conducting the study based on the SNA. The criteria that identify major hub nodes, such as degree centrality and betweenness centrality, are valuable in locating countries or regions which are fundamental for the establishment of flows of trade and in connecting distant parts of the network (Timar, 2013). With their network being quite dense and so that multiple trade routes and channels of distribution tangibly pass through them, these actors can play very decisive roles in international trade governance. Beyond that, SNA provides grounds for the scholars to appraise the key determinations and vulnerabilities of the world trade systems subject to destabilizations. According to Freeman (2004), through representing the network topology, researchers can simulate by the system of trade not only the trade war but also the geopolitical tensions and natural disasters among others to assess their possible impact on trade and connectivity of the network. Discoveries from such data can feed international leaders and businesses towards developing effectual risk solving procedures and a strengthening of supply chain systems on a global stage. Another role of SNA in the studies of trade is empirical discovery of the clusters or trading communities that are formed in the network. Community detection algorithms are useful in the identification of clusters of countries or regions connected internally and involved in the inter-trade, economic unity, or supply chain network which is proof that there is existence of regional blocs, economic alliance, or supply chain. A thorough comprehension of the trading groups aids governments and companies alike in defining markets, judging competitors' landscapes, and creating custom policies. Also amenable to SNA is the study of global trading partnerships and their overtime developments and changes. Through tracing the network performance, the changing links, connectivity and the centrality metrics observing researchers may reveal the developing tendencies, trade pattern changes, and emerging partnerships between countries and regions. These insights provide policymakers, businesses, and others with the foresight to be able to predict the effects of the market especially economic growth and possible opportunities and also come up with a probability of how best to take advantage of changing trade dynamics. Methodology Research Design The research design in this research paper is based on the mixed methods consisting of both quantitative analysis along with the qualitative inputs. The essence of the quantitative analysis of trade is the application of the gravity model, which is the acknowledged theory of international economics, for studying the factors that impact upon the bilateral trade flows. Gravity model, which was originally developed by Newtonian physics and led by Tinbergen in 1962 and by Pöyhönen in 1964, assumes that trade flows between countries will increase together with their economies' sizes and reduce in proportion to the distance between them. It is then possible to create a trade volume predicting tool based on some crucial economic variables and geographical distances, which produces information helpful for explaining the key drivers of international trade. The qualitative research is, in this manner, used as a complementing technique that comes after the quantitative approach where it entails an extensive literature review. This is all done through synthesizing and reviewing the existing literature on the economic non-economic factors that govern trade development. These factors include political stability, legal frameworks, socio-cultural factors, and environmental considerations. By making use of an array of academic sources, the qualitative analysis throws light on trade behavior and preps the quantitative results up with what is needed. In addition, the social network analysis (SNA) model, which comprises of graph theory, is implemented into the research design to explore the structural characteristics of trade networks. SNA allows to map and study the relationships among countries as nodes and trade flows as edges used to build a network graph. The graph theory metrics like the centrality measures, the clustering coefficients, and the network density are some of the tools used by SNA and sheds light on the topology and dynamics of the international trade network. Data Collection Methods In this research, the interaction between trade and foreign direct investment (FDI) of the six Turkic States—Azerbaijan, Türkiye, Kazakhstan, Turkmenistan, Kyrgyzstan, and Uzbekistan—is analyzed. The study utilizes two main methodological frameworks: an illustration of a gravity model of international trade and a social networking analysis for FDIs. Gravity Model Data Collection Hence, one model which is the gravity model or the panel data which cover the time frame from the year 2000 to the year 2021 was used to investigate export and economic indicators among the six Turkic countries. Data on Gross Domestic Product (GDP), exchange rates, and population metric yearly were hand-picked from the complete database of the World bank (World Bank, 2021). Bilateral trade data were extracted from the United Nations Commodity Trade Statistics Database (UNCOMTRADE) which is named for the records on trade transaction details exchanging nation to nation (United Nations, 2021). Certain data problems were experienced, specifically GDP dynamics for Turkmenistan for 2020 and 2021 as well as data on trade between Turkmenistan and Uzbekistan through 20102019. To narrow the gaps, we used backcasting strategies derived from historical data trends and extrapolation from forecasted GDP numbers in reputable international forecasts (International Monetary Fund, 2021). Further to this, the distance metrics on which gravity model is based were calculated by means of the capital city distance for each pair of countries. This data was obtained from CEPII (Centre d’Études Prospectives et d’Informations Internationales) which are the most used geodesic (spherical triangle) distances in literature on international trade studies (CEPII, 2021). To determine the variables regarding the openness to trade, a proxy was created which consisted of summing up the total exports and sum of imports, for each country and divided that number from its real GDP, hence the measure of each country's level of integration into the global trade system. Also, legislative acts and regulatory frameworks were reviewed to be able to designate the dummy variables fundamental for the model. Descriptive statistics such as the mean for all variables, standard deviation, minimum and maximum values were computed and are presented in the empirical analysis section in order to overview the distribution and scale of data. Social Network Analysis Data Collection The study on FDI among the Turkic States adopted the use of a social network as a strategy where the research applied the capabilities of graph theory to examine the network of FDI among the Turkic States. Statistics for this data were collected through the Coordinated Direct Investment Survey (CDIS) database which is under the International Monetary Fund (IMF) that keeps records on recent mutual FDI positions (International Monetary Fund, 2019). The base year 2019 was chosen in view of the fact that the global economy has been affected by the COVID-19 pandemic and the base period cannot be valid in the context of the COVID-19 pandemic. Countries within the Turkic World were considered as connected nodes in the network of these countries and associations between these nodes were established by means of FDI links identified from data provided by the CDIS database. The total of fifteen direct investment links for the above six countries formed the target for constructing the network. The size of each node in the resultant network graph was based on the relative degree centrality; which is the measurement of the degree of a node to measure the importance of a country in the network of FDIs. Gravity Model Application A gravity model is used in the research paper to evaluate trade flows among Turkic nations. T he gravity model, first introduced by Tinbergen (1962) and Pöyhönen (1964), has frequently been applied in trade analysis to explore the determinants that affect peering between trade partners. By this, the formula expresses the common sense that trade flow between two countries is affected by their sizes and quite inversely to the square of the distance between these countries. (Tinbergen, 1962; Pöyhönen ,1964). The traditional gravity model of trade is represented mathematically as follows: where: ??? ? is the trade volume between country i and country j . GDPi ? and GDPj ? are the gross domestic products of countries i and j , respectively, representing the economic size. Dij ? is the distance between the two countries, serving as a proxy for trade costs. In the context of econometric modeling for an empirical analysis, the gravity equation is often logarithmically transformed to linearize the model, facilitating the estimation process: ln(???)=?+?1ln(????)+?2ln(????)+?3ln(???)+??? While discovering the structure of trade among the latter was the primary objective of this research, it is needed to make the use of augmented gravity model of trade. This model, based on the Newtonian concept of gravity, states that the trading volume between two countries grows as their economic mass increases and it falls when the distance between them increases. ln(Tijt?)=?+?1?ln(GDPit?)+?2?ln(GDPjt?)+?3?ln(DISTij?)+?4?ln(POPit?)+?5?ln(POPjt?)+ ?6?ln(IMPij?)+ ?7?ln(EXPij)+?8?ln(EXRij?)+?9?BRDij?+?10?LNGij?+ ?ijt? Where Tijt? represents the trade flows between countries i and j over time t, GDPit? and GDPjt? denote the gross domestic products, DISTij? is the geographical distance, POPit? and POPjt? are the populations, IMPij? and EXPij? includes variables related to importers and exporters, EXRij? represents the exchange rate, BRDij? and LNGij? are dummy variables for shared borders and languages and ?ijt? is the stochastic error term. In this examination, we include trade variables with lags to outline the periodical effects of historical trade linkages on contemporary trade transactions by analyzing the relation between past trade relations and current trade volumes. This dynamic five-year panel data modeling framework is effective for uncovering time series trade relations, the types of flows, and their degree of persistency over time. Another important issue here is the bias of independent variables such as GDP, which might be due to trade flows rather than underlying causes itself. The instrumental variables solution is being developed to address this issue. In the model, in addition to historical GDP values that has been presumed exogenous and also geographical traits that have be expected to be exogenous, these instruments are used to mitigate the reverse causality in the model. The model estimation will be concerned with both fixed and random effects, preliminary tests of which are the diagnostic test of the houseman. Thus, this method serves healthy treatment of unobserved heterogeneity, which might be otherwise conflict the model estimates. The specific nature of these tests may require the selection of the most appropriate estimation approach from the two available techniques: the fixed effects method or the random effects method. The sturdiness of the model’s estimate will be verified through several methodologies, at least such as multicollinearity tests as well as sensitivity analysis which occur when there are different model specifications. The estimating methods of GLS and MLE will be applied to avoid the heteroscedasticity and autocorrelation that may arise within the error terms, which in turn will increase the accuracy and validity of the estimates. Social Network Analysis Approach The center of the social network analysis (SNA) is the measure of centrality. The metrics is used to highlight the roles of nodes (countries in our case) within the trade network. There are the equations and detailed interpretations for three principal centrality measures utilized in this study: the degree centrality, betweenness centrality, and closeness centrality. Degree Centrality The degree centrality expresses the number of direct connections (edges) a node has to others and is a measure of the importance and concentration of the node among other people in the network. The formula for degree centrality for a node ? is: here, ???(?) is used to denote the degree of the node ? which refers to the number of edges that exist outgoing from the node ?. Moreover, the number n?1 is used to refer to the theoretically possible maximum connections, assuming ? is the total number of nodes. These standardizations allow for comparing different-sized networks. Betweenness Centrality The concept measuring betweenness centrality in the network represents a situation when a node serves as an intermediary between the shortest paths between the pairs of the other nodes which may easily be recognized as the influence over the information or resources flow throughout the network. Below is the calculation for the betweenness centrality for a particular node v. Namely, where there are ??? is the total sum of paths that you could take from node ? to node ? and ???(?) is the count of those paths which goes through node ?. Normalization factor 2/(n?1)(n?2) ensures the applicability of this measure to any graph of any size and connectivity, which is an advantage over the methods in which the network scale influences results. Closeness Centrality Closeness centrality measure determines whether there is a given node can reach all other nodes in the network quickly, giving a measure of the central location for this node in terms of its median distance to other nodes in the network. The formula for the closeness centrality of a node ? is: Therefore, this equation shows ? (?,?) as the shortest path distance between nodes ? and ?, and ??1 normalizes the measure to take into account networks of different sizes, which helps comparative analysis. Moreover, to put it in a different context, The network centrality measures when applied to a Turkic States’ trading network give important clues about development and manipulation of strategic positions in the region. Node degree centrality is used for tracking the trade partners of countries with extensive trade connections playing significant roles in regional trade performance because of many direct trading associations a country has (Freeman, 1978). Such findings show that these various places have a sentimental attachment to these economies because they have been connected for quite a while at various levels, the fact that these countries often move large volumes of trade. The network centrality measures when applied to a Turkic States’ trading network give important clues about development and manipulation of strategic positions in the region. Node degree centrality is used for tracking the trade partners of countries with extensive trade connections playing significant roles in regional trade performance because of many direct trading associations a country has (Freeman, 1978). As a result, the countries with multiple connections can be considered as big players in regional trade and they are the ones which keep the trade dynamics at the highest level by handling the biggest volumes of trade. Betweenness centrality shows another point of view on countries who dominate the routes that are essential to trade and close trade links within the network (Newman, 2005).. As such, centrality normally arranges a set of nations that use geographical or political-economic advantage for trade mediation among other countries. This role is quite crucial for ensuring network continuity and security. It is because the efficacy of global trade depends on these nodes and making these nodes essential for the same. Closeness centrality is a measure of countries' ability to be efficient in trade with others in the network. This may optimize costs for transaction, trade facilitation and trade barriers (Borgatti & Halgin, 2011). The countries which hold the highest closeness centrality usually have the shortest path lengths to all the other nodes that are direct international market accessors and this, in turn factors into their ability to conduct their overseas trade cost effectively. Likewise, such real capacity plays a significant role in fostering the ease of trade activities by overcoming logistical and operational hurdles, making the economic activities more viable and competitive. Overall, these measures of centrality enable the construction of the entire complex of the dynamics within trade network, which allows to point out key positions and to analyze indepth the mechanisms underlying the trade interaction among the Turkic States (Opsahl, Agneessens & Skvoretz, 2010). It is the integrated method which is not only leading the empirical understanding of the network behavior but, moreover, is forming a reliable basis for policy formulation and strategic trade negotiations. Through the diagramming of a dense net of trade contacts, policy makers and economic planners would be able to figure out where to intervene, to build up more intense trade relations, and foster a region's cooperation for perfecting the trade efficiency (Carrington et al., 2005). Analysis and Findings Results using the Gravity Model Reflecting on the applied methodological approach and the gained empirical insights based on the mutual trade data of Turkic States (Azerbaijan, Türkiye, Kazakhstan, Turkmenistan, Kyrgyzstan, and Uzbekistan) from 2010 to 2019 will offer a valuable perspective on the dynamics in this region’s economy and efficiency levels of various trade theories in accounting for the observed tendencies. It is especially useful for ascertaining the intensity and spectrum of these countries’ economic connections, based on import and export figures. The following statistical table highlights the differential pattern in traded values both export as well as import in the six Turkic States (Table 1). The export shares are significantly high for Azerbaijan, Kazakhstan and Uzbekistan and thus Türkiye becomes a trade king of the region. As asserted herewith this signifies the impact of Türkiye’s economically dominant power and as a prime exporter in the region. The same is true for the total import figures where the nation shows a trend of extensive import activity, predominantly with Kazakhstan and Uzbekistan via which the country tends to fulfill most of its import requirements. Among the significant export partners, Kazakhstan also holds considerable exports to Türkiye and Uzbekistan, which may evidence the overall exporter nations possibly due to natural resources like oil and minerals. There is also higher trading engagement between countries situated close to each other such as Uzbekistan and Kazakhstan interactions which may be explained by close physical proximities and possibly cultural and historical similarities. Table 1. Mutual Trade – Export and Imports in mln USD Azerbaijan Türkiye Kazakhstan Turkmenistan Kyrgyzstan Uzbekistan - 890.00 55.00 56.00 8.20 11.70 Export Azerbaijan Türkiye 1,460.00 - 740.00 875.00 247.00 510.00 Kazakhstan 180.00 1,235.00 - 93.50 425.00 1,040.00 Turkmenistan 72.50 358.00 74.00 - 5.10 138.00 Kyrgyzstan 2.90 55.50 215.00 4.20 - 106.00 Uzbekistan 11.80 680.00 490.00 54.20 205.00 - Azerbaijan - 940.00 160.00 94.00 1.65 29.50 Türkiye 310.00 - 1,140.00 370.00 59.50 650.00 Kazakhstan 54.00 655.00 - 91.00 193.00 590.00 Turkmenistan 64.00 950.00 100.00 - 5.30 56.00 Kyrgyzstan 5.10 130.00 390.00 5.30 - 112.00 Uzbekistan 5.60 685.00 1,150.00 149.00 48.00 - Import Source: United Nations COMTRADE Indeed, the traditional conventional gravity model where the volume of bilateral trade is determined by the product of the economic mass (measured by Gross Domestic Product) of the two countries involved and the distance between them can be employed here. For example, the large trades between Türkiye and Kazakhstan, and between Uzbekistan and Kazakhstan reflect these two countries large economy and intermediate geographical space. The gravity model also has an aggregate version that can as well explain lower volumes of trade between Kyrgyzstan and other states where geographical and most likely economic distances are even more significant. This theory of international trade, which holds that nations should export goods in industries that enjoy competitive advantage, may help explain the high exports: probably due to the mineral wealth of Kazakhstan. Likewise, the Turkish industrial structure provides the country ability to supply many products within its neighboring countries. Import behavior which concentrates on final goods that a nation is less capable of producing efficiently as the home country also embodies this principle. In extension to the classical trade theories, new trade theory incorporates two factors namely economies of scale and network effects as factors that foster trade, alongside product differentiation. Some of the factors that may partly explain the centrality of Türkiye as a trading nation include scale economies in production and a vast web of trade that boosts its export and import demands. Moreover, according to table 2, the gravity model has been widely used in international economics to analyze the pattern of foreign trade between countries bearing in mind economic and non- economic factors. It is more particularly useful for the analysis of the Turkic States, delivering an overview on the specific forces which may guide the trade relations between them. With regards to factors that include GDP, geographical distance, population size, trade frequency, exchange rate, and cultural similarities, the gravity model gives perfect ground for an all-round approach to estimating trade flows. It can, therefore, be concluded that the economic capacity of a particular country referred to as the gross domestic product of both the exporter and importer significantly influences the level of trade. The two are important indicators; the mean of the GDP for the exporting countries and the mean of the GDP for the importing countries are high, signifying that the economic activities of these countries are healthy and vibrant. The integration between economic capacity and trade volume is well backed as more economically endow countries offer both exports due to their ability to produce many goods and services and also imports supported by bigger markets. The stable GDP for these nations over this period, evidenced by small variance in these values implies economic stability within the study period. Another component of the gravity model include distance which can be defined as geographical distance between trading partners. The data is presented in terms of distance, which occupies the area within the middle mark to slightly above and below the middle mark to indicate moderate separation between trading partners as given by the mean. In general, it implies that any extension of distances reduces the rates of transport and logistics which result in less trading. The fact that some Turkic States are close while others are considerably farther indicating that distance may be limiting the amount of trade that occurs among them. Table 2. Summary of Variables Included in the Analysis In addition to the interpretation of table 2, this is true because the population size is an important key that determines the nature of trade between two countries. The data presented initially shows a quite high population of the countries concerned, meaning big customer demands. In comparing the two equations, the gravity model suggests that larger populations will create larger demands for the final product, be it imported or locally produced, which would in turn boost trade volumes. These results could be explained by the fact that the size of the population in each of these countries is comparable, which means that there is a mutual potential for trade, with both countries having a large market for the goods produced in the other country. Import and export statistics reveal that these nations have significant interaction, however the mean values suggest moderate changes within the middle value. These fluctuations can originate from variation in the nature of trades in goods, policies on trade, and the economic status of the region. In this regard, exchange rates, another significant variable has a wide range that hints at the differences in monetary policies, and the surrounding economical context that influence trade costs and demand. The use of the variables arising from the cultural and administrative similarities such as shared borders/ languages is in the form one or-zero variables’ dummies. These elements dilute measures of trade constraints, improve flow of information and establish goodwill among trading counterparts, hence leading to improved and perhaps expanded trade interaction. Moreover, table 3 provides significant information extracted from the analysis using the gravity model. Primarily, the use of the natural logarithm has the implications that the increase in variation corresponds to the increase in the level of GDP, and the positive value of the correlation coefficient, equal to 0.41, means that there is a moderate positive relationship between ln(Tijt) and ln(GDPit). This means that as the exporter’s GDP rises, the amount of trade also rises, conforming to neoclassical theories pointing that countries with higher levels of production capacity export more due to their wealth (Anderson & van Wincoop 2003). Likewise, there is moderately strong and positive correlation between ln(Tijt) and ln(GDPjt) and has correlation coefficient of 0.70. This stronger relationship further brings out how the economic size of the importer plays a core role in explaining import attraction contrary to the basic assumption of the gravity model that bigger economies demand more hence import more (Krugman & Obstfeld, 2009). On the other hand, the coefficients of determination relating ln(Tijt) as the dependent variable and ln(DISTij) as the independent variable is a negative value of -0.61, which show that there is a strong negative relationship between the two variables. This supports the gravity model’s hypothesis of reduced trade between regions as distance scales up because of the increased cost of transport and other logistical factors (Bergstrand 1985). The simple regression of ln (Tijt) with the natural log of population at time ‘i’ and ‘j’ or ln (POPit) and ln (POPjt) respectively is 0.63 and 0.25. Hence, this means that the population size of the exporter exerts a relatively strong, positively, and directly influenced the trade flows compared to the importer population size. This may due to; larger populations in exporter countries that in turn enhance production abilities hence export capacities (Helpman, Melitz, & Rubinstein, 2008). Hypothesis two of the relationship between import activities is negative (-0.30) while for export activities it is positive (0.18) indicating that the two activities have a different position in the trade flow model. It is also negative with imports, which might suggest that high import levels could be coming and replacing domestically produced exports. On the other hand, the relative positive co-efficient of exports establish a direct link between export activities and trade level. consequently, As it stands, they show a slight positive relationship of 0.06 between ln(EXRij) and ln(Tijt) which means that the export cost is slightly lowered for foreign buyers through depreciation of the exporter’s currency – a factor that improves trade, as pointed out by Gopinath, 2015. Table 3. Analysis of Variables – Correlation Matrix Source: The Author’s elaborations These correlations can be considered as the empirical evidence of several theoretical points mentioned in the thesis. Positive and relatively stable correlations of trade flows with both the GDP and population size confirm the classic economic theories regarding the scale and capacity of markets as critical determinants of trade. Moreover, empirical evidence of the distance coefficient paints a picture confirming theory of the basic assumptions of the grave model while emphasizing more on the solution to the distances that hinder trade through enhancement of the logistic facilities and agreements that reduce tariffs for distant trade partners. However, the complex nature of imports and exports in determining the trade flows shows that the policies encouraging export growth may help to increase total trade. Moreover, the importance of exchange rate stability means that, in the same way, monetary policy should also have a great deal to do with international trade. The positive relations with cultural and institutional variables such as shared borders and languages indicate that soft power and diplomacy may be just as important to trade policy as the hard economic variables. There is need to encourage cultural diplomacy and establishment of cultural relationships such as through exchange training programs, cultural fairs and relaxing visa requirements as they create a favorable environment for trade. In addition, GDP of the export country which is ln(GDPit) and GDP of the import country ln GDPjt both have positive coefficients of 0.43 and 0.69 respectively with P values of 0.000. From this, we find out that an increase in the GDP on either side has a positive and direct effect on the flow of trade between these countries. The coefficient for ln(GDPjt) is greater, which mean that to explain import of goods and services, the economic size of the importer is a little more important as it can be expected from the demand theory. Furthermore, the coefficient estimating geographical distance (ln(DISTij)) is negative (-0.61) and statistically significant in the context of its level (p<0.0001). This supports the gravity model hypothesis that distances between trading partners have a negative or inverse effect on trade volumes perhaps as a result of escalating transport costs and other cost factors associated with the distance. Therefore, in the model, we can approximate coefficients for several variables, including the population of the exporting country (ln(POPit)) and the population of the importing country (ln(POPjt)). These results corroborate and extend prior literature and indicate that increased population in both the exporting and importing country help increase volumes of trade, perhaps because of greater market demand. On the other hand, the exchange rate (ln(EXR ij)) attains the smallest positive coefficient estimate of 0.06 and an associated p-value of 0.000 which suggest that favourable movements in the exchange rate (perhaps a depreciation of the exporter’s currency) could marginally enhance the amount of trade by making exports cheaper. Additionally, the coefficients for import activities (ln (IMPij)) and export activities (ln (EXPij )) are 0.17 and 0.07 respectively. Nevertheless, this increase only holds for the export volume as the result of the estimated regression indicates that overall trade is positively affected by the export activities with a statistically significant P-value of 0.000 whereas this applied to the import activity yields a P-value of 0.1741, thereby pointing out that the import activity is not statistically significant at conventional level of 0.05. The variable of the shared borders (BRDij) has a positively high coefficient of 1.56, with a Pvalue of = 0. 000), which demonstrates the fact that bordering countries have a much higher level of trades, probably because of the lower costs that come with borders among those countries and more frequent contacts and business relations. On the other hand, the coefficient of LNGij which depicts the impact of common language is negative but with a low value of -0.01 and a P value of 0.980 meaning common language has no significant influence on the findings. This implies that in this particular African regional context for instance, language is not a major driver of trade like what could be observed from distance. Table 4. Regression Analysis Results Source: Author’s elaboration The fact that the particulars with the highest statistical significance (GDP of both importers and exporters, distance between them, population size, export operations, and overlapping borders) hold significant roles in trade relations supporting the statements. These results can be quite expected under economic theories that consider the economic size or scope, the size of the market, the logistics, and institutional enablers of trade (Figure 1). Figure 1. Determinants Impacting Trade Relations of Turkic World Source: Author’s elaborations Consequently, it is demonstrated that the suggested gravity model is indeed an appropriate way of considering the dynamics of trade inside the Turkic region. The main variables indicate the friction points that policy-making individuals and entrepreneurs can shift to improve the climate for trade. For example, such strategies as concern with economic growth, enhancement of transport links, development of better institutional relations between state members might be rather efficient. Secondly, a non-significance of shared language also indicates that cultural barriers might not play a paramount role in dictating the collaborative regional trade as opposed to economic and logistical ones in this specific case. Results through Social Network Analysis The Social Network Analysis (SNA) enriches the understanding of the patterns of Foreign Direct Investment (FDI) with the Turkic States by presenting the given data in terms of nodes and ties, which indicates the distribution of the participation of nations and the relations between them. Using the degree centrality, closeness centrality, betweenness centrality and eigenvector centrality measures, the result gives a comprehensive view of the role and positional importance of each country in the investment network. This seems to at least help unveil not only the basic mesh of investment connections but also the position that each state has or is placed in the regional investment context and coordinates of power while doing so; information that is invaluable in designing optimal strategies towards the growth of FDI and the optimum level of integration. Türkiye and Kazakhstan have been identified as critical points with a large degree centrality showing that both countries are the prominent nexuses and destinations as well as sources of FDI in the region. Their out-degree centrality portrays them as firms that are active and forceful in outbound investment strategies implying that they will passionately engage in direct and indirect investment influence on regional investment destination patterns as indicated by the closeness centrality. It is also evident that Azerbaijan has a stringent geographic position with high eigenvector centrality; suggesting their investments are tied to other significant investors in the network intensively. This implies that other central players are primarily influenced by or are in sync with Azerbaijan’s investments; which if encouraged, can be optimally utilized for the development of cooperation in the region specifically more investments into the country’s growing oil and gas industries. Taking into account the fact that the size of the Kyrgyzstan and Uzbekistan economies he significantly less than that of Russia and Kazakhstan, it is quite revealing that both of these countries occupy rather high positions in terms of calculated closeness centrality. This shows that they are likely to be equally important in acting as ‘bridges’ in the investment network, capable of ferrying traffic of large economies efficiently. It implies potential for identifying new markets within which investments can be made that are destined to benefit from geographic and strategic value relative to even more core localized economies. Consequently, this is further confirmed by the low centrality measures of Turkmenistan in all the mentioned metrics suggesting that it is a relatively marginal player in the FDI network in the region. This could be due to restrictives investment policies or unsavoury business environments that becomes a challenge in sourcing capital from external sources. Figure 2. Social Network Analysis of FDI among Turkic States Source: The Coordinated Direct Investment Survey (CDIS), the IMF. When the above states have been compared in terms of these centrality measures, this provides some information on the level of connectedness or isolation that each is in the geographical context of regional FDI. Regions such as Türkiye and Kazakhstan, which have invested in diversified portfolios as well as key economic measures, probably realize stronger economic stability and increase, thus becoming more lucrative to individuals. On the other hand, the low values of centrality in Turkmenistan hint at the necessity of implementing policies which would foster improvement in investment environment. This may entail liberalizing FDI policies, increasing the degree of openness, and developing physical and governance structures to address multinational institutional compatibility with the ASEAN economy. Moreover, among the networks, Türkiye achieved the highest degree centrality of 3 141.6. This can be viewed as evidence that Türkiye bears not only regional importance but also serves as a crucial node in global supply chain. This high figure may be owed to the fact that it is geographically situated between Europe and Asia, and that the country it engages in both export and import of a vast and wide variety of products and services. High centrality score centred in Türkiye means that it has most of the direct linkages with the other countries in actual network display of an invaluable position and influence in Middle eastern trade systems. Kazakhstan is next in line with a degree centrality score of 2117.6. The report thus underlines the significance of the Kazakhstani economy’s interaction with the external milieu, particularly within the confines of the Central Asian region. It is noted that Kazakhstan is rich in natural resources especially oil and mineral and the trade relations are therefore expected to be a reflection of these gifts. A high centrality index presents the country as being strategically positioned in the economic nexus of the regional and global market as a source of raw materials for surrounding countries and worldwide. Uzbekistan 1651.8 degree centrality of the network also can be highlighted as significant concerning its economic performance which implies rather sound macroeconomic reforms and slowly but steadily developing liberalization of international trade. Uzbekistan placed at the centre can also be viewed as an emergence of the country as an important actor within Central Asia’s trade supply chain, as well as initiatives to develop the internal market and expand trade openness. Thus, Kyrgyzstan and Azerbaijan have shown relatively fewer centrality scores of 1508. 6 and 1127. 6, respectively. The former is considerably higher indicating moderate amounts of contact in the import and export of goods in the rest of the network. Thus, has been witnessed that natural energy resources, transit status in Central Asia for Kyrgyzstan and export orientation toward oil for Azerbaijan, remain as some of the most influential determinant in the bilateral trade although its value is moderate, compared to Turkey, Kazakhstan or Uzbekistan. Turkmenistan has the least of the entire centrality measure at about 772.4. It occupied a rather less strategic place in the regional trade system. The specificity of Turkmenistan’s export, mainly derivatives of gas, and its economic policies may explain its lower score since the country may be engaged in fewer, but larger volume, economic relationships. Figure 3. Social Network Analysis of Foreign Trade among Turkic States Source: The BACI CEPII dataset These trade relations indicate that Türkiye possess a significant potential to use its strategic centrality to expand its trade relations by establishing new partnerships and increasing the connectivity within the region through infrastructure and logistics development. Kazakhstan and Uzbekistan could look at widening the trade networks, improving export differentiation accompanied by added manufacturing in a bid to minimize dependency on commodities. This paper suggests that Kyrgyzstan and Azerbaijan should implement policies that involve a deeper integration with the extent of regional trade relations through the use of agreements that could reduce the trade barriers in addition to increasing the interactions across the borders. There are also possibilities for Turkmenistan to mitigate problems of their geographical location by participating in regional trading agreements and possibly in the development of a more competitive economy, hence, more trading partners. Conclusion and Recommendations Strengths and Limitations of the Study This research employs the use of the Gravity Model, as well as the Social Network Analysis (SNA) to quantify and explore the trade and FDI links within the Turkic States. Perhaps, one of the key advantages of this research is the methodological approach based on the quantitative analysis using the Gravity Model, combined with qualitative analysis using SNA. The use of this broader perspective allows distinguishing between more economic and noneconomic factors of trade and investments in the Turkic states. The use of data for twenty years from 2000 to 2021 makes the conclusions generalizable enough to include all the longrun, oscillations, and tendencies in trade and FDI. The reliability and accuracy of the data is further enhanced through the use of sources that are widely recognized and used in similar studies, including the World Bank, UNCOMTRADE, and the Coordinated Direct Investment Survey (CDIS). No less important is the fact that the use of dummy variables for shared borders and languages when analyzing the trade in goods and services in the framework of the Gravity Model expands the list of factors that allow taking into account cultural and geographical similarities that are ignored in economic models. However, the study is not without the possible limitations as are highlighted below. A major limitation is that, a number of missing data have had to be estimated for purposes of the analysis; this includes the trade volume between Turkmenistan and Uzbekistan from the year 2010 to 2019 as well as the GDP for Turkmenistan in the year 2020 and 2021. Even though using the historical trends helps to estimate the future churn rate rather accurately, it does not take into consideration any specific economical events, which can happen during the time, as well as changes in the structure of the whole economy. This reliance on extrapolated data adds to a certain smoothness or variability to the analysis which may distort the results. One major weakness relates to the problem of endogeneity, which might lead to the inclusion of only a few variables while excluding others that are actually relevant. Even when scholars include numerous measures of influence in an attempt at including all possible explanations for trade and FDI within the models, there can still be other factors that are not measurable or observable that may be affecting the results in question. Such qualitative changes may include political risk, quality of regulation and other forms of non-tariff barriers to trade which though highly influential over the flow of trade and investment statistics can be difficult to measure and model. Also, having no access to the latest dataset of the Turkic States and the time range of the data collected is limited up to the early years of 2021. Key factors such as the COVID-19 virus and consequent economic shifts may have influenced the overall structure of the international economic environment and its implications for trade and FDI. First, its may not capture the details of recent development as it mainly covers up to 2009, which might be useful for generating an accurate prognosis of current or future economic conditions. As it has been pointed out, economic processes such as changes in supply relationships and global trade, shifts in trade policies, or variations in rates of economic recovery can dramatically affect trade and FDI. Analyzing the post-crisis trajectory of the countries’ interaction with partners is impossible since the data is available only up to 2021, and changes in global trade and investment resulting from the COVID-19 pandemic cannot be considered here. In the same way, the pandemic ushered significant changes in various nations where countries have gone through large scale economic contractions, global shifts in trade policies practices among others. These could have drastically affected trade and investment and unfortunately the available data to cover this kind of analysis does not range from this period. In addition, the constantly changing global trade and investments indicate that there might have been new trends and patterns that have developed since 2021 and were not captured in the study. For example, digital trade, shift in the existing trade relations, and development of new technologies in the processes of supply chain and logistics may have an impact on trade and FDI flows within Turkic States. Summary of Key Findings The same Gravity Model analysis reveals that the economic mass of both the exporter and importer as stated by their Gross Domestic Products is a major factor in trade. The signs of the coefficients are consistent with expectations and prior theoretical work because it can be hypothesized that economic size associated with greater GDP values equal greater amounts of trade. This link implies that the rich countries are in a position to produce and consume more and hence, have higher export and import rates. The results provide a nod in the direction of the gravity model hypothesis that holds that economic mass is positively related to flow. It could be observed that geographical distance has a negative influence on trade flows, which reflects in the negative standardized coefficients of all the variables incorporated within the model. This finding is in line with the hypothesis of theory of gravity which states that trade reduces with increase in distance due to increased cost of transporting products and other related challenges. The Turkic States are already neighbors although some are still experiencing problems of infrastructure and transport which may also affect trade. The adverse effect of distance is evident from the findings, thus calling for better infrastructure to enhance the efficiency of transportation and logistics for trade. The cross-country analysis shows that the overall population also has a positive impact on trade but for the exporter nation, the impact is more significant. Larger population volume in the countries-exporters has its positive effect on production and export capabilities, which is explained by the more significant number of workers and resources. This can be observed through comparing countries such as Türkiye and Kazakhstan wherein the increase of population in respective countries results in higher levels of production output leading to even higher exports. Consequently, for import and export activities the coefficients vary to the extent of impacts to the trade flows, exported activities exert greater influence on the trade. This means that those countries with higher exports realize higher total trade since export has significant relation with the total level of trade. Exporting has the positive effect of making the economy to grow through revenue generation and stimulation of industrialization. The emphasis laid on export activities fits into the context of the economic development strategies adopted by many of the Turkic States, which have attempted to increase their export capabilities in order to increase economic effectiveness. Moreover, In particular the SNA of FDI reveals that Türkiye and Kazakhstan occupy a strategic place in the network of Turkic investments. They were described to have higher centrality and eigenvector centrality and therefore, these countries are important not only as host countries for FDI but also as shapers of the investment flows within the region. Türkiye has a unique geostrategic location that connects two continents, Europe and Asia and a diverse economy thus offering investment opportunities. Likewise, as a nation abundantly furnished with natural resources and key economic investment prospects Kazakhstan shall be significant at the regional scale. The high eigenvector centrality reveals the fact that Azerbaijan is in an advantageous position in VIEW OF attracting investments from other important investors. Exploration and production of oil and gas, as well as regional linkage, has made the country’s FDI an important consideration eligible for investment. The further policies and the strategic cooperation appropriately signify the country’s favorable investment climate. As Azerbaijan occupies the central place in the cluster, the country should be able to use this advantageous position to attract more diversified investments, sparking further development processes in the economy. The centrality measures help to compare the strategic position of each particular country in the regional economic system. For instance, due to their high centrality, Türkiye and Kazakhstan experienced better connectivity from the global investors and decreased cost and challenges of investment. In this context, these countries should use their positions to advance the process of economic cooperation in the region and stimulate its development. On the same note, it is evident that some countries like Turkmenistan are less centrally located and, therefore, require ensuring proper positions for themselves. This may require measures to develop logistic and regulatory means of supporting trade, developing infrastructures favorable for trade, and participating in regional cooperation in economic activities. Through better linking with more arcane countries, they can raise their appeal to overseas investors and improve their general co-ordination. Implications for Trade Development Policies The first of the resultant points of future discussions that arises from this research relates to the necessity of increasing the economic interaction of the Turkic States. According to the estimation results from the Gravity Model the endogenous variable of economic size, or GDP has a significant impact on trade flows. This means that, in the process of endeavouring to realise national economic development objectives in individual countries, there will be beneficial repercussions on cross-border trade in the region. Subsequently, regional relations strengthening that can be manifested through collective economic activities, including investment in infrastructures, technologies, personnel, as well as education, will positively contribute to improving economic performance in this region. Further, the perceived SNA results show that Türkiye and Kazakhstan are prominent in the regional investment network. These countries can make a difference by leading the regional economic cooperation processes succesfully in the region. Thus, it lies within Türkiye and Kazakhstan’s capabilities to support trade initiatives and engage in bilateral and multilateral trade negotiations for the creation of free trade zones and furthering of trade relations and investments within their regions. Such initiatives would not only support their own GDPs, but also help to establish a more sustainable and connected regional economy all around. According to the findings of the Gravity Model analysis on trade the geographical distance stands out as a major barrier to trade thus calling for the need to reduce any physical hindrances to trade. Transport costs and transit factors are serious sources of inefficiency within trade for nations such as Kazakhstan and Uzbekistan, the former of which is a landlocked nation. Thus, investment in the public transport sector – road and rail networks, ports – to facilitate integration within the Turkic States region should remain a priority. In addition, the modem practices in the logistics and supply chain man may improve the speed of trade. It means efficient supply chain executions through technologies like blockchain for supply chain tracking, mechanisms for automated customs compliance, and logistics connections can help in reduction of burden, cost, and enhancement in reliability of trade channels. Minimizing the inefficiency of trade thus requires regional cooperation in adopting and developing these technologies in this region effectively. Based on the findings of the Gravity Model that highlighted the importance of shared borders in determining trade volumes, it was deemed appropriate to advance the proposition that the formation of Regional Trade Agreements (RTA) among the Turkic States would significantly enhance the volume of trade among member countries. In a nutshell, for any two countries to join forces in an RTA they can bring about lower tariffs, no non-tariff barriers and trade regulations can be made uniform which makes it much easier for global business operations. To be effective, they require legal cooperation between countries to provide incentive for nations to follow centralized policies. Türkiye and Kazakhstan being the countries with high centrality in case of the trade network can play a key role in the initiation and promotion of the RTAs. These countries can therefore lobby for green economy and liberalisation when concluding trade agreements to consider measures of investment protection, property rights, and measures of dispute resolution. In this way, since the RTAs provide the stable and predictable trading environment, they can attract higher share of foreign investment and drive the economic growth. The effect of export on trade flows revealed in the Gravity Model resuls indicates that there should be more emphasis on export diversification. Market conditions which affect exporter’s revenue take some countries hostage since they mainly rely on a few products which dominate their exports. As a result, coordinated policies to reduce dependency on single commodities and diversify export portfolios can contribute to improving the ground for further economic stability. Sustaining export diversification thus requires the Turkic States to consolidate on areas with potential for growth opportunity in manufactured products, technology, and services. Focusing on innovative solutions for SMEs – enhancing access, to finance, capacity building and tailored programs that can provide market access, can foster innovation and entrepreneurship. New export promotion agencies and participation in international trade fairs also provide the necessary support and information to companies to develop their export potential. The SNA results show that major centrality of countries such as Türkiye, Kazakhstan and Azerbaijan in the investment network provide them better connective advantage and opportunities for global investors. The countries should place emphasis on developing and maintaining a favorable environment that can encourage and sustain investment inflows. Facilitating bureaucracy, strengthening, and providing efficient and transparent regulatory mechanisms, ensuring private property rights, and operating with minimum regulation and restrictions. In addition, regulatory institutions such as investment promotion agencies have a great mandate in promoting the region as a favourable investment destination. These agencies can bring in investors into the strategic sectors by highlighting the important benefits that the region has such as natural resources for manufacturing, skilled labor, and the growing consumer markets. Moreover, one of the beneficial strategies is the granting of privileges to attract foreign investment, especially the legalization of incentives, subsidies, and guarantees. On its part, institutional quality proves to have an important influence in trade and investments. Such nations provide efficient protection of private property and resolve disputes, curb corruption while providing political stability which leads to increased inflow of FDI and ease of trade. In particular, the improvement of institutional factors remains a critical precondition for the enhancement of the environment for economic activities. Hence, m easures directed towards increasing the standards of governance, increasing the efficiency of operations within the public sector and making sure that organizational leaders are held accountable are some of the characteristics that can increase investor confidence and foster economic growth. Also, the partnerships with the private sector can improve the application of the infrastructure and various economic programs through private sector’s knowledge and capital. Reducing data deficits and improving forthcoming endeavors for research can be vital for policy initiatives. Many policies, government’s decisions and even attempts to forecast the economic conditions require adequate and timely data. The Turkic States should therefore invest in capacity development in the form of sound statistical frameworks and economic research institutions capable of gathering, analyzing and disbursing economic information. International organizations and other stakeholders like World Bank and the International Monetary Fund IMF can offer technical assistance and aid in the enhancement of the quality of data as well as research strategies. Also, supporting academic studies and independent analytic centers capable to undertake researches of the regional economy can produce useful information and contribute to the formation of the debates over the policies. Recommendations for Future Research In examining the Turkic States’ trade relations and FDI flows, the applied analytic models, namely the Gravity Model and SNA, have proven their worth in the understanding of the region’s economics. Nevertheless, the global trade and investment landscape is dynamic and sophisticated and, therefore, requires further examination of the subject in order to develop more effective policy intervention measures. By considering the conclusions and drawbacks of this research, potential recommendations for further research can be provided. Future study should give emphasis on the data collected later than the year 2021 in order to determine the current trends of economic changes and the effect of events such as the COVID-19 pandemic to the level of trade and FDI. In order to determine the source of trade and investment in each sector, particular attention should be given to industries of focus by revisiting these components of trade and investment balances . There is a need to a nalyze current trade agreements and especial RTAs to enhance understanding of impact these agreements have on economic activities and to identify improvements to policy settings. Subsequently, future study should include analyzing the political stability index, the quality of governance index, cultural similarities, and previous histories between countries to have a round understanding of the process of trading and investments. It is needed to ut ilize more sophisticated methods, such as data mining, graph theory, and data mining, to identify causal relationships or patterns not visible with basic methods, between trade and investment now and in the future. The gap to c ritically assess how digital platforms and online marketplaces are influencing trade, liberalization and market access to aid the Turkic States to promote the digital economy , is crucial. To be able to formulate plans and response measures of preventing the impacts of economic shocks, it is necessary to assess and determine the robustness of the Turkic States economy. 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