Foreign Direct Investment Network and Identification of the Best Investment Partners for Iran

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Economics Department, Yazd University, Yazd, Iran

2 Associate Professor of Economics, Economics Department, Faculty of Economics, Management, and Accounting, Yazd University, Yazd, Iran

3 Assistant Professor, Economics Department, Faculty of Economics, Management, and Accounting, Yazd University, Yazd, Iran

4 Professor of Economics, Economics Department, Faculty of Economics, Management, and Social Sciences, Shiraz University, Shiraz, Iran

Abstract
This study examined the global structure of foreign direct investment (FDI) networks using data from 193 countries for 2016 and 2021. The study analyzes the overall structure of FDI networks, countries’ centrality, and their positions within the international investment system. Furthermore, principal component analysis (PCA) was employed to construct a composite index for evaluating countries’ positions within the network. Based on this index, the analysis identified the most suitable potential investment partners and alternative partners, with particular emphasis on Iran. The results indicated a core–periphery structure in the global FDI network. The United States, the Netherlands, Luxembourg, Germany, the United Kingdom, France, Japan, Switzerland, Ireland, and Canada occupy central positions within the network and achieve high composite index scores, identifying them as the world’s leading investors. However, in the case of Iran, the constraints imposed by international sanctions and existing political relations necessitated a more context-specific approach. Accordingly, three scenarios were developed based on the severity of sanctions, with the aim of enabling Iran to leverage regional and global capacities to attract foreign investment and promote economic development.
Introduction
The formation and accumulation of capital are among the fundamental prerequisites for economic development. In general, investment is one of the most important factors contributing to the expansion of national production. A lack of capital, together with the need for new technologies and technical knowledge, has led developing countries to seek ways to attract foreign investment. In this context, foreign direct investment (FDI) is one of the most important sources of international finance. In addition to supplementing domestic capital, FDI can contribute to the transfer of technology and managerial expertise, as well as the development of human capital.
In recent decades, economic globalization and trade liberalization have intensified competition among developing countries to attract FDI. In this context, Iran enjoys considerable potential, including its strategic geographic location, abundant natural resources, and young and educated labor force. However, the country’s position within the global FDI network remains relatively weak.
The present study aimed to examine investment flows within the global FDI network in order to identify the countries that play the most significant roles in the expansion of international investment. Focusing on the case of Iran, the analysis sought to evaluate countries’ positions in the network and identify the most suitable investment partners and potential alternatives.
Materials and Methods
To construct an FDI network, this study used outward FDI data from 193 countries for the years 2016 and 2021. Network theory was used to analyze the relationships between countries. The analysis also examined the indicators related to the structure of the network, including the core–periphery structure. Then, principal component analysis (PCA) was employed to construct a composite index based on out-degree centrality, PageRank, and hub centrality, which helped identify the best investors. Finally, the cosine similarity method was applied to identify alternative investors.
Results and Discussion
The results for out-degree centrality indicated that the United States, the Netherlands, Luxembourg, Germany, and the United Kingdom recorded the highest out-degree values in both years. These findings revealed an asymmetric distribution of outbound power within the FDI network, whereby a limited number of countries control a substantial share of cross-border investment flows. This underscores their role as major capital exporters and key actors in the allocation of global financial resources. From an economic standpoint, the concentration of outbound power in these countries reflects their strong institutional superiority, deep financial markets, and the presence of large multinational corporations.
Regarding PageRank centrality, the Netherlands, Italy, China, the United States, and Thailand ranked among the top five countries in both years examined. As a measure of structural influence, PageRank identifies countries that occupy positions along the main pathways of capital circulation and are connected to highly influential nodes. The results indicated that a relatively stable group of countries maintained high rankings in both years, suggesting the persistence of influence patterns within the global investment network.
From an economic perspective, a high PageRank score indicates high-quality connections, meaning that a country is not only active in terms of investment flows but is also connected to countries with strong financial and technological capabilities. Such a position can facilitate greater knowledge spillovers, more stable access to financial resources, and deeper integration into global value chains. Therefore, PageRank centrality can be viewed as an indicator of connection quality within the network, complementing volume-based measures of network position.
With regard to hub centrality, Luxembourg, the United States, the Netherlands, Germany, and the United Kingdom exhibited the highest values. This index measures a country’s ability to connect with highly influential nodes in the network and reflects its role in shaping investment routes. According to the findings, the countries with advanced financial systems and prominent intermediary roles in the circulation of capital tend to record higher values for this indicator.
The results indicated a core–periphery structure in the FDI network. A network is characterized as core–periphery when a relatively small number of highly influential nodes account for a large share of transactions, while most other members have limited mutual connections and remain only sparsely linked to one another. This structure implies that the majority of network members rely primarily on the resources, financial capabilities, and attractiveness of a small number of core countries. The core of the network comprised 28 countries, most of which ranked highly in terms of out-degree centrality, PageRank, and eigenvector centrality. This finding demonstrated that the power structure of the global investment network is shaped by persistent institutional and structural factors. Furthermore, on the basis of a composite index, the top 20 investor countries and alternative countries were identified. With regard to Iran, three scenarios were considered based on the continuation, easing, or intensification of sanctions.
Conclusion
FDI is one of the key sources of financing for economic growth. It generates a wide range of spillover effects across various dimensions, including technological advancement, the adoption of modern management practices, and productive capital formation. Moreover, the FDI network exhibits a core–periphery structure. To enhance their FDI performance, countries should strengthen their relationships with the world’s leading investors, including the United States, the Netherlands, Luxembourg, Germany, the United Kingdom, France, Japan, Switzerland, Ireland, and Canada. These countries exhibit both high investment volumes and high-quality investment linkages, maintaining strong connections with other countries that themselves occupy favorable positions within the network. Furthermore, given the sanctions imposed on Iran, there are three alternative investment scenarios reflecting different assumptions regarding the continuation, easing, or intensification of sanctions.

Keywords

Subjects

Abdollahi Arani, M. & Monsef, A. (2021). Simultaneous effects of economic growth, foreign direct investment, and government policies in the Iranian economy. The Journal of Economic Studies and Policies, 8(1), 275–304.
Abedi Ghahi, A., Owjimehr, S. & Samadi, A.H. (2025). Trade agreements, complex network and foreign direct investment: an application of a three-dimensional panel model. International Journal of New Political Economy, 6(1), 83-112. doi: 10.48308/jep.2025.237391.1191
Abedi Ghahi, A., Owjimehr, S., Samadi, A.H. & Rostamzadeh, P. (2023). Analysis of the network of foreign direct investment inflows. Journal of Economic Modeling Research, 14(51), 180-215. dor: 20.1001.1.22286454.1402.14.51.6.1 [In Persian]
Abedi Ghahi, A., Owjimehr, S., Samadi, A.H. & Rostamzadeh, P. (2024). The analysis of FDI of limited connectivity country in a complex network: a case study of Iran. Journal of Iranian Economic Review. doi: 10.22059/ier.2024.369266.1007878
Adarov, A. (2021). Interactions between global value chains and foreign direct investment: a network approach (No. 204). wiiw Working Paper
Alavinasab, S.M. (2013). Determinants of foreign direct investment in Iran. International Journal of Academic Research in Business and Social Sciences, 3(2), 258–269.
Amini, A.R., Rismanchi, H. & Farhadikia, A.R. (2010). Analyzing the role of foreign direct investment (FDI) in improving total factor productivity (TFP); A Cross-Country Panel Data Analysis. Iranian Quarterly Journal of Economic Research, 14(43), 55-80. https://ijer.atu.ac.ir/article_3447_2e1b24a664f5e9c18f407b2f9c73e821.pdf [In Persian]
Arif, A., An, P., Qi, Y., Li, H., An, H., Hussain, M. & Wang, Y. (2021). The influence factors of the national roles in the FDI network: A combined method of complex networks and panel data analysis. Physica A: Statistical Mechanics and its Applications, 563, 125311. https://doi.org/10.1016/j.physa.2020.125311
Barabasi, A.L. (2016). Network science. Cambridge University Press.
https://doi.org/10.1063/PT.3.3526.
Blonigen, B.A. (2005). A review of the empirical literature on FDI determinants. Atlantic Economic Journal, 33(4), 383-403. doi: 10.1007/s11293-005-2868-9.
Bolívar, L.M., Casanueva, C. & Castro, I. (2019). Global foreign direct investment: A network perspective. International Business Review. https://doi.org/10.1016/j.ibusrev.2019.01.007.
Borgatti, S.P. & Everett, M.G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375-395. https://doi.org/10.1016/S0378-8733(99)00019-2.
Borughni, M., Mirkarimi, S.H., Mohammadzadeh, M. & Saeedi. S. (2021). Principal component analysis in identifying the most important criteria affecting the priority of walking trails in the Ziarat watershed. Environmental Research, 12(23), 81-90. ‎[In Persian]
De Benedictis, L. & Tajoli, L. (2011). The world trade network. The World Economy, 34(8), 1417-1454. https://doi.org/10.1111/j.1467-9701.2011.01360.x
Dunning, J.H. (1988). The eclectic paradigm of international production: A restatement and some possible extensions.  Journal of International Business Studies, 19,1– 31.
Ghahroudi, M.R. & Chong, L.C. (2020). The macroeconomic determinants and the impact of sanctions on FDI in Iran. Economics and Business, 34, 15-34. https://doi.org/10.2478/eb-2020-0002.
Jackson, M.O. (2010). Social and economic networks. Princeton university press. https://doi.org/10.2307/j.ctvcm4gh1.
Jolliffe, I.T. (2002). Principal component analysis. New York, United States of America: Springer. https://doi.org/10.1007/b98835
Jones, G. (2005). Multinationals and global capitalism: From the nineteenth to the twenty first century. Oxford University Press. https://doi.org/10.1093/0199272093.001.0001
Komijani, A. & Ghaividel, S. (2006). The spillover effects of foreign direct investment on skilled and unskilled employment in Iran’s services sector. Economic Research Journal, 41(5). [In Persian]
León, C. (2013). Authority centrality and hub centrality as metrics of systemic importance of financial market infrastructures. Available at SSRN 2290271.
Li, B., Shi, Z. & Guo, W. (2024). The global foreign direct investment network: evolution and influencing factors. Kybernetes, 53(3), 841-861. https://doi.org/10.1108/K-05-2022-077.4
Lima, F., Pinheiro, F., Silva, J.F. & Matos, P. (2020). Foreign direct investment–using network analysis to understand the position of Portugal in a global FDI network. IFC Bulletin, 52.
Manning, C.D., Raghavan, P. & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071.
Marandu, E.E. & Ditshweu, T. (2018). An overview of the key theories of foreign direct investment: The way forward. Advances in Social Sciences Research Journal, 5(12), 30-39. https://doi.org/10.14738/assrj.512.5741.
Matlabi, S.M.M., Mortazavifar, Z., Ranjbaraki, A. & Shakiba, A. (2025). Analysis of the biggest obstacles to business and investment in Iran. Iranian Quarterly Journal of Applied Economic Studies, 13(14), 9-34. https://dx.doi.org/ 10.22084/aes.2025.30611.3773. [In Persian].
Mohaddes, F. (2011). Calculating the asset price index and examining its effect on inflation. Quarterly Journal of Economic Research Trends, 19(60), 29-62 .[In Persian].
Naeini, H., Jafari Seresht, D. & Eissazadeh, S. (2024). The Impact of bilateral investment treaties on foreign investment inflows of development countries into Iran. Iranian Journal of Applied Economic Studies, 13(51), 145–170.
Newman, M. (2010). Networks. Oxford university press. http://dx.doi.org/10.1093/
Popovici, O.C. & Călin, A.C. (2014). FDI theories. A location-based approach. Romanian Economic Journal, 17(53), 3-24.
Rafat, M. (2018). The effects of trade integration, globalization and foreign direct investments on employment in Iranian manufacturing sector. Iranian Economic Review, 22(3), 815–835. https://doi.org/ 10.22059/ier.2018.66645
Rubinov, M., Ypma, R.J.F., Watson, C. & Bullmore, E.T. (2015). Weighted network measures of functional connectivity. NeuroImage, 117, 1–13.
Sajedian-Fard, N. & Hadian, E. (2021). Appropriate trade partners for Iran in the import of machinery, equipment, and transportation equipment: A network theory approach. Journal of Economic Modeling, 11(4), 27–56. https://doi.org/10.29252/jem.2021.220645.1576. [In Persian]
Sultana, N. & Turkina, E. (2020). Foreign direct investment, technological advancement, and absorptive capacity: A network analysis. International Business Review, 29(2), 101668. DOI: 10.1016/j.ibusrev.2020.101668
Unctad.org
Wasserman, S. & Faust, K. (1994). Social network analysis: methods and applications. Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511815478.
Yavari, K. & Saadta, R. (2005). The Impact of Foreign Investment on Iran's Economic Growth. Journal of Religion and Life, 27(7), 23-40. [In Persian].

  • Receive Date 18 November 2025
  • Revise Date 25 September 2026
  • Accept Date 29 June 2026
  • First Publish Date 29 June 2026