The interconnectedness of global trade influences international relations. The complexities of this interconnectedness may be better understood through quantitative analysis of the balance of trade within the global economic system. Economics is an instrument of national power affecting international relations. Transnational trade relationships offer both risk and opportunity in the pursuit of national objectives. Globalization has increased the interconnectedness of national economies around the world. This interconnectedness potentially enables international coalition building based upon common economic interest. Likewise, such interconnectedness potentially deters, moderates, and constrains nations from actions, which would negatively affect their economy. Analysis of such relationships presents decision makers both risk and opportunities in terms of international support, influence and the potential success of economic coercion.
This paper serves as a proof-of-concept testing analytic tools for better understanding the efficacy and consequences of economic influence in terms of sanctions and other similar macroeconomic regimes. The underlying concept developed is a calculation of economic threat rings describing the propensity and utility of countries to participate in such regimes. Economic threat rings are bounded by the distance across each of the dimensions of international trade between the country imposing sanctions and the targeted country. For any given set of countries, the economies of countries inside the ring are more at risk to sanctions than those outside the ring. Any countries inside the ring are, therefore, rationally less likely to support such sanctions. The existence of national economies inside such rings shows that sanctions are an imprecise weapon. Sanctions necessarily require countries other than the targeted country to accept losses in the global economy in order for sanctions to be effective. When such loses are suffered by countries un-desirous of supporting sanctions for political, economic or other reasons then the prospect for influencing the targeted country is reduced.
In this paper the economic threat ring technique is applied to the case of modern day Iran. Iran is used as a case study as it has a long standing record of sanctions being imposed upon it by the United States and others since its 1979 revolution. Iran continues to be of contemporary interest in American foreign policy owing to concerns with respect to its known sponsorship of terrorism and suspected pursuit of nuclear weapons. Based on analysis of the results, conclusions are drawn with respect to the risk opportunities present to American decision makers in terms of international support and the potential success of economic coercion targeted against Iran. Statistical analysis of publicly available balance of trade data for Iran and its interconnected network of related global trading partners are central to this study.
Defining and Classifying the Problem
The history of both unilateral and multilateral sanctions is one wrought with very limited success in modifying the behavior of targeted countries. Countries such as Cuba, Sudan, Libya, North Korea, Iraq and Iran have faced sanctions for decades. In Cuba, the Castro government has maintained power and sustained its repressive regime off the coast of the United States even after the fall of the Soviet Union in 1989. Sudan has continued to this day to be a safe-haven for terrorism and criminals while engaging in government sponsored genocide in Darfur. Libya has exhibited some behavioral change relative to its sponsorship of global terrorism; however, not until after air strikes by American forces. North Korea developed nuclear weapons under sanctions. In retrospect, we now know that Iraq may have essentially complied with sanctions following the Gulf War; however, their behavior and rhetoric still led to violent conflict in the form of Operation Iraqi Freedom. Iran continues to be a sponsor of terrorism, particularly Hezballah, and is suspected to be developing nuclear weapons in its nuclear energy program. Iran has continuously obstructed oversight of its nuclear program by international organizations such as the International Atomic Energy Agency.
This brief history of sanctions shows that the prospect for successful sanctions is grim; however, these examples do not answer the question of why sanctions are generally ineffective. Most importantly it is necessary to understand the complexity of the global economic system. By understanding the complexity of this system, it is then possible to select analytical techniques which are best suited to its examination. Having identified such techniques it is then possible to evaluate relationships in the global economic system and quantify the economic risk to countries considering participation in sanctions upon any other country.
It should also be noted that sanctions are generally not applied to all commodities in the global economic system. Frequently, medical and food aid and trade are excluded from sanctions for humanitarian reasons. Such exclusions will be detailed later in this paper utilizing the Iran case study. In general, however, this means that the degree to which sanctions affect a sanctioning country as well as the sanctioned country depends on the type of commodities which are involved in their respective trade arrangements. The commodity based perspective of sanctions adds to the complexity of global economic trade.
While it is commonly accepted that global trade is a complex system, there is utility in formally defining the nature of its complexity. There are many models available to describe complex problems. We apply the model defined by Axelrod in his book Harnessing Complexity. The reason Axelrod’s model was selected is that his goal is not simply to describe complexity, but rather go further into understanding opportunities to take advantage of complexity. In the context of this research, Axelrod’s model helps to clearly articulate the class of problem presented by economic coercion on a transnational scale which then enables the selection of appropriate methods to examine such problems.
Axelrod defines a complex system using the following model. Complex systems, such as global trade, involve a large number of agents (or countries in this study). Agents form a population; in this study the population shall be the countries found relevant to understanding Iranian trade relations relative to international economic coercion. Material resources used by agents are known as artifacts which are trade goods in this study. And, patterned behavior by agents is known as a strategy, or patterns of trade in this study, which are based on bilateral and multilateral agreements as well as market forces. All of these elements form a system, in this case the global economic system.
This system, using Axelrod’s model, is particularly complex owing to the number and diversity of interactions between agents. It is bounded by geography (or what Axelrod calls physical space) as well as trading blocs, agreements and other non-physical influences (which Axelrod calls conceptual space). Economic coercion relates directly to modifying what Axelrod calls selection, related to the frequency with which agents implement certain strategies relative to other agents or types of agents. Strategies, for example, may include most favored nation trade status (reward) as well as sanctions, boycotts and blockades (coercion). And, finally, Axelrod argues that agents employ success criterion. While success criterion related to global trade generally may include increased trade revenue, Foreign Direct Investment, growth in Gross Domestic Product and so on; in this study, the criteria shall be the degree to which the ends of economic coercion strategies are realized. So, for example, if the reason sanctions are applied economically is human rights, proliferation of weapons of mass destruction and/or other such issues; then success should be measured in terms of positive change on these issues from the perspective of the country(s) engaging in economic coercion.
Formally defining international trade as a complex system and economic coercion as a bridge between international relations and economic strategies of countries adds context to this problem. More importantly, it describes the class of problem studied in this paper and as a direct consequence the class of solution techniques which are applicable to the analysis of this problem. Multi-Dimensional Scaling (MDS), described in the methodology section, is a solution technique well suited to complex problems where the underlying patterns of behavior are both complex and ambiguous.
Economic threat rings will be calculated using non-metric Multi-Dimensional Scaling (MDS) because it is a technique which yields a graphic representation of the proportional closeness and relative orientation of objects (countries in this study) based on data where understanding the underlying dimensionality is complex and ambiguous (global trade and its relationship to international relations in this study). MDS “provides a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects.” If N is the number of countries studied, MDS requires an NxN matrix, A, with elements aij representing trade between countries i and j in N. This matrix is called the similarity matrix.
MDS also requires a stress function that measures “the degree of correspondence between distances [or similarities].” The Kruskal stress function is commonly used in MDS and defined as: ((∑i∑j aij - dij)/( ∑i∑j dij2))1/2 where dij is the Euclidean distance between points i and j based on the coordinates assigned.
Using a MDS algorithm, it is possible to graphically plot the coordinates of nations in the global economy for every two dimensional projection of distance. Each dimension corresponds to a mathematically unique aspect of the data under investigation. Those nations that are closer to each other are, based on the theory of this technique, closer economically in the context of the measure applied.
MDS does not and cannot explain or label what these aspects are in a non-mathematical context. Therefore, relationships found of interest in MDS results must be explored through additional research in terms of the context (international trade in this study) to determine the practical nature of the relationship.
To analyze U.S. sanctions on Iran, economic threat rings are drawn with Iran at the center and the radius defined by the distance between Iran and the United States for each dimension of the trade relationship found statistically significant in the MDS analysis. The radius of this ring represents the degree to which the United States is connected to Iran’s economy both directly and indirectly. Countries inside the ring have greater economic connectedness than the United States for a given dimension making them simultaneously more capable and less likely to participate in coercive economic actions owing to the potential impact on their own economy. Countries outside of the ring have less economic connectedness than the United States making them simultaneously less capable and more likely to participate in coercive economic actions. Likewise, leaders of countries inside the ring are accepting greater political risk than those outside of the ring relative to the risk American leaders are accepting.
Risk in this context has two main components which are interconnected. First, there is economic risk. Economic risk is the quantifiable potential for losses to the economy of countries imposing sanctions. This includes losses as a direct result of imports and exports to or from the sanctioned country as well as losses from lessened supply and demand in other countries commonly trading with sanctioned country. Second, and closely tied to economic risk, is political risk. Leaders who make decisions, perhaps for ideological motivations or reasons related to foreign relations, who cause damage to the economic health of their own country risk their ability to maintain power. Depending on the political systems and traditions of such countries, they risk anything from losing reelection to civil unrest and insurrection.
The paradox between capability and likelihood shall be examined in terms of opportunities and risk. The results of this examination are then qualitatively compared to positions, strategies, and actions taken by statistically significant countries with respect to Iran. The correlation between the analysis of statistical results and respective national policies and behaviors shall serve as evidence either confirming or denying the hypothesis that trade relationships constrain international relations within the scope of this study.
It should be noted that there is an inherent potential bias in this analysis of balance of trade data as for any given year the effects of such sanctions both explicit and implicit may be causal in the relations described by such economic threat rings. Explicitly, as will be discussed later, many countries choose largely not to participate in US sanctions on Iran making them nearly unilateral in nature. This adds some degree of confidence to the selection of Iran as a case study; however, the implicit resulting hostility towards Iran by countries friendly to the United States is more difficult to estimate. As data regarding such implicit influence on decision making is difficult if not impossible to collect, this effect shall be treated as a source of potential error rather than as an explicit factor in quantitative analysis.
Data selected for use is the percentage of a country’s imports and exports to or from another country bilaterally. Percentages are used as the monetary value of trade varies drastically depending on the countries examined; however, percentages are understood to represent the relative significance of the bilateral trade relationship to a country’s economy. Separate datasets were developed for imports and exports, respectively.
In examining data available from the Central Intelligence Agency’s (CIA) World Factbook, United Nations (UN), International Monetary Fund (IMF), World Bank (WB) and World Trade Organization (WTO); the CIA’s data was found to be best structured for this purpose.,,,,, In all of these databases, the most current, complete dataset was for the calendar year 2007.
First, Iran’s most significant trading partners were included. Iran’s major export partners are China, Japan, Turkey, South Korea, and Italy. Iran’s major import partners are China, Germany, United Arab Emirates, South Korea, Russia, and Italy. Second, the major trading partners of these countries were included. These secondary relationships are included to capture potential triangular trade relationships where goods are transshipped via an intermediate country. Such triangular trade is both a common practice in international trade as well as a means of avoiding trade barriers, sanctions and other coercive economic measures.
Third, conscious effort was made to include the five permanent members (P-5) of the United Nations Security Council (UNSC), the Group of 8 (G8), and members and observers of the Shanghai Cooperation Organization (SCO). The P-5 (China, France, Russia, United Kingdom, and United States) were included as the P-5 are the key decision makers in the application of international sanctions and other similar coercive measures. The G8 (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, and United States) was included as this organization represents leading global economies. Note that the G8 includes the Group of 7 (G7) with the addition of Russia. Members and observers of the SCO (China, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, India, Iran, Mongolia, and Pakistan) were included as the SCO represents opportunity for Iran to avoid Western coercion by looking Eastward and because Iran presently has observer status in this economic and security cooperation organization. In cases where countries are members of more than one of these categories or organizations, that country is only represented once in the respective dataset. Fourth, any remaining major trading partners of any of the countries included thus far and cited in the CIA data were included for completeness.
Larger organizations such as the European Union (EU), Organization of Petroleum Exporting Countries (OPEC), WTO, UN, and so on were not explicitly included as the resultant dataset would be extravagantly large and not necessarily strongly tied to the economic coercion of Iran. Relevant countries included in the data used here, however, are members of some or all of these larger organizations. Therefore, the role of these larger organizations as applied to the case of Iran, while not explicitly included in the data, is included implicitly in the data and shall be discussed in terms of analysis and conclusions with respect to this data.
This selection process resulted in a total of 35 countries in the imports dataset and 37 countries in the exports dataset. Datasets, including lists of countries and their respective trade data, are provided in the Appendix. Countries are listed by their two letter country code defined by the International Organization for Standardization. Note that totals of the percentages in the datasets need not necessarily sum to 100% owing to the selection process described above. While the datasets are somewhat sparse, they are a non-random sample representing a population bounded only by the selection process defined above. Both datasets are of sufficient size to conduct statistical analysis with respect to this case study of Iran.
As previously noted, these datasets do not contain factors indicating past or present participation, explicit or implicit, in economic coercion of Iran. Future studies expanding this methodology beyond a proof-of-concept should consider time series analysis of available data spanning the period prior to the application of sanctions to the present. For this case study, such effects are treated as a potential source of error analytically and shall be addressed in the contextual analysis of results with respect to the policy position of countries and organizations found significant in the following analysis. Such analysis is not needed to test whether economic threat rings as defined in this study have a correlation to related international policy towards Iran. Such further analysis would only be required for detailed analysis of the efficacy of such policies over time which is beyond the scope of this research.
Using the import and export data described above it is possible to apply non-metric MDS to these samples. As noted in the methodology discussion, it is necessary to select bounds on stress when using MDS. The goal selected for this study is to reduce the stress to at or below 0.10 (or 10 percent). This level of stress represents 10 percent or less distortion of the underlying data. Stress is reduced by increasing the dimensionality, p, in the MDS algorithm. As the input and export data is already single dimensional, the options for p start at 2 and would never be greater than the total number of countries, N, in the dataset.
Stress levels as dimensionality increases for the import and export datasets are provided below. Stress was calculated by entering the matrix datasets for imports and exports into the software package UCINET and running the non-metric MDS algorithm while sequentially increasing the dimensionality from 2 to 10 dimensions.
From this data, it can be observed that the exports dataset preformed slightly better than the imports dataset in terms of stress. This is most likely owing to the fact that the exports dataset is slightly larger than the imports dataset and, thus, has more information regarding the underlying system. To confirm empirically that the underlying data is non-metric, metric MDS was run on both datasets. Even at 10 dimensions, the stress for the exports dataset was 0.131 and the stress for the imports dataset was 0.133. Both of these stress levels are above the threshold selected for this study of 0.10 and high dimensionally with high stress is an indicator of non-metric underlying data (i.e., significant lack of fit to a metric space as previously defined).
To conduct further analysis, it is necessary to determine which dimensional representation to use. Stress is reduced mathematically as the dimensionality increases. This may represent properties of the data, may be artificial, or a combination of both. Therefore, a theoretical rather than mathematical reason should be the basis for this decision. For example, if the data we were studying were known to be the physical volume of rectangular buildings, then we would know that the expected dimensionality is three (representing some projection of length, width and height).
In this problem we do not know with certainty the underlying dimensionality. We chose to use the three dimensional representation for a number of reasons. First, triangular trade, as already described, is common and such trade would have at least three dimensions. Second, the stress for both imports and exports in three dimensions is well below 0.10 indicating very little distortion. Third, the same dimensionality should be used for both imports and exports as one country’s exports are another country’s imports and vice versa. Fourth, this selection is conservative in terms of mitigating any mathematical artificiality in the results caused by over specifying the dimensionality. Fifth, while it will require three two-dimensional graphs per dataset, it is possible to visually assess the resulting graphical representations of the three dimensional solutions, which is consistent with the methodology used in this study.
Now that the three dimensional solution has been identified as the most appropriate for use in this study, it is necessary to produce the mappings of the imports and exports by country and overlay the economic threat rings as previously defined extending from Iran to the United States in each of the two dimensional projections of the data.
The following three graphs represent the imports dataset where proximity of one country to another in each dimension is related to similarities in its import trade relationships with other countries. Note that these similarities result from the complex interactions of bilateral trade relationships as a percentage of the volume of trade related to the countries total trade. This nuance allows for a much more robust analysis of economic influence than merely examining the first order bilateral relationships between countries and their closest trading partners.
The axes are unitless in these graphs as the distance has no absolute scale and relative closeness of one country to another should only be interpreted in a context relative to the closeness of other countries in the graph. The orientation of the graph has no significance and any rotation of the graph is equally valuable as it is a conceptual representation of the data rather than a physical representation. As discussed previously, economic threat rings were overlaid on top of the MDS graphs by centering a circle on Iran (IR) with a radius equal to the distance from Iran to the U.S.
In the two dimensional projection in Figure 1, it can be observed that the majority of the countries in the data have closer ties to Iran (IR) than the United States (US). Additionally, Iran appears to be relatively isolated from most countries and particularly western powers. The closest countries to Iran are Pakistan (PK), Belarus (BY), and United Arab Emirates (AE). Iran imports 9.1% of its total imports from the United Arab Emirates; however, has no significant imports from Pakistan or Belarus in the underlying data. Pakistan’s closeness can be explained as it exports 10.4 percent of its total exports to the United Arab Emirates. The relationship to Belarus is even more complex to understand and can be explained by its close trading ties with both Germany and Russia, as Germany accounts for 9.6 percent and Russia for 5.7 percent of Iran’s total imports. Germany (DE), Russia (RU) and Italy (IT), who all account for significant percentages of Iran’s total imports, are next in terms of closeness along with Saudi Arabia (SA). The position of Saudi Arabia can be explained by its strong relationship with the United Arab Emirates.
Conducting this type of analysis for every two dimensional projection of both datasets will allow for the identification of the key actors in terms of economic influence on Iran. As expected and based on the theoretical foundation of this methodology, it is possible to visualize the relative significance of triangular and other complex trade patterns between countries. This more robust understanding of economic interest provides a clearer picture of economies at risk in terms of coercing Iran.
To complete our examination of the MDS results from the imports dataset, similar analysis of the remaining two projections are required. Next, the projection of the first and third dimensions are considered.
In Figure 2, as in Figure 1, the relationship between the U.S. and Iran is distant. In this projection, Uzbekistan (UZ) is the closest to Iran. This can be explained by both Iran’s and Uzbekistan’s close trading ties to Russia noting Russia’s proximity to both Iran and Uzbekistan in the graph. Notably, other members and observers in the SCO next surround Iran. From the top right around to the bottom right are: Tajikistan (TJ), Kazakhstan (KZ), Kyrgyzstan (KG), India (IN), China (CN), Pakistan (PK), and Mongolia (MN). At about the same distance, as the SCO members and observers, and similar to Figure 1, are Belarus and United Arab Emirates. In this projection, Iran’s major trading partner South Korea (KR), accounting for 6.3 percent of Iran’s total imports, also appears at about the same distance as the SCO members and observers.
For completeness, the final graph to analyze in terms of imports depicts the second and third dimensions.
Figure 3, like Figure 1 and 2, again shows the significant distance between the United States and Iran; however, unlike previous projections demonstrates ties to EU countries, particularly Italy (IT) and France (FR) and to a lesser degree Spain (ES), Belgium (BE), Germany (DE) and the United Kingdom (GB). Even the Netherlands (NL) is relatively close. Iranian ties to both Italy and Germany have already been discussed. This projection can be explained by the interconnectedness of EU economies as would be expected given the formal structure of the EU with respect to trade, finance and economics.
Overall, the analysis of the imports data suggests that the U.S. has very little unilateral capacity to influence Iran economically. The United States has a higher potential of negatively influencing both U.S. economic partners and competitors globally, if sanctions were successful, as the economies of such countries are closer to that of Iran than that of the United States. These circumstances and their potential suggest that such an action by the U.S. would have significant political consequences. Conversely, nothing in the results for imports suggested a strong interconnection between the American and Iranian economy which means that such economic actions aimed at Iran would be unlikely to have a significant adverse economic impact on the U.S. MDS has demonstrated the greatest levers economically lie with the SCO and EU. Iran has managed to put itself in a strong economic position by developing a relationship with both the SCO and EU, as the SCO and EU are themselves natural competitors economically. Before making any final conclusions; however, it is necessary to conduct similar analysis of the exports dataset.
The data describing Iranian exports shall by examined in the same manner as Iranian imports using MDS.
Figure 4 shows the first two dimensions of the export MDS results. In this figure, Asian and Eurasian countries are closer to Iran than the U.S. with Kyrgyzstan and Taiwan (TW) closest to Iran. Taiwan is drawn in as a consequence of its close ties with other Asian economies such as China, Hong Kong (HK), Japan (JP), South Korea and Singapore (SG), all well inside the threat ring. China accounts for 15 percent, Japan 14.3 percent and South Korea 7.3 percent of Iran’s total exports, predominately consisting of petroleum resources. Kyrgyzstan, as before, is close to Iran as a result of the strong SCO representation inside the threat ring.
Figure 5, representing the first and third dimension for exports, continue to demonstrate the same distant relationship between the United States and Iran. The closest countries to Iran are the United Arab Emirates (whose relationship has already been discussed in relation to the imports MDS results) and Ukraine. Ukrainian ties to Iran are not just via Russia as one might expect, but also Turkey (TR). Turkey accounts for 7.4 percent of Iranian total exports and 7.9 percent of Ukrainian total exports. Turkey is the first non-EU NATO member to draw any attention in this study and notably lies well inside of the economic threat ring.
Figure 6 for the first time shows a close relationship between the U.S. and Iranian economy. This is exciting in terms of representing an opportunity to exert direct influence; however, as the threat ring indicates there are very few buffer states inside the circle to protect the US economy from a backlash. This finding is a result of strong U.S. and China ties. The United States accounts for 19.1 percent of Chinese total exports and China accounts for 15 percent of total Iranian exports. Ireland (IE) also appears close to Iran in the MDS results. This again demonstrates shared economic ties with other European countries trading with Iran, predominately Germany and France accounting for 7.4 percent and 5.8 percent of Ireland’s exports respectively. The relationship between Iran, Germany and France has already been discussed.
The significance of the American and Chinese economy to this system can be seen in the following graph also generated using UCINET on this dataset.
Figure 7 was generated using principle component analysis on the exports dataset. Here the UCINET software is attempting to organize the graph into components that are statistically significant. Just as in MDS, countries are grouped together on the basis of similar trade patterns. The U.S. and China (CN) each are their own component indicating that they are economic superpowers in this system. In the upper left of the graph and tied more so to the U.S. than China, major European economies of the United Kingdom, Germany, France and Italy form a component. In the lower right of the graph and tied strongly to both the U.S. and China, the major Asian economies of Japan, South Korea, Taiwan and Hong Kong form a component. The remaining countries in the dataset do not appear to form distinct logical components, but instead have a myriad of ties to the U.S. and China as well as the major economies of both Europe and Asia.
Having completed the analysis of MDS results, it is now necessary to compare these results to the political strategies of the key actors related to Iranian trade. To demonstrate that economics influence international relations, countries who agree with US policy goals conceptually with respect to Iran and have strong ties to Iran economically would need to engage in political strategies which do not result in a negative economic impact upon themselves. If countries are willing to accept negative impacts on their own economy to pursue policy goals, then that serves to disprove this hypothesis.
As MDS results indicate, the member states of the EU and SCO represent those countries with the strongest economic ties to Iran. Therefore, the policy goals of these organizations and the specific countries identified as most significant within them shall be compared to U.S. policy goals. For those countries found to share U.S. policy goals, their political strategies will be examined in terms of their support of economic coercion of Iran. This comparison shall demonstrate the strength of the hypothesis in this study and serve as an indicator of the possible success of such economic coercion of Iran.
First, it is necessary to establish in broad terms the policy goals with respect to Iran that the United States hopes to achieve through economic coercion. U.S. foreign policy has been hostile toward Iran since the 1979 revolution and certainly in response to holding U.S. embassy personnel in Tehran hostage shortly thereafter. Prior to the revolution, the U.S. had good relations and engaged in trade with the Iranian regime of the Shah, as arguably a U.S. proxy in the region. Such diplomatic and economic relations were severed as a result of the Iranian revolution and would have been naturally with or without the imposition of U.S. sanctions.
U.S. sanctions on Iran continue to this day. These sanctions are not imposed by the United Nations Security Council or any other multilateral organization or agreement. These sanctions are a result of Executive Orders of the President of the United States and Congressional legislation. These sanctions relate to four main issues of concern: Iranian suspected pursuit of weapons of mass destruction including nuclear weapons, sponsorship of transnational terrorism, supporting violent opposition to the Middle East Peace Process, and Iran’s domestic human rights violations. U.S. sanctions allow for limited commercial relations including Iranian food and medical imports from the U.S. and food and carpet exports to the U.S.
It has already been noted that these U.S. sanctions have not received formal multilateral support. In the context of this research, the member states of the European Union and Shanghai Cooperation Organization have the greatest capacity to influence Iranian economics. By the hypothesis posed in this study, these same countries should then be unlikely to support sanctions owing to the consequences on their own self-interest.
Examining EU policy on U.S. sanctions is perhaps the most interesting to consider first because of the strong historical, cultural, economic and diplomatic ties between the U.S. and the members of the EU. In a press release upon the renewal of the U.S. Iran and Libya Sanctions Act, the EU Commissioner for External Relations released the following statement: “As a matter both of principle and policy, the European Union has long opposed unilateral sanctions laws with extraterritorial effects. Such laws, designed to impose U.S. requirements on economic operations of foreign countries, threaten the open international trading system.”
This same statement highlights EU policy making corporate compliance with these U.S. sanctions illegal by any company in the EU. This official statement by the EU notes that such actions by the U.S. create expense for European countries and companies whether or not they comply with the sanctions and even threatens action against the U.S. in the World Trade Organization should the US engage in any secondary boycotts or sanctions of European countries or companies owing to their non-compliance with U.S. sanctions.
Such strong policy and rhetoric emanating from the closest allies of the United States might represent strong disagreement with the issues the United States has with Iran; however, the very same EU statement concurs that the EU is concerned with Iranian weapons of mass destruction programs, sponsorship of terrorism and human rights violations. Therefore, it seems reasonable to take this EU policy at face value to mean that the EU opposes these sanctions owing only to the economic self-interest of its member states.
Germany, France and Italy are not only some of the strongest economic powers within the EU, but they are also those most strongly tied to Iran based on the results of this study. The alignment of EU policy to economics over diplomatic ties to the U.S. supports the underlying hypothesis that economics constrains international relations and that such tendency can by quantified by empirical measurement.
The member states of the SCO were next identified as those most capable of exerting influence over the Iranian economy. SCO policy shows a similar dominance of economics in international relations as found in the case of EU member countries. Setting the stage for this analysis, it is most easy to understand that Iran has observer status in the SCO where the US lacks such status and key members of the SCO, Russia and China, are historically global competitors with the US. Thus, unlike the members of the EU, the members of the SCO except perhaps India do not have a long history of close friendly ties to the U.S. Even India has previously grossly defied U.S. policy initiatives in terms of its own development of nuclear weapons.
SCO focus areas are: multi-polar emergence to compete with U.S. global hegemony, energy sector economic development, and regional security cooperation. Central to the SCO strategy is what many are metaphorically calling the New Great Game. The New Great Game (NGG) represents competition between the world’s greatest powers (US, Russia, China and India) for influence in Central Asia and is predominately, but not entirely, tied to the exploitation of oil reserves. U.S. interest in these oil reserves is two-fold. First, much of this oil has the potential to be exported to Western Europe, including the United States’ closest allies in the EU and NATO. The U.S. has an interest in supporting the goals and advancement of its allies. Second, as oil is a fungible commodity, increased global supply can be expected to reduce prices for all oil consumers from all suppliers. As the world largest oil consumer, the U.S. has an interest in lowering the price of oil.
Within the SCO, Russia, Uzbekistan and Pakistan are the countries with the strongest economic ties to Iran according to the results of this study. Iran is a pivot point in the NGG physically, economically, and ideologically residing between the great powers of the East and West. While Iran’s westward looking policies appear to the West to be roguish and radical, Iran’s eastward looking behavior predominately consists of the rational economic and trade relationships previously noted in this study.
Friction between Russia and Pakistan is growing as the NGG emerges because Russia has chosen to support India’s full membership in the SCO in order to form a powerful bloc to compete with China. In exchange, Russia is expected to support India’s desire for the exclusion of Pakistan from full membership in the SCO. This naturally forces Pakistan to seek to cooperate with China. Iran has long cooperated with the ideologically sympathetic countries of Pakistan and Uzbekistan, as seen in the economic data used here; therefore, Russia’s desire to consolidate power is likely to move Iran closer to China while perhaps still maintaining its economic ties to Russia.
While the US and China are competitors in Central Asia and in the NGG paradigm, globally the American economy is strongly tied to the Chinese economy as already discussed. Notably, the United States is also heavily reliant on Pakistan as a logistical supply route for US and NATO military forces in Afghanistan and combating terrorism inside the borders of Pakistan. These ties and the emergent behavior in the context of NGG and related to the growth of the SCO again place Iran at center of this problem.
Whether Iran is a mere pawn in the New Great Game, a power broker between the East and West, or simply a large importing and exporting economy relative to other lesser developed countries in the region; it is clear that none of the member states of the SCO can afford to damage their diplomatic or economic relations with Iran without considering the consequences for their own economy. Few members of the SCO have any strong sympathetic reason to support U.S. sanctions on Iran. Therefore, it is not surprising that economic and policy decisions of the SCO also support the hypothesis that international relations in this context are constrained by economics.
While the case of the EU, SCO and leading member states of these organizations supports the hypothesis of this research, a potential bias is that this situation is not by accident. Iran has had roughly 30 years, from 1979 to today, to craft a strategy to undermine US sanctions by exploiting the globalized economy, Iran’s advantageous geographical location, and global reliance on natural resources available in Iran. This and other biases previously mentioned constrain the case of Iran to a proof-of-concept admitting that there are many factors other than economics which are potentially causal in the emergence of these conditions. This case, however, serves to demonstrate that irrelevant of such potential factors, it is possible to identify and quantify the existence of these conditions using the methodology defined and tested in this study based solely upon empirical analysis of balance of trade data.
Herein lies the limits of such techniques, identification and analysis of empirical results only provide increased understanding of the context of the problem. Without more detailed contextual analysis it is not possible to determine the best course of action to achieve policy aims given this increased understanding of the problem. To this end, discussion of the foundation of such sanctions and alternatives are addressed in the following section.
Context of U.S.-Iran Sanctions
Previously in this paper, the U.S. issues of concern with the Iranian regime have been articulated and the hostile environment surrounding U.S.-Iran relations following the 1979 revolution have been touched on. However, none of this history explains why sanctions were chosen over other approaches to resolving this conflict.
Meghan O’Sullivan of the Brookings Institute makes the case that the U.S. engaged in unilateral sanctions owing predominately to domestic issues rather than the truly shared interests of the international community with respect to Iran’s weapon programs, sponsorship of terrorism, and human rights violations. As previously noted the international community shares these concerns; however, does not share the American zeal for sanctions.
O’Sullivan notes in part that there is distrust of U.S. intentions owing to legitimate Iranian claims of US meddling in Iranian affairs historically and American pro-Israeli leanings in the context of the Middle East Peace Process. Further, Iran received no lenience from the U.S. after its neutral stance in the Gulf War and support of the current war in Afghanistan toppling the Taliban regime. In fact, O’Sullivan goes further arguing that while U.S. sanctions have done little to modify Iranian behavior and have to some degree alienated U.S. allies in the EU and elsewhere, if anything sanctions ensure a stalemate in U.S.-Iranian relations.
The maintenance of internationally unpopular and arguably ineffective unilateral U.S. sanctions is attributed to the lobbying efforts of the American Israel Political Affairs Committee (AIPAC). The interests of AIPAC are by definition a convolution of those of the United States and Israel arguably to the neglect of broader U.S. interests. The Brooking’s Institute estimates that between the years of 1995 and 2001, these sanctions have cost Iran $4.4 billion while simultaneously costing the US$1.9 billion. Whether or not one agrees with the argument posed by the Brooking’s Institute entirely or the ideological aspirations of AIPAC, the fact that AIPAC remains a powerful lobby affecting U.S. decision making related to Iran cannot be neglected in considering alternatives to sanctions.
Ken Pollack who served on the National Security Council and prior to that as an Iran analyst at the Central Intelligence Agency in both the Clinton and Bush Administrations proposes such a set of alternatives in his book, The Persian Puzzle. Specifically, Pollack proposes the following alternative policies: (1) unilateral concessions, (2) the grand bargain, and a (3) triple track approach.
The unilateral concessions alterative argues for lifting US sanctions with the aim of encouraging peaceful, democratic regime change in Iran leading to the election of moderate leaders less desirous of pursuing nuclear weapons, sponsoring terrorism and violating human rights. The grand bargain alternative involves negotiating all of the issues of concern between Iran and the US simultaneously with the existing Iranian regime in exchange for normalized, if not favorable, relations as the immediate result. Pollack for relatively obvious reasons does not favor either of these alternatives, but rather favors a hybrid approach he calls the triple track approach.
The triple track approach keeps the option for the grand bargain on the table should sufficient progress be made toward conflict resolution; applies carrots and sticks to progress in meeting agreed upon milestones in terms of nuclear weapons, sponsorship of terrorism and human rights; and maintains the option for the U.S. to fall back to a new containment policy targeting Iran. The premise being that this new containment policy, perhaps even multilateral sanctions, would receive international support unlike current U.S. sanctions, if the US has shown good faith in attempting to resolve its conflicts with Iran.
It is too early to determine to what degree the Obama Administration’s approach to its renewed interest in conflict resolution with Iran takes advantage of these insights; however, what is clear is that maintaining the U.S. regime of sanctions is likely to continue to have the same relatively insignificant positive influence on Iran. It is not clear that analysts, such as Pollack, have considered the influence of economics on international relations in terms of gaining international support. Likewise, consideration limited to such empirical research neglects the emotional component raised by O’Sullivan in the context of AIPAC sympathy to the state of Israel affecting U.S. foreign policy.
Using Iran as a case study, this paper has demonstrated the viability of the concept of economic threat rings as developed here using MDS as a tool to quantify the degree to which the interconnectedness of global trade constrains international relations. While such quantitative methods applied to balance of trade data appear useful in gaining improved understanding of the underlying complexities of the global economic system, there remains a clearly articulated need for additional qualitative and contextual analysis as well. Analysis of empirical data does not provide answers as to causality nor does it provide prescriptions for intervening courses of action.
In this paper, some of these sources of bias and context have been discussed such as domestic policy and historical legacies; however, many other sources of bias likely exist in any given case study. Qualitative analysis regarding the relationship between economics and international relations has been discussed in the context of Iran. Alternative policy options have been presented. What is perhaps most valuable is the gained understanding that the most viable policies are likely those that account for the observed behavior both quantitatively and qualitatively.
Volumes have been written on the qualitative aspects of international relations over generations perhaps starting with Plato’s Republic; however, less intellectual effort has been expended examining the quantitative aspects presented in this paper. Therefore, the greatest contribution of this work rests in the development and application of the economic threat ring methodology. While the analytic technique applied (Multi-Dimensional Scaling) is not a new mathematical technique, the application to balance of trade as a tool for gaining insight into the propensity and potential of nation state actors in the complex global economic system is new. Most important to the proper application of this technique is selection of the countries to include in sample datasets based on international relations, the selection of the indicator variable as percentage versus raw monetary value, and understanding how to apply theoretical selection criteria to analytical issues such as dimensionality and thresholds on stress.
The utility of such techniques shall endure not simply owing to continued U.S. interest with Iran as in this case study, but as a result of the enduring fact of economics as an instrument of national power affecting international relations. As globalization increases the interconnectedness of national economies around the world, transnational trade relationships shall offer both increased risk and opportunity in the pursuit of national objectives. While this interconnectedness potentially enables international coalition building based upon common economic interest, such interconnectedness potentially deters, moderates, and constrains nations from actions, which would negatively affect their economy. The growing complexity of this paradox will challenge decision makers in new ways requiring the application of new tools such as those presented in this paper.
About the Authors
Dr. David A. Anderson is a retired U.S. Marine Corps officer. He is now a professor of Strategic Studies at the U.S. Army Command and General Staff College, Fort Leavenworth, Kansas, where he teaches strategic and operational studies, as well as economics. He is also an adjunct professor for Webster University, Missouri, where he teaches various international relations courses. He has published numerous articles on military and international relations related topics. He holds a doctorate in business administration (with emphasis in International Business and Development Economics), from Alliant International University, California.
Lieutenant Colonel Rob Renfro, US Air Force, is presently assigned to the U.S. Army School of Advanced Military Studies having previously served as a Senior Staff Scientist at U.S. Central Command in Tampa, Florida. He holds a BS in Operations Research and Arabic from the U.S. Air Force Academy, an MS in Strategy and Tactics/Operational Analysis and a PhD in Operations Research both from the Air Force Institute of Technology. He is a graduate of the Post-Graduate Intelligence Program of the Joint Military Intelligence College where he later taught as an Adjunct Instructor for the graduate course, “Middle East Intelligence Issues and Concepts.” He has also studied Arabic, Arab Anthropology and Islamic History at the University of Aleppo, Syria and Arabic and Islamic Studies at the Institute for Islamic and Arabic Sciences in America, a branch of Imam Muhammad Bin Saud Islamic University in Riyadh, Saudi Arabia (Saudi Arabian Ministry of Higher Education). Additionally, he is an Assistant Professor of International Relations at Webster University.
Anderson, Rolph, and others. Multivariate Data Analysis, 3rd ed. New York: Macmillan Publishing Co., 1992.
Apostol, Tom M. Mathematical Analysis. Reading: Addison-Wesley Publishing Company, 1974.
Axelrod, Robert and Michael D. Cohen. Harnessing Complexity. New York: Basic Books, 2000.
Borgatti, Stephen P., and others. UNCINET for Windows : Software for Social Network Analysis. Harvard, MA: Analytic Technologies, 2002.
Borgatti, Stephen P., and others. UCINET 5.0 for Windows User’s Guide. Version 1.00. Natick: Analytic Technologies, 1999.
Central Intelligence Agency. “The World Factbook: Exports – Partners,” accessed June 12, 2009.
Central Intelligence Agency. “The World Factbook: Imports – Partners,” accessed June 12, 2009.
European Union. “EU regrets extension of US sanctions law against Iran and Libya,” accessed September 9, 2009.
Hawkins, Charles and Robert R. Love. The New Great Game. Fort Leavenworth: Foreign Military Studies Office, 2006.
International Monetary Fund. “World Economic Outlook Databases,” accessed June 12, 2009.
International Organization for Standardization. “English country names and code elements,” accessed June, 12, 2009.
O’Sullivan, Megan L. Shrewd Sanctions. Washington, D.C.: Brookings Institute Press, 2003.
Pollack, Kenneth M. The Persian Puzzle. New York: Random House, 2005.
Shanghai Cooperation Organization, accessed June 12, 2009.
United Nations. “United Nations Commodity Trade Statistics Database,” accessed June 12, 2009.
United States Department of State. “U.S.-Iranian Relations,” accessed September 9, 2009.
World Bank. “World Development Indicators,” accessed June 12, 2009.
World Trade Organization. “Statistics Database,” accessed June 12, 2009.
1. Megan L. O’Sullivan, Shrewd Sanctions, (Washington, D.C.: Brookings Institute Press 2003), 1-2.
2. United States Department of State, “U.S.-Iranian Relations,” (accessed September 9, 2009).
3. Robert Axelrod and Michael D. Cohen, Harnessing Complexity (New York: Basic Books, 2000), 153.
4. Stephen P. Borgatti, and others, UCINET 5.0 for Windows User’s Guide, Version 1.00 (Natick: Analytic Technologies, 1999): 29.
5. Borgatti, 78.
6. Borgatti, 78.
7. Borgatti, 32.
8. Borgatti, 32.
9. Central Intelligence Agency, “The World Factbook: Imports – Partners,” (accessed June 12, 2009).
10. Central Intelligence Agency, “The World Factbook: Exports – Partners,” (accessed June 12, 2009).
11. United Nations, “United Nations Commodity Trade Statistics Database,” (accessed June 12, 2009).
12. International Monetary Fund, “World Economic Outlook Databases,” (accessed June 12, 2009).
13. World Bank, “World Development Indicators,” (accessed June 12, 2009).
14. World Trade Organization, “Statistics Database.”
15. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
16. Central Intelligence Agency, “The World Factbook: Imports – Partners.”
17. Shanghai Cooperation Organization, (accessed June 12, 2009).
18. International Organization for Standardization, “English country names and code elements,” (accessed June 12, 2009).
19. Stephen Borgatti, and others, UCINET for Windows: Software for Social Network Analysis (Harvard, MA: Analytic Technologies, 2002).
20. Central Intelligence Agency, “The World Factbook: Imports – Partners.”
21. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
22. Central Intelligence Agency, “The World Factbook: Imports – Partners.”
23. Central Intelligence Agency, “The World Factbook: Imports – Partners.”
24. Central Intelligence Agency, “The World Factbook: Imports – Partners.”
25. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
26. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
27. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
28. Central Intelligence Agency, “The World Factbook: Exports – Partners.”
29. United States Department of State, “U.S.-Iranian Relations.”
30. United States Department of State, “U.S.-Iranian Relations.”
31. United States Department of State, “U.S.-Iranian Relations.”
32. European Union, “EU regrets extension of US sanctions law against Iran and Libya,” (accessed September 9, 2009).
33. European Union, “EU regrets extension of US sanctions law against Iran and Libya.”
34. European Union, “EU regrets extension of US sanctions law against Iran and Libya.”
35. European Union, “EU regrets extension of US sanctions law against Iran and Libya.”
36. Charles Hawkins and Robert R. Love, The New Great Game, (Fort Leavenworth, KS: Foreign Military Studies Office, 2006), 1-2, 47, 171-172.
37. Hawkins, 1.
38. Hawkins, 11.
39. O’Sullivan, 54.
40. O’Sullivan, 45.
41. O’Sullivan, 45.
42. O’Sullivan, 47.
43. O’Sullivan, 54.
44. O’Sullivan, 101-102.
45. Kenneth M. Pollack, The Persian Puzzle, (New York, NY: Random House, 2005).
46. Pollack, 390,395,400.
47. Pollack, 390.
48. Pollack, 395.
49. Pollack, 401, 405, 412.
Appendix: Iranian Trade Data