1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

Research indicates that if third parties provide assistance to sanctioned states, the sanctions are less likely to be successful. However, the scholarship on the profile of sanctions busters and their motivations remains underdeveloped. Drawing on the realist and liberal paradigms, this piece develops two competing theories to account for third-party sanctions-busting. The hypotheses drawn from these theories build upon existing work on sanctions, the political determinants of international trade, and the effects of indirect interstate relationships. A quantitative analysis develops a new measure to identify sanctions-busting behavior for a dataset covering 77 sanctions cases from 1950 to 1990. The liberal and realist explanations are then tested. The results offer strong support for the liberal theory of sanctions-busting and less support for the realist theory. In particular, the analysis reveals a counter-intuitive finding that a sender’s close allies are more likely to sanctions-bust on the target’s behalf than are other states.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

When even one country gives substantial assistance to a sanctioned state, it diminishes the sanctions’ likelihood of success (Lektzian and Souva 2007; Martin 1992). The Soviet Union’s support of Cuba helped to sustain the state during the American sanctions imposed against it, just as China’s support helps sustain the North Korean regime today. Given the recognized impact of sanctions-busting, it remains an underdeveloped field of inquiry. This gap is both theoretical and empirical. While no generalizeable framework exists to explain what drives sanctions-busting behavior, both realism and liberalism offer potentially strong explanations for sanctions-busting trade. Realism argues that third-party states’ trade with sanctioned target states should reflect their state-based security concerns. In turn, liberalism contends that third-party states’ trade with the target is driven primarily by profit-seeking firms and individuals. As just one sanctions buster can harm a sanctions regime, it is crucial to gain a better understanding of which states pose the greatest threats of sanctions-busting. This piece sets out to uncover the general profile of sanctions busters and answer the question of what motivates their trade with sanctioned states: state-based security prerogatives or profit-hungry firms.

A theory explaining the emergence of sanctions busters has important implications for both the sanctions research agenda and the policymaking community. Lisa Martin’s trailblazing theory of coercive cooperation made notable advances in explaining “the conditions under which states cooperate to impose economic sanctions” (1992, 10). Yet, the complementary and equally important theoretical prerogative has yet to be developed—a theory that can explain the conditions under which cooperation occurs to evade sanctions.

While Martin (1992) explains how senders can coerce third parties into participating in sanctioning efforts, it is also possible that sanctioned-states can leverage assistance in similar ways. Indeed, a third-party state’s response to sanctions can be affected by its relations with the sender, target, and economic constituencies, as each can pressure the third-party state to adopt favorable policies. The sanctions sender wants to prevent third-party states from interfering with the sanctions or convince them to join the sanctioning effort (Drezner 2000; Martin 1992). Conversely, the sanctioned state seeks third-party assistance—either by aid or trade—in undermining the sanctions (Naylor 2001). Finally, economic constituencies in third-party states, and even the sender state, want access to potentially lucrative trade with the sanctioned states (Kaempfer and Lowenberg 1999; Morgan and Bapat 2003). Thus, the foreign-trade policies selected by third-party governments, whether explicitly or implicitly, are affected by political and economic factors and, in turn, have political consequences on their relationships with the target state, sender state, and domestic constituencies.

While other empirical work explores how third-party trade with sanctioned states changes after sanctions are imposed (Caruso 2003; Early 2007; Kaempfer and Ross 2004; Yang et al. 2004), this piece focuses on identifying those states that become the largest sanctions busters. Such behaviors can be identified in terms of how salient a third-party state’s bilateral trade is to the target and the degree to which the two states’ trade increases after the target is sanctioned. Exploring the factors that explain the emergence of sanctions busters provides a more nuanced account of the challenges faced in imposing sanctions. From a policy-making standpoint, a theory of third-party response can provide ex ante insight into the conditions under which sanctions busters are likely to emerge and guide policymakers in focusing their limited diplomatic resources upon those states most likely to sanctions-bust. While this topic has implications for how third-party responses affect sanctions’ successfulness,1 this study confines itself to the initial task of establishing which states sanctions-bust and why.

In constructing an explanatory model of sanctions-busting, this paper draws from both the realist and liberal schools of international relations. These contending theories differ principally with respect to international trade over their assumption regarding governments’ abilities to control their states’ trade flows and whether state trade behavior is driven primarily by security or profits. Within the sanctions literature, Hufbauer, Schott, and Elliot’s (1990)“black knight” explanation for why third parties assist sanctioned states holds that sanctions busters are motivated by political considerations. This corresponds to the realist literature, which argues that states’ strategic considerations shape their international trade flows. More recently, a separate body of work explores the economic incentives behind sanctions-busting (Kaempfer and Lowenberg 1999; Morgan and Bapat 2003), and the challenges they create for multilateral sanctioning efforts (Drezner 2000). While consistent with liberalism’s emphasis on commercial motivations, such works do not take full advantage of the theory’s broader perspective on the role of politically based transaction costs in shaping firm behavior. The hypotheses tested in this piece draw on these perspectives to explain how characteristics of third-party states and their economic and security relationships with the target and sender states affect their likelihood of becoming sanctions busters.

The paper begins by drawing from the literature on sanctions and the political determinants of international trade to develop both a liberal and a realist account of third-party behavior. To test these theories, the empirical portion of the paper begins by explaining how sanctions busters can be identified and how their behaviors can be operationalized. This sanctions buster variable is used as the dependent variable in the analysis. The theories are tested using a logit model with a cross-sectional time series dataset covering 77 sanctions cases from 1950 to 1990. The results provide strong support for the liberal perspective and counterintuitive findings with respect to the realist perspective. As the evidence shows, close allies of the sender are more likely to sanctions-bust on the targets’ behalf. The discussion explores how these counterintuitive findings can be reconciled within the realist and liberal theories. The paper concludes by examining the implications of the findings for both the academic and policy-making communities studying sanctions.

Theoretical Explanations for Sanctions-Busting

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

This study considers an economic sanction to be “a restriction placed [by the sender] on commercial activities with the intent to inflict economic losses on others” (Askari et al. 2003, 84). This definition focuses on economic sanctions as a coercive policy tool used by a sender against a target, but the definition does not tie the use of this tool to the specific type of policy changes being sought.2 As this study focuses on the externalities that the imposition of economic sanctions have on their targets’ trade with other states, this simple, instrumental definition of economic sanctions fits well.

Overall, much of the academic literature on sanctions examines the extent to which economic sanctions are successful and what factors contribute if and when they succeed. This work tends to focus on the monadic characteristics of the sender and the target as well as the sender-target dyadic relationships to explain variation in why some sanctions achieve their senders’ objectives and others do not (Drury 1998; Hufbauer and Oegg 2003; Hufbauer, Schott, and Elliot 1990; Lektzian and Souva 2007). However, the literature on multilateral sanctions (Drezner 2000; Kaempfer and Lowenberg 1999; Martin 1992) and the economic externalities of sanctions (Caruso 2003; Van Bergeijk 1994; Yang et al. 2004), have paid more attention to the role of third-party states. In the abstract, the relationship between these states can be conceptualized with a triadic model. The three states in the model are: the primary “sender” of the sanctions, the “target” of the sanctions, and “third-party states” that constitute the rest of the states in the world.3

As Figure 1 illustrates, the relations among the three states are interdependent. Scholars drawing on both the liberal and realist schools (Maoz et al. 2007; Polachek, Robst, and Chang 1999) demonstrate that indirect relationships can meaningfully affect states’ trade and conflict behaviors. Depending upon the nature of those relationships, certain states may be more or less likely to become sanctions busters. To understand sanctions-busting behavior, both the liberal and realist theories developed below draw on this triadic model of interaction in explaining third-party responses to the imposition of sanctions.


Figure 1.  Triadic Relationship Between the Target, Sender, and Third-Party States

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The Realist Perspective and Sanctions-Busting

Realists view the world in terms of unitary states existing in an anarchic, self-help international system (Morgenthau 1978). Realism argues that states must constantly be concerned about relative gains made by potential rivals. As such, the theory’s principal concern regarding international trade addresses its effect on security considerations. Realists tie international trade to the provision of externalities that can enhance national power. This perspective argues that states trade strategically, trading more with their allies and states sharing similar interests, and less with their rivals (Gowa 1994; Gowa and Mansfield 1993; Long 2003). Some scholars have begun exploring more complex indirect relationships, positing that states’ trade can be strategically shaped by “the enemy of my friend is my enemy” or “the enemy of my enemy is my friend” dictums (Maoz et al. 2007; Polachek, Robst, and Chang 1999). This asserts that third-party states interested in the security implications of their trade should strengthen their allies’ positions vis-à-vis their allies’ enemies and weaken their rivals’ positions vis-à-vis their rivals’ enemies. Key within this perspective is the assumption that governments, not the private sector, are viewed as the primary drivers of their states’ trading behaviors. Realist explanations for sanctions-busting thus focus on third-party states’ security relationships with the target and sender states.

Indeed, Hufbauer, Schott, and Elliot (1990) offer an explanation of sanctions-busting assistance based primarily upon political motivations. The authors speculate that the assistance of “black knight” states is politically motivated, but never parse out whether rivalry between the third party and the sender or an alliance between the third party and target is responsible for the assistance. The archetypical case they cite is the Soviet Union’s assistance to Cuba during the Cold War. They conjecture that the Soviet Union’s alliance with Cuba and rivalry with the United States drove its response. Problematically, the “black knight” variable which the authors develop to test their hypotheses regarding the effects of third-party assistance on sanctions does not identify which states provided the assistance, in what levels, or when it was given, which to date has precluded this theory from being tested (Hufbauer, Schott, and Elliot 1990, 46).

Thus, the two “black knight” hypotheses assert that third-party states with close security relationships with the target should come to their aid after sanctions are imposed, as should rivals of the sender. As defense pacts have already been found to elicit intra-alliance trade for security purposes (Long 2003), and alliance partners are more likely to be adversely affected by any security vulnerabilities the sanctions create in the target, defense pact partners should be more likely to come to the target’s aid than other states. The alliance should also facilitate the target’s efforts to find cooperative means of skirting the sender’s sanctions with third-party assistance. Following the “the enemy of my enemy is my friend” logic, the “black knight” perspective also predicts that rivals of the sender should seek to strengthen the position of fellow states that are rivals of the sender. As resources spent by the sender on the imposition of sanctions against the target are tied up and cannot be used for other purposes, rivals of the sender may seek to make gains by deliberately undercutting the sender’s sanctions. This would force the sender to divert more resources to achieve its objectives and make it less likely that its sanctions will succeed. These “black knight” behaviors are apt to be mutually reinforcing, not mutually exclusive.

RH1: If the third-party state has a defensive pact with the target (TargetDP), it will be more likely to become a sanctions buster.

RH2: If the third-party state is a rival of the sender (SenderRival), it will be more likely to become a sanctions buster.

The realist perspective also produces expectations over the types of relationships that third parties will have with targets and senders that should deter sanctions-busting behavior. States sharing defense pacts with the sender will be more likely to support the senders’ sanctions, as their security considerations are intertwined. Likewise, these states’ alliance ties should structure their political relations, making policy cooperation and coordination easier. This is consistent with the “enemy of my friend is my enemy” logic. In terms of the third-party’s relations with the target, the presence of a rivalry between the two states should provide the third party with incentives to support the senders’ efforts to weaken its adversary or, at the very least, to refrain from helping the target. In both cases, the state government has strategic incentives to impose restrictions upon its commercial sectors that otherwise might be interested in trading with the target. The extent to which states effectively implement these restrictions is apt to vary due to variation in the domestic political costs necessary to impose them, and the costs necessary to enforce them (Morgan and Bapat 2003). It could be expected that even if interested third parties could not significantly halt trade with the target, their policy responses could still increase the transaction costs of that trade. This is especially true if that trade subsequently must be conducted illicitly.4

RH3: If the third-party state has a defensive pact with the sender (SenderDP), it will be less likely to become a sanctions buster.

RH4: If the third-party state is a rival of the target (TargetRival), it will be less likely to become a sanctions buster.

Sanctions-Busting and the Liberal Theory of International Trade

The liberal perspective on international trade provides an explanation for sanctions-busting that focuses primarily upon the economic considerations of the parties engaging in the trade. Liberal theory considers “individuals and private groups” to be the primary actors in international politics, pursuing their interests in a largely “rational and risk averse” way (Moravcsik 1997, 516). Instead of viewing trade as occurring between unitary states, the liberal perspective highlights the role that firms and individual actors play in determining trade flows (Barbieri and Levy 1999; Kastner 2007; Keshk, Pollins, and Reuveny 2004; Li and Sacko 2002; Pollins 1989a, 1989b). International trade is thus explained by the profitability of trading opportunities available to individual firms and their capacity to capitalize on them. Variation in the physical, economic, and political costs of doing business inherent to international trade effect its profitability, which explains why firms in some states may be advantaged in trading internationally. As Dyer (1997, 535–36) writes, transaction costs exist in any sort of exchange between multiple parties, and these costs rise as uncertainty does. Driven by the pursuit of profits and “…under the constraint of uncertainty, rational firms continuously formulate and update their expectations of future returns and adjust trade accordingly” (Li and Sacko 2002, 33). In other words, firms are constantly seeking to recognize and take advantage of the economic opportunities available to them while avoiding losses when possible.

How target states respond to sanctions can depend upon the other trading opportunities they have to replace the market(s) denied to them, understanding the role played by third-party states is essential. Scholars using liberal or neo-liberal approaches have focused on the economic incentives of trading with sanctioned states. The common wisdom for many sanctions researchers has been to posit an “if you’re not with us, you’re against us” perspective on participating in sanctions regimes, assuming non-participants will always try to sanctions-bust (Drezner 2000; Kaempfer and Lowenberg 1999; Martin 1992; Morgan and Bapat 2003). This perspective places economic incentives at the forefront of the motivation of states’ trade with sanctioned states. At face value, the assumption does a good job of explaining why senders have such a difficult time multilateralizing their sanctioning efforts. However, the assumption is a precarious one.

The “one-size-fits-all” perspective assumes away the findings of the work addressing the political determinants of international trade (Keshk, Pollins, and Reuveny 2004; Morrow, Siverson, and Tabares 1998; Pollins 1989a, 1989b). It also ignores the cross-national variation that exists in how third-party-target trade changes after the imposition of sanctions. In some cases, third-party trade with targets increases after sanctions are imposed and in others it declines, with the magnitudes sometimes varying extensively across cases. By making the universal busting assumption, analysts give up their leverage to explain why this variation exists. For example, why did Soviet, French, Canadian, and Japanese trade with Cuba change after the United States imposed sanctions against it? As the rest of this section details, the liberal theory can provide a more nuanced explanation of sanctions-busting than has been used in the past.

Economic sanctions create uncertainty in trading with the target and may cause disruptions to the trade networks in which the target state is involved (Caruso 2003; Van Bergeijk 1994). However, sanctions also can generate lucrative commercial opportunities with the target because of the imbalanced terms of the trade they create. For imports denied to the target by the senders, Kaempfer and Lowenberg (1999, 40–6) provide a model for how the laws of comparative advantage offer incentives for third parties “to step in to fill the gap.” As the authors demonstrate, this creates a catch-22 for sanctions senders in that the more their sanctions worsen the targets’ terms-of-trade, the greater the potential benefits are for those parties willing to trade with the target. In terms of the target’s exports, the sanctions’ effects depend upon the markets available for the products the target produces. The more limited the demand for their product, the more difficult it will be for firms in the target to find other buyers for their surpluses, forcing down the prices target state exporters can charge.

Cross-national variation in political and economic factors that affect firms’ opportunities to take advantage of the targets’ situation may allow certain states to capture a disproportionate share of the potential gains to be made in trading with the target. For firms seeking to capitalize on sanctions-busting opportunities, the size and commercial profile of the third-party state from which they are importing and exporting matters. Third-party states with large economies will be more capable of absorbing surplus exports from the target. States with large economies also can more easily adjust their production to meet the increased demands of the target, making it easier for them to redirect goods to the target, capitalizing on sanctions-induced opportunities for greater profits.

LH1: The larger the third-party state’s economy (lnGDP3), the more likely it will be to become a sanctions buster.

While economically self-interested, firms do not ignore the international politics of the states from which and to which they are trading. Even as liberalism focuses on the societal level, it acknowledges that “individuals, firms, and governments all shape trade flows” (Keshk, Pollins, and Reuveny 2004, 1164). The finding that “trade follows the flag” has been explained as a function of firms accounting for the risks and political transaction costs created by state governments in their trade behavior (Keshk, Pollins, and Reuveny 2004; Pollins 1989a, 1989b). As sanctions create transaction costs by raising the uncertainty surrounding trade with the target (Van Bergeijk 1994), political and economic linkages that help to foster transparency give firms in certain states more confidence in trading with the sanctioned state. This is born out in the findings that states having social linkages, such as past colonial relationships, trade more with one another than with other states (Bliss and Russett 1998). Third-party states having a past colonial relationship with the target are more likely to have stronger commercial, political, and social linkages than states without such historical ties, making it more difficult for the sender to disrupt those states’ relations. For firms, this would inspire confidence in the stability of those states’ relations.

LH2: If the third party shared a past colonial relationship with the target (Colony), it is more likely to become a sanctions buster.

The target and third-party states’ regimes may also have an effect on their trading relationship, as liberal democratic states’ relationships tend to be more stable and transparent, and they offer their citizens more economic freedom than autocracies. Dixon and Moon (1993, 11) contend that “expected stability is also key because so much modern trade has a long-term component…private actors will be drawn to partner choices that minimize political risk that could disturb long-run relations. This implies that trade relationships are most likely to prosper under conditions where diplomatic conflict is least likely to pose a serious threat.” Examining the aftermath of sanctions, Lektzian and Souva (2001) find that jointly democratic dyads returned to their previously established trade levels faster than mixed-polity dyads, after one of the states had employed sanctions against the other. Lektzian and Souva (2001, 63) posit that democratic institutions play an important role by helping to re-establish “trust” and certainty between the economic actors in both states. When both the third party and target states are democracies, it provides an additional degree of certainty and stability in the two states’ relations. This should be attractive to firms in target states attempting to find new commercial linkages that are not likely to be disrupted again.

LH3: If both the target and third-party states are democratic (JointDem), the third-party state is more likely to become a sanctions buster.

As well, liberalism views economic actors not merely as passive responders to the policies of their governments. Instead, liberalism argues that these economic actors play an active role in lobbying states to adopt their preferred policies (Moravcsik 1997, 528). Drawing on the liberal perspective, Kastner (2007, 670) argues that the utility state leaders derive from foreign-trade policies “depends in part on the trade policy preferences of their core constituents,” which may deter them from “taking actions detrimental to foreign commerce” when it alienates those constituencies. Thus, the policies adopted by third parties are to some extent endogenous upon their constituencies’ demands; policies which in turn affect those constituencies’ ability to trade with the sanctioned states.

As such, the greater the extent to which international trade comprises a significant proportion of the economic and commercial activity of the third-party state, the larger the size and power of the commercial interests available to pressure the government toward maintaining the openness of the state’s economy. Moreover, if firms within the third-party state have vested interests in trading with the target, they are more apt to use their domestic-political leverage to prevent their state governments from placing restrictions on that specific trade (Kastner 2007). This reduces the uncertainty of disruptions. For firms in target states, simply increasing trade with existing trade partners is also less costly than seeking out new trade partners (Keohane and Nye 1989, 8–13). The TradeShare (Tradeij/Total Tradei) variable represents the degree to which a state’s trade is dependent upon trade with another state. The variable captures the extent to which third parties’ commercial constituencies have incentives to lobby against restrictions on their trade with the target (Gartzke and Li 2003, 561). Similarly, states that engage in extensive amounts of international trade will likely have greater logistical capabilities, and be more intertwined in commercial and transshipping networks that facilitate sanctions-busting trade. States with well-developed transportation infrastructures, extensive commercial networks, and marketplaces with logistical and brokerage sectors offer better environments for taking advantage of sanctions-busting opportunities than more closed states. Firms in commercially oriented states, especially those with close linkages to the targets, thus have both political and economic advantages in sanctions-busting.

LH4: The more open the third-party state’s economy is to trade (Open3n−1), the more likely it will be to become a sanctions buster.

LH5: The heavier the third-party state's dependence upon trade with the target state is (TradeShare3n−1), the more likely the third party is to become a sanctions buster.

Differentiating Between the Realist and Liberal Explanations

Both the state-based realist perspective and firm-based liberal perspective of sanctions-busting behavior provide compelling explanations that can be supported with anecdotal evidence. It is likely that both political and economic considerations play a role in determining third-party states’ responses. Both the third-party governments’ security interests and their private sectors’ economic interests affect whether they sanctions-bust on the target’s behalf. As such, the question of which considerations take primacy when those interests conflict with one another becomes extremely important. Exploring this question allows for the substantive foreign-policy implications of the findings to be parsed out, even if both the liberal and realist perspectives produce significant findings.

Table 1 depicts both perspectives’ predictions regarding the emergence of sanctions busters. The realist perspective predicts that sanctions-busting will occur only when the state has a salient security interest in doing so. Similarly, the liberal perspective argues that sanctions-busting will occur only when firms have such an interest. The critical category that can determine whether state-based security motives or firm-based economic motives take precedence occurs on the bottom left. In this case, the state’s security interests are vested in supporting the sanctions or forbidding aid to the target (e.g., it has a rivalry with the target), while the self-interested firms have strong economic incentives to sanctions bust.5 Support for the realist perspective would demonstrate that only security-based factors prove to be significant. Direct support of the liberal perspective does not exclude the role played by security factors, but would show that economic factors play a salient role. The smoking gun for the liberal perspective would be if economic factors continued to lead states [by way of their firms’ behavior] to engage in sanctions-busting behavior even if the third-party state’s security interests should lead the government to oppose it.

Table 1.   Realist and Liberal Predictions of Sanctions-Buster Emergence
Firm Interest in Sanctions-BustingState Interest in Sanctions-Busting
  1. Note. Bold text denotes “Realism” and italics text denotes “Liberalism.”

Not presentNoYes

Operationalization, Data, and Methodology

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

The Structure of the Data Set and the Triadic Unit of Analysis

The empirical portion of this study tests the above hypotheses using data drawn from the sanctions cases identified in Hufbauer, Schott, and Elliot's (1990) sanctions dataset, covering a 41-year period from 1950 to 1990. A total of 77 sanctions cases are included in which states are the primary senders.6 Among these cases, there are 80 instances of individual states being targeted with sanctions, as several sanctions regimes had multiple targets.

The unit of analysis within the dataset is the triad-year, consisting of the primary sender of the sanctions, the sanctions’ target, and individual third-party states taken annually. In this triadic unit of analysis, the sender and target states are identified separately with respect to each sanctions case for all years the sanctions are in place. Then, for each sender-target pairing in the sanctions cases, the remaining states in the world are matched with the sender-target pairs as “third-party states” to form a triad (see Figure 1). The unit of analysis is structured so that in each observation there is a target state, sender state, and third-party state, with the individual and relational characteristics of the states being tied to their roles in the triad. This coding makes the three-way relationship between the sender, target, and third party in a given sanctions case, taken annually, a unique observation. Table 2 provides an example set of triadic observations to demonstrate how the data is structured. For example, the triad consisting of the United States (sender), Cuba (target), and the Soviet Union (third party) during the U.S. sanctions imposed against Cuba in 1965 is distinct from the triad among the United States (sender), Cuba (target), and France (third party) that same year. States can be both a third-party state and the imposer of sanctions in the same year. Just as the Soviet Union was a third party to the U.S. sanctions against Cuba, it also had sanctions imposed against China in 1965, to which the United States played the role of a third party. The observations are taken on a yearly basis instead of aggregating behavior across the course of the sanctions regime to account for temporal variation in the conditions under which sanctions-busting occurred. Overall, this coding method produces 63,778 triadic-observation years in the full model’s data set.

Table 2.   Structure of Triadic Units of Analysis
Primary SenderTargetThird PartyYear
United StatesCubaSoviet Union1965
United StatesCubaChina1965
United StatesCubaFrance1965
Soviet UnionChinaCuba1965
Soviet UnionChinaFrance1965
Soviet UnionChinaUnited States1965

Operationalization of the Sanctions-Buster Variable

This study defines sanctions-busting assistance in terms of international trade. Sanctions-busting behavior is conceptualized as a significant increase in a third-party’s trade with the target following the imposition of sanctions, constituted in high enough levels so as to have a salient impact upon the economic costs the sanctions would otherwise impose. The operational definition of sanctions-busting trade has two key components: a significant increase in third-party-target trade after sanctions are imposed and an absolute threshold for which a trading partner can be of significant importance to the target state. This operationalization provides a mechanism for exploring how the commercial relationship between the third party and target changes after sanctions are imposed. It also requires establishing standards for both attributes where none exist.

To capture the first of these two elements—post-sanctions change in third-party-target trade—separate trade growth indices were created for exports and imports between the third party and target states for the years preceding and during the sanctions. Average index figures of the import and export growth between the target and third party are created for the 3 years preceding the sanctions (where possible)7 to provide a baseline measure of the states’ pre-sanctions trade relationship. These pre-sanctions index figures are then subtracted from the third-party-target states’ annual trade-growth percentage during the years the sanctions were in place. This provides a percentage by which the third-party state’s trade with the target state was either higher or lower than the baseline levels of growth established by the pre-sanctions indices. The threshold set for constituting sanctions-busting behavior is a 5 percent positive growth in the third-party state’s imports/exports after the imposition of sanctions beyond the baseline established by the index figure. Given that changes may occur in one-time boosts, sanctions busters are considered to remain busters the following years if their trade growth with the target state remains positive. While there are sound reasons to think this is a strong measure, alternative coding schemes were also tested and provided results consistent with those reported in the study.8 In terms of the second criterion, for a third-party state to qualify as a significant trading partner its trade with the target had to comprise at least 5 percent of the target’s overall trade. Using this qualification on the third-party’s share of the target’s total trade (TradeShare) is important for two reasons. First, it identifies those states with which the target’s trade is most concentrated, providing a criterion for determining the salience of the third-party’s trade in terms of the target’s dependence upon it (Gartzke and Li 2003). Second, third parties conducting only minimal trade with the target could have their trade change figures fluctuate significantly due to only small changes in trade flows. Thus, this criterion identifies those states that the target has non-trivial commercial relationships with, and with whom it could rely on in ameliorating the effects of the sanctions. The population cannot be restricted solely to examining those states that meet this qualification, however, because the states that meet this specification change over time and the imposition of sanctions influences which states actually become this salient to the target.

Using these criteria, a binary variable signifying the third-party state is a sanctions-buster (SanctBust) is coded as 1 in the years that the third-party state’s trade with the target meets these criteria and 0 otherwise.9 The variable is coded using data on recorded international trade flows, primarily compiled by the International Monetary Fund, from Gleditsch’s (2002)“Expanded Trade Data Set 4.1.”10 This coding has face validity in that it captures the most likely culprits, such as the Soviet Union on behalf of Cuba, South Africa on behalf of Rhodesia, and China on behalf of North Korea. Additionally, it flags non-intuitive cases like Japan’s sanctions-busting on behalf of Cuba, and Belgium and Spain’s busting on behalf of Libya. The dependent variable SanctBust identifies 1,495 cases of sanctions-busting among states that sanctions-busted either imports or exports, which constitutes 2.34 percent of the total observations in the set.11 The table in Appendix A contains the listing of the sanctions cases in the set and the number of observations within each in which states are identified as sanctions-busting.

Coding the Independent Variables and Controls

For SenderDP and TargetDP, the variables are coded 1 if the third-party state had a defense pact with the given state during the observation year in the “Formal Interstate Alliance Data Set,” and 0 otherwise (Gibler and Sarkees 2004). For SenderRival and TargetRival, the variables are coded 1 if the third-party state has a rivalry with the given state in the observation year in the “New Rivalry Dataset,” and 0 otherwise (Klein, Goertz, and Diehl 2006). Turning to the liberal variables, Colony is coded as 1 if the third party and target shared a past colonial relationship, and 0 otherwise (CEPII 2006). Using the “Polity IV Dataset,”JointDem was coded as 1 if both the third party and target had polity scores of 6 or above for the observation year, and 0 otherwise (Marshall and Jaggers 2003).

The trade and economic data used for the variables, lnGDP3, lnGDPT, TradeShare3n−1, and Open3n−1 came from the Gleditsch (2002) data set. Economic size was logarithmically transformed to reflect the diminishing returns of economic size and make the units of analysis comparable in the logit model. TradeShare3n−1 constitutes the third-party’s lagged trade with the target as a proportion of its overall trade. Open3n−1 is coded as the total amount of trade a state conducts as a proportion of its GDP, which measures the state’s degree of economic openness and engagement in international trade. TradeShare3n−1 and Open3n−1 are both lagged 1 year to avoid simultaneity bias.

The model also contains a set of variables to control for characteristics of the third-party-target economic relationship, the nature of the sanctions, and temporal biases. The target’s economic size (lnGDPT) is used to control the target-specific effects that factor into its need to make up for lost trade and the incentives for third parties to trade with it following sanctions. To control for the transaction costs created by the third party and target states’ geographic relationships, the model includes variables for the logged distance between the two states (lnDist) using data from the EUGene v.3.02 Database (Bennett and Stam 2000) and whether the states are contiguous within 24 nautical miles of one another (Contig) using data from Stinnett et al. (2002). Using a gravity model of international trade, Kaempfer and Ross (2004) find that the countries that were geographically proximate to apartheid South Africa during the sanctions imposed against it in 1980s traded more with it than states that were further away—irrespective of their political support for the sanctions. As such, it could be expected that distance should negatively affect a state’s likelihood of being a sanctions buster, while contiguity should positively affect it. In addition, a variable (Majpower3) is added to control for the third party being a major power state, which could affect its opportunity and willingness to sanctions-bust. Majpower3 is coded as 1 if the state is a major power and 0 otherwise (Bennett and Stam 2000).

Several controls are included that specifically address the nature of the sanctions imposed. Hufbauer, Schott, and Elliot’s (1990, 48) “Cost” variable is included within the analysis to measure the senders’ incurred costs in imposing sanctions. Cost is a 4-point ordinal measure of sanctions severity going from least to most severe. A score of 1 indicates that the sanctions may have incurred a net gain for the sender, while a 4 indicates that the sanctions incurred major losses for the sender. The model also includes a count variable (Duration) for the number of years the sanctions have been in place. The longer the sanctions have been around, the harder it is for senders to keep up pressure on third-party states not to bust the sanctions. In addition, a variable (OrgSender) is coded to designate when the sanctions’ were strongly augmented by an international organization, as in the cases of the British-UN sanctions against apartheid Rhodesia. According to Drezner (2000), organizational backing helps keep member states from reneging on their sanctions obligations, and so this variable may exert a negative effect. Finally, a variable is coded (Overlap) for the number of ongoing sanctions that both the trading partners are being subjected to at one time (Hufbauer, Schott, and Elliot 1990).12 Sanctioned states facing multiple external sanctions may be more likely to trade with each other than with other partners. This variable examines whether or not, in terms of their trade, sanctioned states cooperate more with one another to defeat the sanctions imposed against them.

The Statistical Model and Results

Five sanctions-busting models are run using logit with White-Huber robust standard errors, which are displayed in Table 3. Models 1 and 2 test the realist hypotheses, first without and then with control variables. Models 3 and 4 do the same respectively with the liberal hypotheses. Model 5, the full model, includes the liberal, realist, and control variables. Temporal dependence is addressed in all five models by including a variable measuring the number of elapsed years since sanctions-busting behavior has occurred and three natural cubic spline variables (Beck, Katz, and Tucker 1998). Despite the relative dearth of sanctions-busting observations, the full model’s adjusted-R2 of 16.5 percent indicates that it provides a significant improvement over simply assuming a non-busting mean. The full model makes correct positive predictions of sanctions-busting behavior 64.68 percent of the time.13

Table 3.   Logit Models of Sanctions-Busting Behavior
 PredictionModel 1Model 2Model 3Model 4Model 5
  1. Note.*,**,*** indicate statistical significance at the .05, .01, and .001 confidence level respectively for one-tailed tests. Two-tailed tests are used for the splines. Robust standard errors are reported in parentheses. The values reported as “.00” signify that the value was less than .00.

TargetDP+1.05 (.08)***.22 (.10)*  .41 (.11)***
SenderRival+1.06 (.08)***.16 (.11)  −.06 (.12)
SenderDP1.03 (.06)***1.16 (.07)***  .33 (.07)***
TargetRival.72 (.15)***−.29 (.19)  −.16 (.20)
Colony+  .73 (.13)***.62 (.14)***.59 (.14)***
JointDem+  .60 (.10)***.59 (.11)***.47 (.12)***
lnGDP3+  .82 (.02)***.75 (.02)***.71 (.02)***
TradeShare3n1+  5.95 (.35)***4.41 (.37)***4.40 (.39)***
Open3n1+  .72 (.08)***.75 (.07)***.75 (.07)***
lnGDPT  −.07 (.02)***−.12 (.02)***−.11 (.02)***
Majpower3+ 1.34 (.11)*** −.12 (.11)−.01 (.11)
lnDist −.34 (.03)*** −.30 (.03)***−.29 (.04)***
Contig+ −1.49 (.26)*** −1.35 (.27)***−1.42 (.30)***
OrgSender −.38 (.17)* .62 (.23)**.63 (.23)**
Duration+ .16 (.01)*** .08 (.01)***.08 (.01)***
Cost+ .20 (.04)*** .23 (.05)***.27 (.05)***
Overlap+ .37 (.05)*** .18 (.05)***.18 (.05)***
Yrs w/o Busting−1.56 (.06)***−1.60 (.06)***−1.49 (.06)−1.51 (.07)***−1.51 (.07)***
Cubic Spline 1 −.09 (.01)***−.08 (.01)***−.09 (.01)***−.08 (.01)***−.08 (.01)***
Cubic Spline 2 .04 (.00)*.03 (.00)***.04 (.00)***.03 (.00)***.03 (.00)***
Cubic Spline 3 −.00 (.00)−.00 (.00)−.00 (.00)*−.00 (.00)−.00 (.00)
Constant−2.60 (.04)***−1.25 (.29)***−10.49 (.30)***−7.81 (.44)***−7.79 (.47)***
N 70,77570,49963,77863,77863,778
Waldχ2 2,649.272,648.713,733.953,659.743,686.19
Pseudo R2 .

The results indicate strong support for the liberal hypotheses and minimal support for the realist hypotheses. The following section interprets the substantive significance of the variables in terms of their odds ratios, which have a more intuitive interpretation than the logit coefficients.14 While all the realist variables are significant in Model 1, the effects of rivalries wash out when controls are included. The effects of defense pacts are more robust across models, however. To begin, a defense pact between the third party and target (TargetDP) has a positive, statistically significant effect on sanctions-busting in Models 1, 2, and 5, which supports the “black knight” hypothesis (RH1). Compared to cases in which the third party and target are not defense pact allies, having such an alliance increases the odds that the third party will sanctions-bust by a factor of 1.50. Counter-intuitively, the presence of a defense pact between the third party and sender (SenderDP) is positive and significant across all three models in which it is included. In cases in which the third party and sender have a defense pact as compared to cases in which they do not, the third-party’s odds of sanctions-busting increase by a factor of 1.39. This contradicts RH3’s prediction regarding the “enemy of my friend is my enemy” proposition. It indicates that close allies of the sender are more likely to bust its sanctions than are other states. Having a rivalry with the sender (SenderRival) demonstrates a significant positive effect in Model 1, but its effect washes out in Models 2 and 5. In contrast to predictions, the presence of a third-party rivalry with the target (TargetRival) has a positive significant effect in Model 1, but the variable loses significance when controls are added. These findings indicate minimal support for RH2 and RH4.

The liberal variables are significant in the predicted manner across all three models in which they are tested, providing strong support for LH1–LH5. The presence of a past colonial relationship between the third party and target (Colony) exerts a positive, significant effect on the likelihood of sanctions-busting. Having a colonial relationship versus not increases the third-party’s odds of sanctions-busting by a factor of 1.80. Both the third party and target states having democratic institutions (JointDem) also has a positive, significant effect. The sharing of jointly democratic institutions increases the odds that the third party will sanctions-bust by a factor of 1.60 compared to cases in which one or both states are not democracies. Given their substantive interpretations, the next three variables are best interpreted by changes in their standard deviations (SD). The results show that the third-party’s trade openness (Open3n−1) positively affects its proclivity to sanctions-bust. A one-SD increase in the third-party’s trade openness from its mean value increases its odds of sanctions-busting by a factor of 1.32. The third-party state’s economic size (lnGDP3) also has a large, positive effect on its sanctions-busting behavior, with a one-SD increase from the transformed variable’s mean value increasing the odds of sanctions-busting by a factor of 3.82. Finally, the third-party’s lagged trade share (TradeShare3n−1) with the target has both statistically and substantively significant effects. A one-SD increase in TradeShare3n−1 from its mean value increases the third-party state’s odds of sanctions-busting by a factor of 1.24.

With respect to the controls, several interesting findings are revealed. While distance (lnDist) does have the negative significant effect that was predicted, the third-party state’s contiguity (Contig) with the target decreases its likelihood of sanctions-busting in all three models. The findings on distance support those of Kaempfer and Ross (2004), but the negative effects of contiguity seemingly challenge this perspective. One potential explanation for this finding is that it reflects an inherent under-reporting bias within the data used to assess sanctions-busting trade, as a shared border makes it easier for governments to under-report trade and for firms to smuggle goods in and out of the target without detection (Dadak 2003; Naylor 2001). A shared border with the target state would substantially diminish the transaction costs of using illicit methods of sanctions-busting, as opposed to using legitimate means of trade. The observed negative effect of contiguity on third-parties’ legitimate trade with the target, understood as such, implies that contiguous states do not actually stop trading with the targets, but that they are more likely to shift their trade to unobservable, illicit channels. The target’s economic size (lnGDPT) has a negative and significant effect on sanctions-busting behavior across all three models. In the full model, a one-SD increase in the target’s logged-GDP from its mean value decreases the odds of the third-party sanctions-busting on its behalf by a factor of 0.82. The third-party’s great power status has a significant effect upon sanctions-busting behavior only in Model 2, but drops out when economic factors are included.

The characteristics of the sanctions also yield interesting findings. The duration of sanctions has a positive effect on the likelihood that third-party states will sanctions-bust in all three models, with the odds of a state sanctions-busting increasing by a factor 1.08 every year sanctions are in place. The costs sanctions impose upon the sender (Cost) exert a positive significant effect in all three models. A one-unit increase in the severity of the costs imposed upon the sender by the sanctions increases the odds of third-party states sanctions-busting by 1.30 in the full model. The overlap of sanctions regimes also has a positive effect on the likelihood of sanctions-busting. In the full model, for every sanctions regime in place on either the third party or target, the odds that they will sanctions-bust on each others’ behalf increases by a factor of 1.20. This indicates that, indeed, sanctioned states are more likely to cooperate with one another to defeat sanctions. The presence of an organizational sender (OrgSender) supporting the sanctions has a negative effect on the emergence of sanctions-busters in Model 2, but its effect is significant in the opposite direction in Models 4 and 5 that includes the liberal variables. As this variable’s effects are sensitive to model specification, they should be interpreted cautiously. The positive effect that the presence of organizational senders has in Models 4 and 5 suggests that while organizations may prevent “back-sliding” in general (Drezner 2000), they do not prevent the emergence of extensive sanctions busters. By making the sanctions’ adverse economic effects on the target more severe, organizational-backing increases the commercial incentives for sanctions-busting—leading more non-cooperating states to become busters. Finally, the full model indicates that every year that the third party does not sanctions-bust in a given case (Yrs w/out Busting), the odds that it will do so in the given year decrease by a factor of .22. This relationship is significant across all five models.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

The evidence indicates strong support for the liberal explanation of sanctions-busting behavior and provides much less support for realist expectations. All five of the liberal hypotheses receive strong support from the statistical analysis, while only one of the realist hypotheses finds support. The evidence regarding the “black knight” hypotheses suggests that, in general, salient third-party alliances with the target motivate sanctions-busting more than rivalries with the sender do, thus clarifying the proposition’s substantive underpinnings. The liberal and realist interpretations on this point are not mutually exclusive, however, as the findings are also consistent with the liberal literature on trade “following the flag.” Overall, though, the results indicate that state-based security considerations do not directly affect trade flows in the way realism predicts they should. Indeed, the finding that defense pacts with the sender increase third-party states’ likelihood of sanctions-busting directly contradicts realism’s predictions. As this section explores, the liberal perspective provides not only a comparatively better explanation of sanctions-busting but also can be extended to explain the counter-intuitive findings with which realism struggles.

Analyzing the predicted probabilities that emerge from a sanctions-busting model against the predictions made by realism and liberalism provides insight into the substantive power of each theory’s account of sanctions-busting. Table 4 shows the predicted probabilities for a major power’s likelihood of sanctions-busting in four scenarios. The third-party state is categorized as interested in sanctions-busting if it has a defense pact with the target and a rivalry with the sender. The state’s interests are opposed if it has a defense pact with the sender and a rivalry with the target. Third-party firm interest is considered absent if JointDem = 0, Colony = 0, and Open3n−1, TradeShare3n−1, and lnGDP3 are set at their 25th percentile values. Firm interest is considered to be strongly present if JointDem = 1 and Colony = 1, with Open3n−1, TradeShare3n−1, and lnGDP3 set at their 75th percentile values.

Table 4.   Predicted Probabilities for a Major Power Sanctions-Busting
Firm Interest in Sanctions-BustingState Interest in Sanctions-Busting
  1. Note. Within the models, Contig = 0, Overlap = 1, Cost = 2, OrgSender = 0, Duration = 3, Yrs w/out Busting = 0, the spline values = 0, and the remaining variables are set at their means.

Not present1.24%1.47%

In Table 4’s scenarios, the situations in which firms have strong economic interests in sanctions-busting are roughly 19 times more likely to sanctions-bust than in the scenarios in which those interests are absent. The scenarios show that, substantively, the presence of state interests for or against sanctions-busting do not have the significant effects that realism predicts. Altogether, the highest probability of busting occurs when both the state’s security interests and economic interests align in favor of sanctions-busting. Tellingly, though, when state interests are opposed but firm economic interests are present, the predicted probability of a state sanctions-busting is still very high. These scenarios demonstrate significantly more support for the liberal account of sanctions-busting.

So, what can account for the findings that sender’s allies are more likely to bust its sanctions than other states? First, these findings are not without precedent. In a separate piece analyzing 12 cases of U.S.-imposed sanctions, Early (2007) finds that having a defense pact with the United States had a significant, positive effect on third-party states’ general trade flows with sanctioned states. Thus, while this study examines those states that engage in the most extensive sanctions-busting, the same effect holds true for more general trade as well. Yang et al. (2004) also find that the imposition of U.S. sanctions had significant, positive effects on Japanese and European Union trade with sanctioned target states. As Graph 1 demonstrates, France, Great Britain, Japan, and the Federal Republic of Germany were all identified as being heavily involved in sanctions-busting in the dataset. Those four states all had defense pacts with the United States, which imposed the largest number (56) of sanctions in the dataset. This is consistent with Kaplowitz’s (1998, 75–6 and 199) finding that, surprisingly given its alliance with the United States, Japan made up for a significant amount of the trade Cuba lost with the United States after the imposition of sanctions against it, which the model correctly captures.


Figure Graph 1..  Select Prolific Sanctions Busters

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Both realism and liberalism provide potential post hoc accounts of why these counter-intuitive findings might exist. Despite the results’ seeming contradiction to realism, an explanation paralleling the findings of Maoz et al. (2007, 113) may be that states manage indirect relationships in complex ways. From this perspective, the third-parties’ apparent undercutting of their alliance partners’ policies could be understood as a complex strategy of balancing. Third parties may not always consider the “enemy of their friends” to be their enemy as a matter of course. In the realist conception of the world, states must fear their friends of today almost as much as their enemies; thus, they must balance between being too successful. As such, states may seek to thwart their allies’ success in “side-project” policy ventures, just as they can sometimes give their rivals a free hand in pursuing their initiatives.

Yet, how compelling is this argument when the evidence shows that economic factors have stronger effects? In terms of its other predictions, the firm-based, liberal perspective offers much greater insight into explaining why sanctions-busting occurs. Drawing on the liberal perspective, one explanation could be that firms may have strong political incentives to relocate their business to states friendly with the sender.

To understand why a third-party defense pact with the sender could contribute to sanctions-busting, one must take into account how firms within the target, third party, and sender might respond. When the sender state sanctions the target, the sanctions punish its own domestic firms. As Morgan and Bapat (2003, 66) argue:

In many instances, the interests of the sender’s domestic actors and the sender government may in fact conflict. By preventing exchanges between sender’s domestic actors and the target, the sender’s domestic actors must forfeit the gains from exchange with the target state.

As switching to second-best trading partners entails substantial costs (Keohane and Nye 1989, 8–13), firms within the sender state may seek to continue trading with their partners in the target state, by finding alternative means of doing so. Such measures include first sending their cargo to another state and having it re-exported to the target or shifting their business to a third party and then trading with the target from that location (Morgan and Bapat 2003). Firms in sender states would likely view the option of shifting their operations to allied partner states with whom they already have close connections as the least expensive way to continue trading with the sanctioned state. Indeed, many U.S. firms relocated their commercial activities to Canada in response to the U.S. sanctions against Cuba or re-routed their trade through proxies. Such trade helped motivate the U.S. Congress to pass the “Cuban Democracy Act” in 1992, that “reimposed the Cuban embargo on foreign subsidiaries,” sparking vehement opposition from Canada and Great Britain (Rodman 2001, 114). The findings with respect to Cost further substantiate this interpretation: as the costs to the sender rise in imposing sanctions, so does the likelihood of sanctions-busting. The greater the economic disruption within the sender state, the larger the incentives are for their own firms to find other states in which or through which to continue trading with the target.

Firms are both sensitive to how politics affects their trade and themselves skilled practitioners of it. If economic interests within allied states can prevent their governments from participating in the sanctions, those firms should have greater protection from potential retaliatory actions by the sender state. This is because the sender state will be constrained in the types of punitive actions it can impose against its allies. Firms from both the sender state and other third-party states then have strong incentives to relocate to allies of the sender state with which they have established connections. Having established presences in those states is also potentially important because they can lobby those states to adopt favorable policies.15 For sanctions-busting firms, and especially multinational corporations that do business with the sender, it should be much safer to move their business to states with which the sender has close relations than those at odds with the sender. This strategy provides the firms with political cover, and diminishes the likelihood of punitive action on the part of the sender against the third-party state, causing further disruptions.

The potential strength of this account is born out in the vociferous opposition of the Canadian, Mexican, and European governments in response to the United States’“Helms-Burton Act” (1996). This congressionally mandated measure imposed secondary, coercive sanctions directly on firms from third-party states investing in Cuba. As the chief U.S. negotiator responsible for responding to the political storm created by the act recalled: “[we] confronted demonstrations in Canada, a raft of tomatoes thrown at us in Mexico City and a stone wall of resistance in Europe” (Eizenstat 2004, 5). Indeed, the United States was forced to waive the coercive restrictions it attempted to use against firms in these allied states, due to the strength of the third-party states’ responses in leveraging their relations with the United States (the sender) on behalf of their economic constituencies (Eizenstat 2004, 5–6). The liberal account explains why, given their incentive-structures, profit-seekers can be expected to go to whichever states offer them the best prospects of profitably trading with sanctioned states, be they pariahs or the senders’ friends next door. While more fine-grained inquiries focusing on the explicit strategies taken by sanctions-busting firms are warranted, liberalism provides a compelling first-cut explanation for these otherwise puzzling findings.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

This study has sought to identify which states are engaging in sanctions-busting and explain the motivations behind their behavior. Two competing theories, one based on a realist, state-based paradigm, and the other on the liberal, firm-based paradigm, have been developed and tested against one another. Through first developing a measure to codify sanctions-behavior and then testing the theories with a large-n analysis, the results provide compelling evidence regarding both the motivations behind sanctions-busting and the profile of sanctions busters.

Overall, the evidence offers the strongest support for the liberal theory of sanctions-busting. The liberal theory provides a much richer account of sanctions-busting then simply assuming that if the opportunity to sanctions-bust exists, states will uniformly seek and be able to exploit it. While the “black knight” theory proposed by Hufbauer, Schott, and Elliot (1990) receives some support, realism offers only a limited explanation of sanctions-busting. As the broader results show, viewing sanctions-busting as the product of calculated responses by profit-driven firms offers the most complete explanation of third-party behavior. This perspective further provides a coherent explanation for the otherwise puzzling behavior of states that seemingly undermine their individual security interests by sanctions-busting.

The findings also have interesting implications for states considering whether or not to impose sanctions. The results indicate that a sender state’s closest allies are more likely to bust its sanctions than are its rivals. Instead of solely focusing the blame for sanctions’ failures on their enemies, senders should be wary of their allies, as well. The results also suggest that the most influential predictors of sanctions-busting amongst third parties are factors that senders can do little about: the third party’s GDP, trade openness, and its having a strong pre-existing commercial relationship with the target. In concert with findings regarding the senders’ allies, the results of this study lend little support to the notion that senders have been able to deter severe sanctions-busting from occurring in meaningful ways. The good news is that large-scale sanctions busters are relatively rare, but this is balanced by the fact that it may take only one sanctions-buster to hurt a sanctions regime’s likelihood of success. For policymakers considering the imposition of sanctions, this study indicates that measures targeting firm-behavior may be better suited for encouraging compliance; however, third-party states can and do use their leverage to protect the interests of their economic constituencies (Eizenstat 2004; Rodman 2001). The results also reveal that firms within allies of the sender state, and potentially firms from the sender and other third parties, can leverage the allies’ privileged positions with the sender in using them as bases for sanctions-busting.

Moving forward, several avenues of research merit pursuit. First, future studies should explore how the post-Cold War environment potentially changes the incentives for firms to trade with states targeted by sanctions. It is likely that globalization has only increased their freedom of movement and ability to sanctions-bust. Second, the method of identifying sanctions busters within this piece can be used to create a more nuanced measure of the “black knight” variable that has been predominately used within the sanctions literature to capture the effects of third-party assistance. By coding sanctions busters individually, studies examining sanctions success may be better able to capture the effects that assistance given to the target has on sanctions’ success. Third, future work could specifically examine what the political consequences of sanctions-busting are for third-party states’ relations with both the sender and the target. A better understanding of the degree to which other states hold sanctions busters accountable for their behavior could further enrich the account provided here. Finally, this study sheds light on the counterintuitive effects of indirect relationships on state behavior. Future research exploring these relationships should also be willing to break down the unit of analysis from the state level to the firm or individual level to provide insight on the complex nature of the causal processes at work.

  • 1

    Within much of the literature addressing the issue of economic sanctions’ success, “success” constitutes the “the extent to which the policy outcome sought by the sender country was in fact achieved…,” and the degree to which the sanctions factored into that outcome (Hufbauer, Schott, and Elliot 1990, 41). To the extent that third-party sanctions-busting helps target states ameliorate the costs that sanctions impose, it can diminish the sanctions’ likelihood of success (Martin 1992, 3–4). This study does not directly address how sanctions-busting affects the success of sanctions, but other studies using the “black knight” variable find that third-party assistance has a negative effect on sanctions’ success (Martin 1992; Lektzian and Souva 2007). Studies conducted by Drury (1998) and Drezner (2000), however, do not find the negative relationship to be statistically significant. Discussions within this piece on the negative effects of sanctions-busting on sanctions’ success draw on this mixed body of findings. That the findings have not been conclusive suggests that a better conceptualization of sanctions-busting is needed.

  • 2

    Implicit within this definition, though, is the notion that the extent to which the sender is willing to incur costs in inflicting economic losses on the sanctions’ target can be explained—at least in part—by how much the sender values achieving the objectives for which the sanctions were imposed.

  • 3

    Most studies designate a “primary-sender” state as being responsible for the sanctions’ imposition, even if multiple parties subsequently join the regime (Hufbauer, Schott, and Elliot 1990). Third-party states are considered all states besides the target and primary sender.

  • 4

    Though illicit trade plays an unobservable role in this study of sanctions-busting, it does come into play on a conceptual level in affecting those instances in which trade must move to illicit channels to remain profitable (Naylor 2001). In some cases in which legitimate trade declines, such trade may have just moved to the black market (e.g., smuggling) rather than having stopped. Yet in terms of the effects that governments’ security prerogatives have on their states’ legitimate trade with the target, the expected declines should be evidenced even if illicit trade continues to occur.

  • 5

    Another potential category would be if the states’ interests were ambivalent. Realism does not make a specific prediction as to how the state should act if its interests are ambivalent, and liberalism makes the same predictions in both cases. Empirically, tests were run using the ambivalent typology, which produced slightly lower predicted values than the cases in which the third-party’s security interests were opposed. The findings, though, were consistent with liberalism’s predictions. As such, there is little theoretical or empirical reason to include the ambivalent category.

  • 6

    The case data comes from a dataset produced by Drury (1998) using Hufbauer, Schott, and Elliot’s 1990 cases. Cases in which a primary-state sender was augmented by organizational senders are coded with the state as the primary sender. The independent variable OrgSender denotes the presence of additional organization support in these cases. In the three cases with multiple primary state senders (U.K. & U.S.A. v. Uganda; Japan, West Germany, & U.S.A. v. Burma; and U.S.A. & U.K. v. Somalia), the state that imposed the greatest costs upon the target is coded as the primary sender.

  • 7

    Due to the emergence of new states this was not always possible and more limited measures had to be taken for pre-sanctions trade growth. If only 2 years of previous observations were available, then an average of the previous 2 years was used. If only the previous year was present, that observation was used. If the state became independent during the course of the sanctions regime, the third-party-target trade index was set at 0.

  • 8

    The author will provide additional materials to demonstrate the robustness of similar measures upon request.

  • 9

    Early (2007) develops an alternative method of measuring sanctions’ effects on third-party trade with the target. The author uses a gravity model of international trade to develop counterfactual predictions of what third-parties’ trade would have been with the sanctioned states had the sanctions not occurred. This measure is then compared to the trade that occurred during the sanctions to get a differential value for whether trade was greater or less than predicted. This method allows for cumulative changes in third-party-target trade flows to be evaluated over time; however, the method is used to provide a measure of general responses to the imposition of sanctions and its conclusions are tied to the quality of the counter-factual predictions developed. As such, it is not as appropriate for studying the emergence of extensive sanctions busters.

  • 10

    This study only accounts for recorded and documented legitimate trade, which has certain biases. Not all sanctions-busting trade is stigmatized by the international community, but a significant portion of it occurs within the grey nebula of legality (Naylor 2001). Data on “legitimate” sanctions-evading trade could be biased, as it may be in the third-parties’ interest to cover up the trade or doctor the trade figures they report (Dadak 2003). While this may bias the data against finding sanctions-busting cases, it does not invalidate those cases in which it is found to have occurred.

  • 11

    In the full model data set, third-party states are flagged as import sanctions busters 1,048 times, 999 times for being export sanctions busters, and in 552 cases the state was a sanctions-buster for both.

  • 12

    Though these sanctions cases are not included within the analysis, Overlap includes sanctions cases in which organizational senders imposed sanctions upon the target or third party during the given year. These cases are: UN v. S. Africa, UN & Org. of African Unity v. Portugal, Arab League v. U.S.A. & Netherlands, Arab League v. Egypt, Arab League v. Canada, and EC v. Turkey (Hufbauer, Schott, and Elliot 1990).

  • 13

    The results hold true even if the high-leverage sanctions cases (U.S.A. v. N. Korea, U.S.A. v. Vietnam, and U.S.A. v. Cuba) are excluded.

  • 14

    The odds-ratios discussed below are calculated using Model 5 and Long and Freese’s (2006, 177–78) suite of S-Post commands. All the odds-ratios reported in the discussion assume that only the variable in question is being changed and that all other variables in the model are held constant.

  • 15

    I thank an anonymous reviewer for suggesting this point.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix
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  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Explanations for Sanctions-Busting
  5. Operationalization, Data, and Methodology
  6. Discussion
  7. Conclusion
  8. References
  9. Appendix

Appendix A: Observed and Predicted Sanctions-Busting Cases

Sanctions Case: Sender v. TargetStart YearEnd YearTotal No. of ObservationsIdentified as SBsPredicted SB Cases
  1. Note. Summary statistics for the sanctions busters reported in the table are from the observations contained in the full model. Case Source: Drury (1998).

U.S.A. & UN v. N. Korea195019904165102114
U.S.A. v. Iran19511953 145  1  0
USSR v. Australia19541955 152  6  0
India v. Portugal19541961 648 29  1
Spain v. U.K.195419843527 93 94
U.S.A. v. N. Vietnam195419904332159 70
U.S.A. v. Israel19561960 392  6  3
U.S.A. v. U.K. & France19561957 305 10  4
U.S.A. v. Laos19561962 580 10  0
USSR v. Finland19581959 159  4  0
U.S.A. v. Dominican Republic19601962 286  5  0
USSR v. China196019701240 38  2
U.S.A. v. Cuba196019903853 53 28
USSR v. Albania19611965 458 19  0
U.S.A. v. Brazil19621964 271  3  0
USSR v. Romania19621963 180  0  0
U.S.A. v. United Arab Republic19631965 322 13  0
Indonesia v. Malaysia19631966 437 8  2
U.S.A. v. Indonesia19631966 43715  0
U.S.A. v. S. Vietnam19631964 213 3  0
France v. Tunisia19641966 342 4  0
U.S.A. v. Chile19651966 220 9  0
U.S.A. v. India19651967 349 2  0
U.K. & UN v. Rhodesia19651979132646 18
U.S.A. v. Peru19681969 238 6  0
U.S.A. v. Chile19701973 50514  0
U.S.A. v. India and Pakistan19711972 257 6  0
U.K. & U.S.A. v. Uganda19721979 90916  5
U.S.A. v. S. Korea19731977 659 7  4
U.S.A. v. Chile19731990234661 37
U.S.A. v. Turkey19741978 65414  9
Canada v. India19741976 39815  5
Canada v. Pakistan19741976 39814  6
U.S.A. v. USSR19751990214060 53
U.S.A. v. S. Africa19751982106913 11
U.S.A. v. Uruguay19761981 78618  9
U.S.A. v. Taiwan19761977 264 0  1
U.S.A. v. Ethiopia19761990197952 31
U.S.A. v. Paraguay19771981 65531  9
U.S.A. v. Guatemala19771986131012  4
U.S.A. v. Argentina19771983 91726 12
Canada v. Japan & EC19771978 271 3  4
U.S.A. v. Nicaragua19771979 393 3  0
U.S.A. v. El Salvador19771981 65517  0
U.S.A. v. Brazil19771984104816  0
China v. Albania19781983 78629 9
U.S.A. v. Brazil19781981 52415 0
U.S.A. v. Argentina19781982 65518 5
U.S.A. v. India19781982 6702316
U.S.A. v. USSR19781980 3931412
China v. Vietnam1978198814752515
U.S.A. v. Libya1978199017413234
U.S.A. v. Iran19791981 407 8 2
U.S.A. v. Pakistan1979199016303819
U.S.A. v. Bolivia19791982 53912 9
U.S.A. v. USSR19801981 270 812
U.S.A. v. Iraq1980199014953023
U.S.A. v. Nicaragua1981199013593726
U.S.A. v. Poland19811987 952 9 7
U.S.A. v. USSR19811982 272 611
U.K. v. Argentina19821983 272 4 3
S. Africa v. Lesotho19821984 408 7 3
Australia v. France19831986 5441615
U.S.A. v. USSR19831984 272 4 8
U.S.A. v. Zimbabwe19831988 817 0 1
U.S.A. v. Iran19841990 95117 8
U.S.A. v. S. Africa19851990 8152614
U.S.A. v. Syria19861990 6791613
U.S.A. v. Angola19861990 67916 7
U.S.A. v. Panama19871990 543 4 9
U.S.A. v. Haiti19871990 543 2 1
U.S.A. v. El Salvador19871988 273 6 1
Japan, W. Germany, & U.S.A. v. Burma19881990 407 8 3
U.S.A. & U.K. v. Somalia19881990 407 9 7
India v. Nepal19891990 270 6 5
U.S.A. v. China19891990 270 2 2
U.S.A. v. Sudan19891990 270 6 5