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First published online April 1, 2008

Power or Plenty: How Do International Trade Institutions Affect Economic Sanctions?


Does the dramatic rise of the number of preferential trade agreements (PTAs) worldwide make economic sanctions more likely through increasing the leverage of the powerful and pitting states against each other in competition (power) or less likely through increasing the benefits of trade, resolving disputes, and promoting like-minded communities (plenty)? The authors offer the first systematic test of these propositions, testing hypotheses on sanctions onset using a data set of episodes from 1947 through 2000. In favor of the plenty argument, increases in bilateral trade do decrease sanctioning behavior; in favor of the power argument, an increase in the potential sanctioner's GDP or centrality in the network of all PTAs make sanctioning much more likely. However, mutual membership in PTAs has no direct effect on the propensity of states to sanction each other.

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1. On centrality in networks and its advantages in terms of access, information, coalition building, and power, see the general explanations in Wasserman and Faust (1997) and Scott (2000).
2. Fifty-one of the 226 cases in our data set are listed as having more than one primary sanctioner; 20 of these are ad hoc coalitions.
3. On in-group and out-group behavior and competition in networks, see Levine and Moreland (1998) and Burt (1987).
4. For studies of regionalism at the domestic level of analysis, see Busch and Milner (1994) and Mansfield and Milner (1997). For a study of economic sanctions at the domestic level of analysis, see Hiscox (n.d.).
5. There is some question as to whether economic sanctions are a precursor to or substitute for military intervention. We are currently writing on the matter elsewhere and do not address that topic here for brevity.
6. For a nice overview and critique of these debates, see Drezner (2000).
7. For a response to Hufbauer et al. (HSE; 1990), see Pape (1997) and Morgan and Schwebach (1997). For more general discussions of the ineffectiveness of sanctions, see Galtung (1967), Hoffmann (1967), Knorr (1977), Renwick (1981), and Daoudi and Dajani (1983).
8. For accounts of the multilayered sanctions on Iran, see Perkovich and Manzanero (2004); for North Korea, see Lee (2003).
9. However, Hafner-Burton and Montgomery (2008) show that this finding is an artifact of U.S. hegemony; most sanctions are initiated by the United States, and it is unlikely to target another democracy. Other democracies, by contrast, are not so reticent to sanction democracies.
10. In 1990, the United States placed an embargo on the import of Mexican tuna, even though an international tribunal for the General Agreement on Tariffs and Trade ruled that sanctions were a violation of international law.
11. For example, article 96 of the Cotonou Agreement between the European Union and the countries of Africa, the Caribbean, and the Pacific (ACP countries) lays out a sanctions procedure in the event of human rights violations. ``Appropriate measures'' can be put into effect to punish a member state that has violated the human rights clause of the agreement only after formal consultations have taken place. Full-blown sanctions are restricted to measures of last resort.
12. Bearce (2003) and Schiff and Winters (1998) similarly assume that trade between neighboring states creates trust and reduces the likelihood of conflict and that international organizations can serve a trust-building function. See also Schiff and Winters (2002).
13. U.S. sanctions against Mexico in the tuna/dolphin case is an example (Runge, Ortalo-Magné, and Vande Kamp 1994).
14. Our thanks to Dan Drezner for bringing this issue to our attention.
15. See Boehmer, Gartzke, and Nordstrom (2004, 5-6) on the complex relationship between institutions and conflict.
16. Barbieri has shown that open trade before World War II increased the likelihood of observing military disputes among partners (Barbieri 2002, 1996).
17. Centrality (specifically, degree centrality) is proportional to the number of institutional ties received by a state; a state accordingly is highly central if many other states have institutional ties to it. For a complete theoretical and mathematical treatment of these measures, see Wasserman and Faust (1997); for an application to the IO network, see Hafner-Burton and Montgomery (2006) and Dorussen and Ward (2008).
18. Traditional sample limitations to politically relevant dyads in the study of war are not appropriate here, as almost all states trade with all other states, making sanctions possible. We choose to use all dyads instead of the ``sanctions-relevant'' dyads used by Cox and Drury since we observe several instances of sanctions among non-sanctions-relevant dyads or dyads with low levels of trade as well as multiple (redundant) layers of sanctions on some states. We replicate on this limited sample as a robustness check.
19. An alternative unit of analysis is the symmetrical dyad year, which allows analyses of mutual behavior of two states with each other, for example, total trade or democracy between a pair of states. Directed-dyad years are common in the study of economic sanctions and are the appropriate unit from which to test our directed hypotheses.
20. Oneal and Russett's (1999) study gives full details of their model specification and their results; both manuscript and data are available online at
21. Data were collected by Emilie Hafner-Burton using sources from the World Trade Organization (Smith 2000; Schott 2003), among other sources. We would like to thank Ed Mansfield, John Oneal, Jon Pevehouse, and Bruce Russett for generously sharing their data. Additionally, we note that trade institutions exhibit a great deal of institutional variation. Ideally, any study comparing their effects on conflict would also include information about varying institutional qualities, such as dispute settlement mechanisms or security aims. Unfortunately, these data are simply not available for most trade institutions. Following Mansfield and Pevehouse (2000) and Oneal and Russett (1999), we consequently adopt the simplifying assumption that trade institutions can be analyzed as if they supply homogenous institutional qualities across agreements.
22. We thank Nikolay Marinov for providing us with our original dyadic version of the HSE data updated through 2000, including corrections from Dan Drezner; we have since extended it to include every case in the forthcoming third edition of the HSE book (Elliott et al. 2007).
23. Due to a lack of GDP data before 1950, lagging the data, and including GDP in every model, we effectively test the period 1951—2000.
24. King and Zeng (2001b, 2001a) have shown in the case of rare events that logit coefficients are commonly biased in substantively meaningful ways, even in large samples: estimated event probabilities are always too small. We correct for these biases accordingly. Since fixed-effects models are inappropriate for rare-events models (introducing a theoretical assumption that all-zero dyads tell us nothing about sanctions), we accordingly use rare-events logit instead. See King (2001) on rare events and fixed effects. Clustering by sender instead of by dyad resulted in similar results.
25. One case of double sanctions onset (U.S. sanctions on Peru, 68-1 and 68-2) is treated as a single onset. By splitting cases into multiple separate episodes and including actors discarded by HSE as per Drezner's (2000) and Marinov's (2003) recommendations, 15 cases are added to HSE's expanded list of 211 (dyadic) cases.
26. In the few cases where institutions were sanctioning its own members, we used the state with the second highest Composite Index of National Capability score; we used the same coding rules if sanctions targeted an institution.
27. U.S. domination of the data set may be due to many causes, which are dealt with elsewhere, such as the U.S. role in establishing and maintaining the international political and economic order and the ease of implementing sanctions (Carter 1987; Hafner-Burton and Montgomery 2008).
28. For the subset of matching dyads between our samples, our PTAij is highly correlated with their variable ( ∼ 0.84).
29. Data measuring preferential trade agreement (PTA) integration are available only for a smaller sample of PTAs than the sample that we use for our analyses; while we observe more than 170,000 observations of joint membership in PTAs in our sample, the integration sample records just more than 120,000 observations. This difference in sample size makes comparison difficult. We thank Jon Pevehouse for generously sharing these data.
30. The term nonreciprocal refers to the structure of a trade agreement that offers one-way access of a state party to the negotiated market of another state or trading entity.
31. The goal of a free-trade area is to facilitate easier trading within the area. These agreements prohibit internal tariffs among members, although each member country keeps its own external tariff policies.
32. A customs union is a free-trade area with common external tariff policies on goods imported from countries outside the union.
33. A common market is a customs union with removal of restrictions on the free flow of capital, labor, and technology between members.
34. For additional research on international organizations and social networks, see Dorussen and Ward (2008 [this issue]) and von Stein (2008 [this issue]).
35. The correlation matrix is available in the appendix.
36. Correlation between Trade ij—1 and PTAij— 1 × Tradeij—1 is high, inflating the associated standard errors reported in Table 1. We therefore run models 1 and 2 without PTAij—1 × Trade ij—1 as well; our results are robust. Dyads linked by mutual ties to trade institutions are neither more nor less likely to sanction than other dyads.
37. In contrast to Hafner-Burton and Montgomery's (2006) research on intergovernmental organization (IGO) social networks and militarized disputes, we found that states were more likely to enact sanctions against other states in the same structurally equivalent cluster. This is consistent with a logic of competition among peers rather than a logic of in-group favoritism. Similarly, states that were highly central in the PTA network were more likely to enact sanctions, whereas for dyads in the IGO network, large differences in centrality tended to dampen militarized disputes. In both IGO and PTA networks, particularly large clusters tended to increase conflict (Hafner-Burton and Montgomery 2006).
38. We evaluate all variables in our base model (Table 1, column 1, excluding interaction terms) at their means, with the binary variables PTAij—1 , PTAClusterSameij—1, and Allies ij—1 at their medians (zero in all three cases).
39. We thank Helen Milner for suggesting this possibility.
40. Additionally, we have limited our sample to Cox and Drury's (2006) sanctions-relevant dyads in order to ensure consistency, although as we have argued, we do not believe this sample is appropriate because sanctions do take place within nonrelevant dyads. Our substantive results on this reduced sample are identical. We have also replaced our binary PTAij variable with a continuous measure to see whether a greater number of mutual memberships in PTAs affects dyads' likelihood of sanctions and find no substantive change in our results. We have replaced our binary Alliesij variable with several binary measures to see whether formal mutual defense treaties, neutrality pacts, or ententes affect the likelihood of sanctions independently. We find no such evidence.
41. States in the EU are also more likely to sanction, although less so than the United States. When we included an EU sender dummy, our centrality results lost statistical significance due to the high correlation (0.75) between the two variables.
42. This premise stands in contrast to views that international organization have little effect on international relations between states (Mearsheimer, 1994/95).


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  1. economic sanctions
  2. preferential trade agreement (PTA)
  3. liberal peace
  4. social networks

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Emilie M. Hafner-Burton
Woodrow Wilson School for Public and International Affairs Department of Politics Princeton University
Alexander H. Montgomery
Department of Political Science Reed College

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