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⁸ This result may be due in part to selection bias that results from only examining realized cases of economic sanctions. There may have been cases in which such asymmetry did not obtain, no state took the lead in imposing sanctions (in hopes that someone else would), and no sanctions were imposed. Given this kind of collective action problem, it is possible that sanctions never get organized without a leading sender even if they are Pareto-superior to no action. If this is so, situations without a leading sender will never make it into a data set on economic sanctions, which yields the empirical pattern found in studies of sanctions.

⁹ Baldwin (fn. 1), 174–89.

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²¹ Firms lobbied their representatives heavily to decrease the stringency of sanctions, which suggests that escalating the conflict to a trade war would not receive legislative support. In fact, bills were introduced calling for an end to the unilateral U.S. measures. See Congressional Record, 97th Cong., 2d sess., July 22,1982, p. E3429; Congressional Quarterly, August 14, 1982, p. 1961; Congressional Record, 97th Cong., 2d sess., September 29, 1982, pp. H7927–30; Congressional Quarterly, October 2, 1982, p. 2467.

²² For a rigorous development of a similar argument about audience costs in the context of deterrence and the original development of the concept of audience costs, see James D. Fearon, “Deterrence and the Spiral Model: The Role of Costly Signals in Crisis Bargaining” (Paper presented at the annual meeting of the American Political Science Association, San Francisco, August 30-September 2, 1990).

²³ Paarlberg (fn. 2).

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²⁸ However, when state interests are not as conflictual as in the case under consideration here, “cheap talk” may have a significant effect on outcomes. See Morrow, James D., “Modeling International Regimes” (Paper presented at the annual meeting of the American Political Science Association, San Francisco, August 30-September 2, 1990).Google Scholar

²⁹ The model developed here focuses on a rational-choice approach to costs and credibility. This subject has also received attention from those adopting a psychological approach. See Jervis, Robert, The Logic of Images in International Relations (Princeton: Princeton University Press, 1970)Google Scholar; Larson, Deborah Welch, “Order under Anarchy: The Emergence of Convention in U.S.-Soviet Relations” (Paper presented at the annual meeting of the American Political Science Association, Atlanta, Ga., August 31-September 3, 1989)Google Scholar; Hermann, Margaret G., Hermann, Charles F., and Hutchins, Gerald L., “Affect,” in Callahan, Patrick, Brady, Linda P., and Hermann, Margaret G., eds., Describing Foreign Policy Behavior (Beverly Hills, Calif.: Sage Publications, 1982).Google Scholar

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⁴⁰ This game could also be modeled with State A deciding whether to make side payments if B does impose sanctions. The equilibria are similar to those in this threat game.

⁴¹ This is one conclusion that clearly differs from what we would find in a signaling game. With incomplete information, one usually finds some equilibria that include the carrying out of threats.

⁴² New YorK Times, September 1, 1982, p. A1.

⁴³ Although it is the most comprehensive descriptive study of sanctions to date, Economic Sanctions Reconsidered (fn. 3) has been criticized for some of its methods. Other students of economic sanctions have questioned in particular the definition and measurement of success. Hufbauer, Schott, and Elliott define success narrowly as a desired policy change in the target country; they constructed a 16-point scale that captures varying degrees of success. Baldwin, for one, disagrees with this definition; see Baldwin (fn. 1), 130–34. The 16-point scale also takes into account the contribution of sanctions to the observed change in policy, which perhaps is better left as a separate variable. Economic Sanctions Reconsidered also exhibits some weaknesses from a statistical perspective. Because the 16-point success scale is constructed by the multiplication of two 4-point scales, it is impossible for any case to achieve certain values, such as 7, 13, 14, and so forth. The results also do not meet the usual standards of statistical significance and examine only bivariate rather than multivariate relationships, so that correlations among the independent variables make the observed relationships unreliable. In this study, I do not use the success score but use only Hufbauer, Schott, and Elliott's measures of some independent variables, along with additional variables added to their data set. I also develop statistical techniques appropriate for the kinds of data used, rather than relying on bivariate correlations.

⁴⁴ Note that COST is not intended as a direct measure of audience costs. Instead, it is an indicator of the strategy adopted by the leading sender, with higher levels of COST indicating a higher-cost strategy. This is assumed to lead to higher audience costs in case of reneging.

⁴⁵ Hufbauer, Schott, and Elliott (fn. 3), 1:35

⁴⁶ McKelvey, Richard and Zavoina, William, “A Statistical Model for the Analysis of Ordinal Dependent Variables,” Journal ofMathematical Sociology 4, no. 1 (1975).Google Scholar

⁴⁷ Ordered probit assumes that the dependent variable, cooperation, is an unobserved linear function of the independent variables. While the precise level of cooperation is unobserved, ordered probit assumes that we can split the cooperation continuum into four parts and observe in which of the four each case lies. See King, Gary, Unifying Political Methodology: The Likelihood Theory of Statistical Inference (New York: Cambridge University Press, 1989), 115–17.Google Scholar Thus, the third column in Table 2 refers to the expected score on the unobserved cooperation scale, while the fourth column translates this score into an expected value for the observed variable COOP.

⁴⁸ King, Gary, “Event Count Models for International Relations: Generalizations and Applications,” International Studies Quarterly 33 (June 1989).CrossRefGoogle Scholar

⁴⁹ King (fn. 47), 218–20.

⁵⁰ Ibid., 126–29.

⁵¹ These values are calculated by substituting the estimated parameters and specified values of COSTD and INST into the systematic component of the negative binomial model as given in Equation 1.

⁵² As discussed above, the positive coefficient of gamma indicates positive contagion, thus making precise predictions of the expected number of cooperating countries impossible. This is reflected in large confidence intervals around the expected value of NUMBER. For example, in all cases in Table 4, the lower bound of a 95% confidence interval would include NUMBER = 1. Thus, the fitted values should not be taken as precise estimates but only illustrative of the logic of the event count model. More important for interpretive purposes are the estimated parameter coefficients.

⁵³ Martin (fn. 14), chaps. 5–8.