In order to keep the exposition as clear as possible, the theory section has used a single subscript to denote targets. The remainder of the paper uses two subscripts to denote the actor, the target and the third party. As illustrated in Figure 2, and discussed in the theoretical model section, an increase in trade between an actor and target #3 (assuming target #3 is the third party) will influence the conflict between the actor and target #2. Conflict will decrease if target #2 and target #3 are friends, while conflict will increase if target #2 and Target #3 are rivals. A similar relationship is likely to hold if conflict changes between the actor and target #3.
A change in conflict between the actor and target #3 will influence the conflict between the actor and target #2 according to whether targets #2 and #3 are friends or rivals. In other words, the empirical work will test a broader interpretation of the hypothesis
Figure 2 Third Party Interactions
that considers conflict and trade as measures of whether the actor and target #3 are becoming more or less friendly.
We develop a measure of trade (conflict) between the actor and target #3 (X13, M13, Z13) and a measure of whether target #2 and target #3 are friends or rivals (Z23).If the target’s net conflict towards the third party is greater than zero (Z23>0), it is assumed the target and third party are rivals. If the net conflict is less than zero (Z23<0), then the target and third party are friends.18 The test will treat each country as a third party. Thus for each dyad there are 28 observations. I would apply ordinary least squares (OLS) multivariate regressions to test the hypotheses. First I examine the actor-third party relationship based on conflict and equation (8) is performed.19
We use the target’s conflict towards the third party to measure whether they are friends or rivals. One could also use the third party’s conflict towards the target.
where Z12＝ the frequency of net conflict from actor country towards the target country;
Trade12＝ exports from the actor country to the target country (X12), or imports of the actor country from the target country (M12);
A1＝ a vector of actor country attributes;
A2＝ a vector of target country attributes;
Z13＝ frequency of net conflict initiated by the actor country towards the third party;
Z23＝ frequency of net conflict initiated by the target country towards the third party;
＝ a random error term normally distributed with mean zero.
The strategic (commodity) trade will cause different levels of conflict, for example one can see Solomon W. Polachek and Judith McDonald,
“Strategic Trade and Incentives for Cooperation,” in Disarmament, Economic Conversion and the Management of Peace, ed. M. Chatterji and L. Forcey (New York: Praeger, 1992) and Mansfield, Edward D.
and Jon C. Pevehouse, “Trade Blocs, Trade Flows and International Conflict,” International Organization, No.54(2000), pp.775-808. For causality problem between conflict and trade, please see Solomon W.
Polachek, “Conflict and Trade,”op. cit.; Mark Gasiorowski and Solomon Polachek, “Conflict and Interdependence,”Journal of Conflict Resolution, No.26(December, 1982), pp.709-29; Katherine Barbieri, “Economic In-terdependence: A Path to Peace or a Source of Interstate Conflict?” op.
cit. and Rafael Reuveny and Heejoon Kang, “International Trade, Political Conflict/Cooperation, and Granger Causality,” American Journal of Political Science, No.40(1996), pp.943-970.
Country attributes are considered to be exogenous and are used to identify the equations. The coefficients of country attributes can be thought of as other aspects of a price vector for conflict. The intercept terms reflect levels of conflict that would result independently of attributes and trade. In this study, we neglect the impact of country attributes and concentrate solely on the signs and magnitudes of the coefficients of third-party trade and net conflict.20
First, the coefficients of X12 and M12 in Table 1 are negative and match the neoliberalists’ view which claims that trade promotes peace between countries.21 The results in column (1) of Table 1 show that, when controlling for exports, an increase in conflict from the actor towards other countries (Z13) reduces actor to target conflict. Increases in conflict from the target towards other countries (Z23) increase actor to target conflict. In column (2) we control for imports and find that actor-third party and target-third party conflict increase actor-target conflict. The above results provide some evidence that actor to target conflict depends on the actor’s conflict towards other countries and the target’s conflict towards other countries. We include an interaction between Z13 and Z23 to explicitly test the proposition. The interaction will show whether the change
For more references, see Solomon W. Polachek, “Conflict and Trade,”
op. cit.; Solomon W. Polachek, “Why Democracies Cooperate More and Fight Less: The Relationship Between International Trade and Cooperation,”
Review of International Economics, No.5(1997), pp.295-309, Many thanks to a referee’s notification.
For a detailed test, see Solomon W. Polachek, “Why Democracies Cooperate More and Fight Less: The Relationship Between International Trade and Cooperation,” op. cit., p.303.
in actor to target conflict due to a change in the actor’s conflict towards other countries depends on the relationship between the target and other countries. The results in column (3) are statistically significant and somewhat consistent with the proposition.22
From column (3):
Thus an increase in conflict with target #3 has an impact on conflict with #2, and the strength of this relationship depends on the relationship between the target and the third party when controlling for exports.
In the theoretical section, we consider four different maxims based on whether countries #2 and #3 are friends or rivals. It is worthwhile discussing how consistent the results are with the theory.
First, consider the case where we have an increase in Z13, implying that countries #1 and #3 are becoming less friendly or more conflictual. This will influence the conflict between countries #1 and #2 depending on the relationship between countries #2 and
#3. If Z23 is positive, this implies that countries #2 and #3 are rivals and an increase in conflict between countries #1 and #3 will lead to a decrease in conflict between countries #1 and #2. This is consistent with the maxim “a rival of a rival is a friend”. If Z23 is negative, this implies that countries #2 and #3 are friends,
The results in column (4) controlling for imports will not discuss as the coefficient of Z13 is insignificant.
but Z23 must be less than– 3.91 to result in an increase in conflict between countries #1 and #2. This is somewhat consistent with the maxim “a friend of a rival is a rival”.
Let us consider the case where Z13 is decreasing, implying that countries #1 and #3 are becoming friendlier. Again if Z23 is positive, this implies they are rivals and a decrease in conflict between countries #1 and #3 leads to an increase in conflict between countries #1 and #2. This is consistent with the maxim “a rival of a friend is a rival”. Lastly, Z23 being negative (again Z23 must be less than– 3.91) leads to a decrease in conflict between countries
#1 and #2. This is somewhat consistent with the maxim “a friend of a friend is a friend”.
Separate regressions controlling for Z13 are estimated for observations where the target and third party are friends or rivals.
As stated earlier, when the target and third party are friends (Z23<0), we expect the coefficient of Z13 to be positive, and when the target and third party are rivals (Z23>0), we expect it to be negative. The empirical results are provided in Table 2. In column (1) the target and third party are friends and when controlling for exports, we find a positive, but insignificant relationship between actor to third party conflict (Z13) and actor to target conflict(Z12). A positive and significant relationship is found in column (2) when controlling for the imports and matches our prediction, because the actor will increase conflict towards the target as actor-third party conflict increases when the target and third party are friends. Columns (3) and (4) look at cases where the target and third parties are rivals.
Column (3) controls for actor’s exports and shows that an increase in actor-third party conflict significantly reduces actor-target conflict.
Column (4) finds similar results when controlling for actor’s imports.
These results in both columns are consistent with the proposition.23 The hypotheses are actually based on the relationship between the actor and target #3 being measured by trade, not conflict. Thus we again perform the above analysis using trade to measure the actor-third party relationship (i.e., X13 and M13)and equation (9) is estimated.
whereTrade13＝ exports from the actor country to the third party (X13), or imports of the actor country from the third party (M13).
Table 3 includes interactions between actor-third party exports/
imports and target-third party conflict. The positive and significant coefficients of X13 may indicate that the existence of alternative markets for the actor reduces the costs of a conflict with a given target. Column (2) includes an interaction between X13 and Z23, however the coefficient is insignificant. The coefficient of M13 in column (3) is marginally significant, thus imports from third parties are not as important as exports in determining actor to target conflict. As similar as column (3), the results in column (4) are marginally significant. From column (4):
It may be noted that the sample size is much smaller in Table 2 than in Table 1 as there are a large number of observations where target-third party net conflict equals zero.
For the entire sample, approximately 6,000 observations in relation to Z23 are less than– 28.26. If Z23 is positive, this implies that countries #2 and #3 are rivals, and an increase in imports from the third party leads to an increase in the actor’s conflict towards target #2. If Z23 is negative and must be less than– 28.26 in this case, this implies that countries #2 and #3 are friends, and an increase in imports from the third party leads to a decrease in the actor’s conflict towards target #2. Countries #2 and #3 must be highly cooperative with each other, otherwise when increasing trade, actor country #1 will still on average increase conflict towards country #2. Thus for the vast majority of observations for which it is implied that the target and third party are typically friends, an increase in imports from the third party leads to an increase in actor-target conflict.
As before, we run separate regressions for observations where the target and third party are friends or rivals. As the hypotheses stated, if the target and third party are friends (Z23<0), we would expect that the coefficients of X13 and M13 on actor-to-target conflict (Z12) will be negative, and if the target and third party are rivals (Z23>0), we would expect that they will be positive. Column (1) looks at actor-third party exports, but does not find a significant relationship. Column (2) looks at actor-third party imports and finds that actor-third party trade reduces actor-target conflict when the target and other countries are friendly. The results in columns
(3) and (4) consider the cases where the target and third party are rivals. In these cases, the positive coefficients of X13 and M13 show that an increase in trade between the actor and other countries will increase actor-target conflict and support the propositions.24
The most explicit test parts of the hypotheses are summarized in the Table 5. Except the coefficients that are not significant, all other coefficients in the empirical tests match the expectation.
Since in our sample the mean of net conflict equals– 1.09, the dyads tend to be more cooperative on average. As such, if the target and third party are rivals, it shows stronger and more consistent results which strengthen the propositions.
5. Summary
This article presents an initial step in the analytical study of the impact of multilateral trade on international interactions. In the process, it develops a theoretical framework for subsequent empirical investigation. In 1978, Solomon Polachek developed the trade-conflict model which claimed that increased trade between countries reduces conflict. The purpose of this paper is to illustrate the static nature of the underlying links between trade and conflict where third party relationships are considered. In so doing, we extend the basic trade-conflict model to analyze international interactions involving third parties. An actor country maximizes its plausible social welfare function subject to a balance of payments
It is worth noting that the empirical tests apply to average results. This research does not discuss the potential heterogeneity between nations that will cause different international interactions.
constraint. We derive a theorem whereby, under reasonable assumptions, trade between the actor and a third party will affect conflicts between the actor and the target. A similar relationship is discussed for conflicts which may change between the actor and the third party.
According to the theory of structural balance in international polities, changes in international relationships between two states affect a third nation. Heider (1946) and Cartwright and Harary (1956) formulated the postulates of this theory which focused on the tendency toward balance in a triadic relationship. Imbalance is an important factor in attitude change. The imbalance can be resolved either by all nations becoming friends or by two deciding to like each other and to dislike the third member of the trio, who responds negatively to both. This research essentially highlights the importance of accounting for how the changes in trade or conflict between countries affect the international multilateral relationships. The policy implication here is straightforward.
Encouraging free trade tends to decrease conflict and increase cooperation. The classical liberal thesis that trade promotes peace between states is based on two ideas: trade between two states increases the economic costs of waging war, and an inherent facet of increased trade is increased communication between states. The increased communication between states reduces the possibility of misunderstanding and fosters peaceful resolution of conflict (Hegre, 2000, p.5). Baron de Montesquieu (1990, p.316) stated that “Peace is the natural effect of trade. Two nations who traffic with each other become reciprocally dependent: for if one has the interest in buying, the other has the interest in selling; and thus their union
is founded on the mutual necessities.” With democracy being a worldwide trend, most contemporary leaders cling to this longstanding belief that expanding economic ties will increase the bonds of friendship and eliminate the thought of a resort to arms (Mansfield and Pollins, 2001, p.855). If the trade gains increase countries’
welfare and serious conflict among countries disrupts trade, trade will promote peace and increased world trade will make the maxim
“a friend of a friend is a friend” a reality. As such, the international system will be very structurally balanced.
This is a preliminary study that leaves some questions unanswered, especially in empirical work. That trade changes caused by tariffs, foreign aid, transportation costs,…etc., indirectly influence third party interactions needs to be investigated. Further research is suggested to develop along two key lines. First, more sophisticated measurements of the appropriate variables need to be developed.
Second, a more thorough understanding of the interaction of these variables and their relative importance is necessary. How far it will take us in understanding the relationship between third party trade and international interactions remains to be determined by future researches.
Table 1
The Conflict Relationship the Among Actor, Target and Third Parties Dependent Variable: Net Conflict (Z12)
Intercept - 1.71** - 1.08** - 1.72** - 1.08**
Z13 -0.00772** 0.0123** - 0.0186** - 0.00354
(-2.50) (5.55) (-5.92) (1.57)
Z23 0.013** 0.00624** 0.00216 - 0.00275
(4.08) (3.04) (0.67) (-1.32)
R-squared 0.094 0.104 0.097 0.109
N 93376 93805 93376 93805
T-statistics are in parentheses, * indicates significant at the 10 percent level, **
significant at the 5 percent level.
Table 2
The Conflict Relationship Among the Actor, Target and Third Parties Dependent Variable: Net Conflict (Z12)
Intercept - 2.21** - 1.29** - 2.66** - 1.62**
R-squared 0.108 0.109 0.127 0.113
N 49689 50168 9110 9325
Z23<0 Z23>0
T-statistics are in parentheses, * indicates significant at the 10 percent level, **
significant at the 5 percent level.
Table 3
The Conflict Relationship Among the Actor, Target and Third Parties Dependent Variable: Net Conflict (Z12)
Intercept - 1.73** - 1.74** - 1.12** - 1.12**
Z13 *Z23 0.00000417
(1.39)
M12 - 0.0069** - 0.0069**
(- 70.3) (- 70.3)
M212 1.0× 10-6** 1.0× 10-6**
(53.8) (53.8)
M13 - 0.00009** 0.0000842*
(-5.92) (1.74)
M13 *Z23 0.00000298*
(1.81)
Z23 0.00954** 0.00782** 0.00348 0.00203
(2.93) (2.24) (1.67) (0.91)
R-squared 0.0985 0.0985 0.111 0.111
N 85318 85318 84283 84283
T-statistics are in parentheses, * indicates significant at the 10 percent level, **
significant at the 5 percent level.
Table 4
The Conflict Relationship Among the Actor, Target and Third Parties Dependent Variable: Net Conflict (Z12)
Intercept
R-squared 0.114 0.117 0.099 0.106
N 56811 56256 8258 8509
T-statistics are in parentheses, * indicates significant at the 10 percent level, **
significant at the 5 percent level.
Table 5
The Summary of the Tests of Hypotheses
Friends or Rivals Controlling Coefficient Expected Effect Predicted Effect
Variable on Net Conflict on Net Conflict
Z23<0： X12 Z13 ＋ N.S.
M12 Z13 ＋ ＋
X12 X13 － N.S.
M12 M13 － －
Z23>0： X12 Z13 － －
M12 Z13 － －
X12 X13 ＋ ＋
M12 M13 ＋ ＋
N.S. indicates Not Significant; “＋”: The Actor Increases Net Conflict Towards the Target; “－”: The Actor Decreases Net Conflict Towards the Target;
Z23>0: The Target and Third Party Are Friends;
Z23>0: The Target and Third Party Are Rivals.
（收件：2003 年 3 月 17 日，修正：2003 年 7 月 11 日，採用：2003 年 7月 15 日）
Appendix: Procedures for Comparative Statics
In order to satisfy the second order conditions for maximization, the Hessian matrix must be negative definite. In other words, the principal minors , , , ……, must alternate in sign:
For a simple two-country case, the solving procedures are:
and similarly,
References
Anderton, Charles and John Carter (2001). “The Impact of War on Trade: An Interrupted Times-Series Study.” Journal of Peace Research 38(4): 445-457.
Azar, Edward (1978). “An Early Warning Model of International Hostilities.” In N. Choucri and T. Robinson (eds.), Forecasting International Relations:Theory, Methods, Problems and Prospects.
San Francisco: Freeman.
Azar, Edward (1980). “The Conflict and Peace Data Bank (COPDAB) Project.” Journal of Conflict Resolution 24: 143-152.
Altfield, Michael (1984). “The Decision to Ally: A Theory and Test.” Western Political Quarterly, 37: 523-544.
Altfield, Michael and Bruce Bueno de Mesquita, (1979). “Choosing Sides in Wars.” International Studies Quarterly, 23: 87-112.
Banks, Arthur (1973).SUNY-Binghamton Cross-National Time-Series Data. Center for Comparative Political Research, State University of New York at Binghamton.
Barbieri, Katherine (1996). “Economic Interdependence: A Path to Peace or a Source of Interstate Conflict?” Journal of Peace Research 33(1): 29-49.
Barbieri, Katherine and Gerald Schneider (1999). “Globalization and Peace: Assessing New Directions in the Study of Trade and Conflict.” Journal of Peace Research 36(4): 387-404.
Bercovitch, Jacob (1991). “International Mediation and Dispute Settlement: Evaluating the Conditions for Successful Mediation.”
Negotiation Journal 7(1): 17-30.
Bercovitch, Jacob and Allison Houston (1993). “Influence of
Mediator Characteristics and Behaviour on the Success of Mediation in International Relations.” International Journal of Conflict Management 4(4): 297-321.
Bercovitch, Jacob and Jeffrey Langley (1993). “The Nature of the Dispute and the Effectiveness of International Mediation.”
Journal of Conflict Resolution 37(4): 670-691.
Bercovitch, Jacob and Gerald Schneider (2000). “Who Mediates?
The Political Economy of International Conflict Management.”
Journal of Peace Research 37(2): 145-165.
Blainey, Geoffrey (1988). The Causes of War.
Basingstoke: Macmillan Press.Bremer, Stuart (1993). “Democracy and Militarized Interstate Conflict, 1816-1965.” International Interactions 18: 231-249.
Cartwright, D. and Frank Harary (1956). “Structural Balance: a Generalization of Heider’s Theory.” Psychological Review 63:
277-293.
Chan, Steve (1984). Mirror, “Mirror on the Wall…Are the Freer Countries More Pacific.”Journal of Conflict Resolution 28: 617-648.
Chang, Yuan-Ching (forthcoming). “Conflict and Trade: The Relationship Between Geographic Distance and International Interactions,” Journal of Socio-Economics.
de Montesquieu, Baron (1900). The Spirit of Laws, translated by Thomas Nugent. New York: Collier Press.
de Wilde, Jaap (1991).Saved from Oblivion: Interdependence Theory in the First Half of the 20th Century. A Study of the Causality Between War and Complex Interdependence. Aldershot: Dartmouth.
Dixon, William J. (1993). “Democracy and the Management of International Conflict.”Journal of Conflict Resolution 37: 42-68.
Dixon, William J. (1996). “Third-Party Techniques for Preventing Conflict Escalation and Promoting Peaceful Settlement.”
International Organization 50: 653-681.
Domke, William K. (1988). War and The Changing Global System.
New Haven: Yale University Press.
Dorussen, Han (1999). “Balance of Power Revisited: A Multi-Country Model of Trade and Conflict.” Journal of Peace Research 36(4): 443-462.
Gasiorowski, Mark and Solomon Polachek (1982). “Conflict and Interdependence.”Journal of Conflict Resolution, 26 (December):
709-29.
Gillespie, J. and D. Zinnes. n.d. World Trade Data: 1958-1968.
Inter-University Consortium for Political Research, University of Michigan.
Hegre, Havard (2000). “Development and the Liberal Peace: What Does it Take to be a Trading State?” Journal of Peace Research 37(1): 5-30.
Heider, Fritz (1946). “Attitudes and Cognitive Organization.”Journal of Psychology 21: 107-112.
Holsti, Ole; Terrence Hopmann and John Sullivan (1973). Unity
Holsti, Ole; Terrence Hopmann and John Sullivan (1973). Unity