Model Specification

In order to generate an appropriate variable for interest similarity, I predict the value for each interest indicator individually right after exploratory factor analysis (EFA), and take the logarithm of the interest ratio from state A to state B. The dyadic ratio of security interest ranges from -26.2 (indicating less similar in bilateral security interests) to 14.3 (indicating more similar in bilateral security interests). Community

and economic interests have the same measure.9 _{Alternatively, I generate one major}

variable by reducing these three policy factors into one final interest indicator.10 _In

9_{In order to fit the data with large value of the variable indicating the similarity of policy}

affiliation, I also transform these three indicators by multiplying them by -1.

10_{By running the EFA again for these three different policy interests, the result gives us one}

fact, interest similarity signifies the similarity of revealed preferences between the two states in the dyad. This latent factor covering three different policy objectives gives us a more robust measure of states’ general policy affiliation. The score ranges from -7.4 (weak affinity) to 5.4 (strong affinity). In general, higher values indicate that two states in one pair have more similarities in their policy choices, which endows these two states with more “ingroup” characteristics. On the other hand, lower scores of interest similarity index represent a more “outgroup” phenomenon between these two states in a dyad.

I use the undirected dyad-year for the conflict study. The dependent variable, militarized interstate dispute (MID), is derived from the Correlates of War data set. A MID is defined as a conflict between members in the international system that involved the threat, display, or use of force (Jones, Bremer and Singer 1996). From the data, 1 indicates that there is a new militarized interstate dispute, while 0 means

no MID during a given year.11

I employ the data set from the Composite National Capability (CINC) in the Correlates of War project. In order to demonstrate the difference in national capa- bility between two nations, I follow convention and take the logarithm of the ratio of the stronger states’ capability index to that of the weaker state (Russett and Oneal 2001). The results tell us how much two nations’ capabilities differ from each other. The higher value represents a bigger capability gap, while the lower value suggests a balance of capability.

In order to reveal the effect of proximity, I include the variables log distance and

contiguous. If two states cannot interact, there is very little opportunity for both to

fight. Most countries in the world do not have the capability to project military power over distances. The distance is the natural log of the great circle distance between

empirical work can be obtained from the author.

11_{I also ran analysis on Maoz’s militarized disputes data using a similar examination. The results}

capital cities.12 _{Except for distance, scholars believe that geographical contiguity}

is also statistically significant for all interstate conflict. I control the variable for territorial contiguity because it creates more possibility of interaction for different countries. In addition, in terms of the force projection and global interests, not all states have the same social status. Major powers may have stronger networks with other countries than small or weak states. In order to control for the influence of

major power dynamics, we should include the variable major power, which equals 1

if at least one state in the dyad is a major power and 0 otherwise.

I adopt the other three major Kantian variables from Oneal and Russett’s model

(Oneal and Russet 1999c). The democratic peace literature offers a robust and ap-

propriate baseline model, which allows for ready comparison of results and diminishes the danger of incorrect coding or model specification. I adopt Oneal and Russett’s

DemocracyL to represent the least democratic state in the dyad. This variable has

long been associated with the lower probability of interstate conflict. It represents

the key idea of how democracy generates more peace for countries in the world.13

Second, in order to reveal how economic interdependence impacts states’ conflict be-

havior, I adoptDependenceL as the measure from Oneal and Russett’s model (Oneal

and Russet 1999c). They divide a country’s total trade with its dyadic partner by its

gross domestic product (GDP). In general, we expect that this variable has negative effects on conflict onset. Third, I also include the measure of the total number of joint IGO memberships. This data is also generated from Oneal and Russett’s study and has long been considered as a pacifying indicator on states’ conflict behavior.

Last but not least, in order to test and correct for time dependence issues generated by conflict onset, I follow the propositions of Beck, Katz, and Tucker (1998) on

12_{In order to examine whether interest similarity influences conflict onset, I attempt to replicate}

Russett and Oneal’s model of dispute likelihood. I chose their work because it is the most replicated and complete model. Russett and Oneal argue that using the shortest distance around the surface of the globe between the capitals can reveal the constraints imposed by geography.

controlling for the years of peace a dyad has experienced. I also control for the cubic splines of order 1 to 3.