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IGOs and the Determinants of Member State Interest Convergence
Unformatted Document Text:  not enough to look at states by themselves because interest convergence is a relational process. In other words, one would not expect a state to be more likely to converge in interests with another state as a result of being a member of some IGO, but rather that they are more likely to take on more similar interests with a particular state that also belongs to that same IGO or a network of common IGOs. Because I expect that states’ interests converge as a result of their interaction within IGOs to which they jointly belong, it makes sense to expect a cumulative effect as a result of joint memberships in more of the same IGOs. Of course, as mentioned above, not all IGOs are alike and one should not expect a simple count of joint IGO memberships to have a cumulative linear affect on state interest similarity. However, given the theory laid out above, it does make sense to expect an overall cumulative effect of joint memberships in IGOs with higher levels of specific institutional attributes theorized to be conducive to the process that leads to interest convergence. Dependent Variable I will now discuss the measurement of each of the variables to be included in the model. The dependent variable, which is interest similarity between two states, will be measured using Gartzke’s AFFINITY measure. The AFFINITY measure is an S-score calculated using roll-call votes within the United Nation’s General Assembly (UNGA) . The A FFINITY variable does not capture UNGA votes, but instead captures the similarity of the voting decisions of two states, which is calculated using an S statistic. Admittedly, this is not a perfect measure of interests. Choosing measures that match with the concepts involved in social science theories is very important, especially if we are going 20

Authors: Bondanella, Stacy.
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not enough to look at states by themselves because interest convergence is a relational
process. In other words, one would not expect a state to be more likely to converge in
interests with another state as a result of being a member of some IGO, but rather that
they are more likely to take on more similar interests with a particular state that also
belongs to that same IGO or a network of common IGOs. Because I expect that states’
interests converge as a result of their interaction within IGOs to which they jointly
belong, it makes sense to expect a cumulative effect as a result of joint memberships in
more of the same IGOs. Of course, as mentioned above, not all IGOs are alike and one
should not expect a simple count of joint IGO memberships to have a cumulative linear
affect on state interest similarity. However, given the theory laid out above, it does make
sense to expect an overall cumulative effect of joint memberships in IGOs with higher
levels of specific institutional attributes theorized to be conducive to the process that
leads to interest convergence.
Dependent Variable
I will now discuss the measurement of each of the variables to be included in the
model. The dependent variable, which is interest similarity between two states, will be
measured using Gartzke’s
AFFINITY
measure. The
AFFINITY
measure is an S-score
calculated using roll-call votes within the United Nation’s General Assembly (UNGA) .
The A
FFINITY
variable does not capture UNGA votes, but instead captures the similarity of
the voting decisions of two states, which is calculated using an S statistic. Admittedly,
this is not a perfect measure of interests. Choosing measures that match with the
concepts involved in social science theories is very important, especially if we are going
20


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