The Social Dynamics of International Organization Membership ∗

Why do states join international organizations (IOs)? Cooperation theory offers a number of plausible answers to this question. However, empirical analysis of IOs has not kept pace with theory. We identify three key limitations in existing empirical research on IO membership. First, the units of analysis commonly used to model membership, such as the country-year or dyad-year, do not sufficiently distinguish between the attributes of states and the attributes of IOs. Not only do states select IOs, but IOs also select states, which necessitates careful attention to the attributes of each. Second, empirical models generally ignore the match quality between countries and IOs, implicitly assuming that all IOs are equally accessible to all countries. In practice, many IOs are functionally off-limits to large numbers of states, while others attract only certain types of states. Third, although cooperation theorists have long argued that IO membership is partially influenced by social effects, where the IO memberships of some states influence the memberships of others, few empirical models incorporate social effects into the analysis. We address these limitations by modeling IO membership as a dynamic affiliation network. Using newly collected data at the state-IO level, we build an inferential network model that addresses all three of the above limitations. The analysis shows that state-IO match quality and social effects are, by far, the primary determinants of IO membership. ∗For comments, we thank Duncan Snidal, Brian Greenhill, Christina Schneider, Paul Poast, Zeev Maoz, and participants in the Social and Political Interacting Networks research group at UC Davis. We also thank Jennifer Le for exceptional research assistance. This research is supported by Minerva Research Initiative grant 67804-LS-MRI. Why do states join international organizations? Cooperation theorists have offered numerous answers to this question.1 Yet, the empirical study of membership in formal intergovernmental organizations (IOs) has historically lagged behind theory. Until recently, IO membership was rarely treated as a dependent variable (with Jacobson, Reisinger, and Mathers (1986) and Shanks, Jacobson, and Kaplan (1996) as notable exceptions). Recent scholarship has revived interest in IO membership as an outcome worthy of analysis (e.g., Donno, Metzger, and Russett 2015; Poast and Urpelainen 2013). This increased attention, in turn, has generated promising new opportunities for reconceptualizing and refining the empirical analysis of membership. This paper introduces an approach that theorizes and empirically models IO membership as a type of social network. As typically employed, social networks involve a population of nodes—such as individuals, firms, or countries—with ties or edges connecting the nodes to one another. Yet, networks can take many forms. Of particular relevance to IOs is the affiliation network, which has been extensively employed in other fields—such as legislative processes, management, and industrial organization—to model affiliations between actors and events. An affiliation network consists of two heterogeneous populations of nodes, where ties form between populations but not within them. This framework is often used to explain individual organizational memberships, as in directors on corporate boards (Mizruchi 1996) and, most famously, southern women and the social functions in which they participate (Breiger 1974; Davis, Warner, Gardner, and Gardner 1941; Homans 1950). Additional well-known examples include soccer players and the clubs for which they play (Onody and de Castro 2004), music fans and their favored music genres (Lambiotte and Ausloos 2005), or national economies and export products (Hidalgo and Hausmann 2009). Defining IO membership as an affiliation network offers numerous benefits. First, because the affiliation network builds upon a state-IO unit of analysis, it places states and IOs on equal footing, allowing us to consider simultaneously the attributes of both. Thus, we need not rely on dyadic or country-level counts of IO membership, or focus only on membership in particular IOs. Instead, we include the full population of both states and IOs, with relevant covariates for each group. In short, affiliation networks eliminate the unit-of-analysis problem. Second, because the network 1 See, e.g., Abbott and Snidal (1998); Gilligan and Johns (2012); Koremenos, Lipson, and Snidal (2001); Martin and Simmons (1998); Stein (1982).