Abstract
The link between security and economic exchange is widely recognized. But when and how much do geopolitical interests matter for economic cooperation? While existing work focuses on bilateral trade and aid, we examine how geopolitics shapes membership in multilateral economic organizations. We demonstrate that substantial discrimination occurs as states welcome or exclude states based on foreign policy similarity. Biased selection of members can politicize economic cooperation despite multilateral norms of non-discrimination. We test the geopolitical origins of institutional membership by analyzing new data on membership patterns for 231 economic organizations from 1949 – 2014. Evidence shows that security ties shape which states join and remain in organizations at both the formation and enlargement stages. We use a finite mixture model to compare the relative power of economic and geopolitical considerations, finding that geopolitical alignment accounts for nearly half of the membership decisions in economic institutions.
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08 March 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11558-021-09416-2
Notes
We discuss the sample in more detail later in the paper, and conduct robustness checks on a smaller sample of organizations exclusively focused on economic activities.
Indeed, multilateral trade negotiations relied on bilateral deals based on the principal- supplier rule to isolate the exchange of benefits (Hicks and Gowa 2018).
As distinct from public goods, club goods allow for the possibility of exclusion through limiting benefits to those who contribute to the provision of the club good (Cornes and Sandler 1996).
Economic topics are broadly construed to include aid, finance, trade, and more general management of resources, travel, and standards relevant for economic exchange. Our coding of organizations draws on the Yearbook of International Organizations’ description of the aims and subject of each organization along with information contained in the IGO charter documents.
Results are not contingent on our exclusion of IGOs without charter documents.
Our empirical results are robust to the inclusion of these observations. See Table A7 in the online appendix (available at the Review of International Organizations webpage) for results on a sample of all state-IGO-year observations.
We identify these “universal” IGOs by examining their founding charters. IGOs are universal, and thus excluded from the sample, if there are no formal restrictions on membership eligibility or requirement for a vote of approval by members.
States are divided into eight geographic regions: Sub-Saharan Africa; Middle East and North Africa; Europe and Central Asia; Western Europe; Latin America and Caribbean; North and Central America; East Asia and Pacific; South Asia. IGOs are coded as regional if their charter or organizational title references a geographic region.
IGOs enter the dataset in the year in which they are founded and continue until 2014 or the year that the organization ends. We include all states listed in the COW state system for which we have data on covariates. Covariate coverage primarily excludes small states (e.g., Grenada) or those where data is unavailable (North Korea). We follow the COW coding for the start and end of IGO existence.
Data on alliances, which include defense pacts and neutrality or nonaggression pacts, come from version 4.1 of the COW Formal Alliances dataset (Gibler 2009).
S-scores are calculated using the COW formal alliance dataset.
Bilateral trade data is from the IMF Direction of Trade dataset. The “trade with members” variable measures average (logged) volume of merchandise imports and exports between state i and each member of IGO j. The “trade with lead state” variable measures (logged) trade volume with the lead state. We add one before taking the log to ensure values of zero trade are not excluded due to the mathematical transformation.
We use the natural log of GDP and GDP per capita in constant 1967 US dollars. Data through 2004 are from Goldstein et al. (2007); we use adjusted World Bank GDP estimates to fill in subsequent years.
MIDs data are from the dyadic version of the COW Militarized Interstate Disputes Dataset.
Data on geographic distance and colonial linkages are from CEPII.
We also include IGOs that require potential members to receive approval from a specific committee. Approximately a third of the IGOs in our sample (75) have stringent accession procedures.
This is consistent with Stone (2011), who theorizes participation in IGOs as an ongoing process of decisions to enter and continue cooperation.
States’ alliance and trade ties are positively correlated in our sample (0.18), consistent with existing work demonstrating that allies are more likely to trade with each other (Gowa and Mansfield 1993). Our estimates for the effect of alliances are therefore likely to be conservative: by controlling for trade ties, we omit one potential causal pathway (alliances→trade→IGO membership) in which alliances encourage IGO membership.
This “entry” sample is equivalent to a model of membership onset. Following McGrath (2015), we treat continued membership as missing for this model. This sample has a much lower probability of membership at .003 (relative to .37 for sample in models 1 and 2) given that it drops current members while retaining observations for all non-members. This attenuates the effect size substantively.
The sample in Model 4 only includes the year of formation for each IGO, yielding a smaller sample. Thirty-eight IGOs created before 1950 drop from the sample.
The sample in Model 5 excludes the year of formation for each IGO, examining state entry in subsequent years. As in Model 3, we exclude continued membership after a state has joined an IGO.
The dependent variable in this model is a dichotomous measure of exit, equal to one when existing members leave an IGO. We use rare events logit because exit is very infrequent (0.12% of observations).
Here, we revert to the full sample used in Model 2. Following Lechner (2011), we use a linear probability model for the difference-in-differences specification. We remove the “Cold War” indicator in this specification, since the model includes year fixed effects.
For Fig. 1 and subsequent empirical tests, we use the pooled sample of state-IGO-year observations from Model 2 above. Appendix C replicates all subsequent tests with the entry-only sample from Model 3. The significant positive effect of alliances persists in this sample.
Appendix Table A1 shows the full set of coefficients and standard errors when replicating Table 1, Models 1-2 using S-scores and UN Ideal Point similarity. Appendix Figure A1 shows the substantive effect of these measures in the entry-only, IGO formation, and IGO enlargement samples from Table 1 Models 3-5. The alliance measures of geopolitical alignment are significant and positive across samples. In the truncated entry-only sample that drops current members, the smaller effect size is relative to a lower 3.2% baseline probability of membership. The average measures for S-scores and UN Ideal Point similarity are significant predictors of entry, while the lead state measures are insignificant.
We define as salient any IGO which received at least 50 references in major newspapers during the founding year or when our sample ends in 2014.
Our key explanatory variable for average alliances meets the proportional hazard assumption. However, diagnostic tests reveal a potential violation of the trade ties variable, which has an effect that changes over time. We add a time interaction that captures the conditional effect of a variable that violates the PHA with years of eligibility as recommended by Licht (2011).
See Figure A4 in the Appendix for the distribution of model assignments in the fitted model.
Koremenos et al. (2001) contend that IGOs limit membership to address enforcement or uncertainty about preferences.
Though both models include the same control variables, the coefficients are allowed to vary across the two theories.
We use the main sample of state-IGO-year observations from Table 1, Model 2. Appendix Table A11 replicates the analysis with the entry-only sample. The model is estimated using the flexmix package in R (Grun and Leisch 2008). Coefficients and standard errors are obtained by estimating a weighted logistic regression, with weights corresponding to each observation’s assignment to the two competing models.
Increasing Trade with Members by one standard deviation (4.20) is associated with a 31.24% increase in the probability of IGO membership.
We calculate this measure by summing over all observations’ probability of assignment to Model 1 and Model 2. See Table A10 in the Appendix for equivalent results for a mixture model fitted on the entry-only sample and for estimates of the geopolitical and trade models’ prevalence among IGO formation and IGO enlargement observations.
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Acknowledgments
Christina Davis is Professor of Government at Harvard University (cldavis@harvard.edu). Tyler Pratt is Assistant Professor of Political Science at Yale University (tyler.pratt@yale.edu). We are grateful to Raymond Hicks for valuable research assistance. We thank Lawrence Broz, Joanne Gowa, Julia Gray, Kosuke Imai, Srividya Jandhyala, Miles Kahler, Robert Keohane, Yonatan Lupu, Devorah Manekin, Ed Mansfield, Lisa Martin, Jong Hee Park, Duncan Snidal, Etel Solingen, Randall Stone, Jaroslav Tir, Felicity Vabulas, Erik Voeten, and Inken von Borzyskowski for comments on an earlier draft.
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Davis, C.L., Pratt, T. The forces of attraction: How security interests shape membership in economic institutions. Rev Int Organ 16, 903–929 (2021). https://doi.org/10.1007/s11558-020-09395-w
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DOI: https://doi.org/10.1007/s11558-020-09395-w
Keywords
- International cooperation
- Economic statecraft
- Membership in international organizations
- Multilateral economic organizations
- Alliances
- Issue linkage
- Finite mixture model