Transnational Advocacy Networks

Elizabeth A. Bloodgood, Emily Clough
2016 Social science computer review  
We examine the costs and benefits of NGO networking using a complex systems approach and agent-based modeling to simulate the effects of NGOs' efforts to seek influence in policymaking at home and abroad. We elaborate on the boomerang model developed by Keck and Sikkink (1998) and uncover macrolevel effects of multiple NGOs networking for policy influence in multiple states around multiple positions on the same issue simultaneously. The results of our model and simulations lead us to argue that
more » ... the boomerang effect has interesting unexplored implications for NGO behavior and state policy worthy of further empirical testing. We find that networking is necessary for NGOs to change state policy, but leads to a higher likelihood of organizational collapse for NGOs. While networking leads to policy change, as is well-demonstrated within existing literature, our model suggests that efficacy comes at a cost to NGOs, which should make analysts and academics more ambivalent about the advisability of NGO networking. NGOs will change policy at home, but leads to a higher likelihood of organizational collapse for NGOs. While networking leads to policy change, as is well-demonstrated within existing literature, our model suggests that efficacy comes at a cost to NGOs, which should make analysts and academics more ambivalent about the advisability of NGO networking. Future empirical testing of these theoretical findings will help to extend and strengthen a growing research program on transnational NGO advocacy. We begin by describing the boomerang model, as outlined by Keck and Sikkink (1998) , and explaining why the agent-based modeling approach is appropriate to studying it. We then present our model of NGOs in international affairs, explain its assumptions, and describe the results of multiple runs of this model. Based on the results of these simulations, we propose networking has mixed effects on TANs, individual NGOs, and states. While the purpose of this paper is hypothesis generation, we discuss the empirical implications of the model and prospects for future testing. In the conclusion we revisit the value of agent-based analysis for further study of NGOs and transnational activist networks. The Boomerang Model of NGO Influence in International Affairs The boomerang pattern, as presented by Keck and Sikkink, serves as a standard model for transnational NGO behavior. Some argue that this is the most rigorous and systematic theorizing of NGO behavior to date (Florini 2000; Price 2003; Yanacopulos 2005). Furthermore, other scholars have built upon these ideas theoretically and empirically (Risse, Ropp, and Sikkink 1999; Thomas 2002; Bob 2005; Hertel 2006; Lerche 2008). Our goal is to use agent-based modeling to systematize the insights of the boomerang model in a general model of NGO networking and influence. sharing of resources, initial resources will have no discernible impact on their ability to change policy in their home country. DNGOs are not condemned by their original network structures or resource endowments. This is consistent with case studies of NGOs that grew to become major political players, including Greenpeace, Friends of the Earth, Save the Children, Amnesty International, and Human Rights Watch. Each began from humble beginnings but grew over time and, in many cases, centralized network partners into more hierarchical organizations (Clark 2001; Bob 2005; Skjelsbaek 1971; Stroup 2012; Wapner 1995). It is also interesting to note that regime type had little effect on policy distance. The regime's resistance to change does not affect the final policy distance. While we cannot draw strong conclusions without a higher R 2 , and empirical testing, it seems possible that a network structure is a sufficient condition for policy change. Although it may take more work to change policy in dictatorships and other strongly opposed states, this is not a priori impossible even for relatively weak NGOs as long as networking is possible. This is consistent with the finding by Risse-Kappen (1995) that significant policy change in dictatorships is easier than in democracies. While access is more difficult to obtain, the centralized structure of decision-making increases the depth of policy change for those NGOs who can gain access. NGO Networks and Organizational Survival 24 The model has thus far produced results consistent with the expectations of the boomerang model. Namely networking abroad helps NGOs produce policy change at home. The advantage of using computer simulations, however, is that it allows us to explore other implications of the boomerang model. We find that, in addition to increasing the effectiveness of DNGO lobbying, networking also increases the likelihood of a NGO dying. In other words, NGOs are sacrificed in the boomerang effect, although which NGOs will die is not predetermined nor planned by any one actor. The percentage of NGOs surviving to the 80 th iteration for each of the conditions is shown in Table 4 . We compare those DNGOs that had no network in the beginning and no network at the end (which occurred at either the DNGO's death or the end of 80 iterations) with those that were in a network in at least one of those points. [ Table 4 about here] Two points are apparent from Table 4 . First, in every condition of the model, NGOs that are networked are far less likely to survive to the 80 th iteration of the model. The differences between the networked and unnetworked NGOs are both substantive and statistically significant, ranging from a difference of approximately 30% to 45%. Second, as the number of DNGOs in the model increases, so does the difference between the networked and unnetworked DNGOs. While both types of NGO have less likelihood of survival as the system is more densely populated with DNGOs, the networked NGOs see a much more dramatic drop off in survival rates. Hypothesis: NGOs that form networks and share resources in a boomerang fashion are more likely to dissolve through lack of resources.
doi:10.1177/0894439316634077 fatcat:e2nfttvp5zhilaevyz6dlc7cum