Diffusion Across International Organizations

For a long time, international organizations (IOs) such as the United Nations, the European Union, and the OECD have played a prominent role in research on the diffusion of policies and institutional features among national governments. Some studies portray IOs as efficient fora, facilitating the exchange of knowledge between member state governments (Greenhill 2010; Needergard 2006; Holzinger, Knill, Sommerer 2008; Simmons and Elkins 2004). IOs can also take an active role by collecting and distributing information on good practice and by promoting particular regulatory instruments or norms (Finnemore 1993; Keck and Sikkink 1998; Risse, Ropp, and Sikkink 1999; Joachim 2003). And when a substantial degree of authority has been delegated to the international level, IOs affect decisions in member states by conditioning financial assistance or the admission of new members on the adoption of certain rules (Brooks 2005; Schimmelfennig and Sedelmeier 2004; Weyland 2007).

Diffusion, understood as a process where ‘prior adoption of a trait or practice in a population alters the probability of adoption for remaining non-adopters’ (Strang 1991, 325), is not the exclusive domain of national governments. For more than one century, research on diffusion has provided evidence for interdependent decision-making at different levels (Rogers 1995). In addition to nation states and their governments, diffusion scholars studied voters (Zaller 1987) and consumers (Rogers 1976), firms (Abrahamson 1991), cities (Shipan and Volden 2006), and local governments (Walker 2006) as well as states in a federal system (Gray 1973; Berry and Berry 1990). Diffusion is a relevant phenomenon for all kinds of institutions or organizations that face uncertainty, social pressure, and shared problems. Thus, it is reasonable to expect that interdependence matters for decision-makers in IOs as well.

Recent years witness a growing scholarly interest in this phenomenon. Grigorescu (2010) showed how formal connections between IOs lead to the spread of bureaucratic oversight mechanisms. Ovodenko and Keohane (2012) studied institutional diffusion in international environmental cooperation. Böhmelt and Spilker (2016) demonstrated how international interactions affect the design of environmental treaties. Sommerer and Tallberg (2019) provided an analysis of interdependence in the spread of non-state participatory arrangements across IOs. Alter (2012) studied the global diffusion of European style international courts, and Lenz and Burlikov (2017) showed how the EU as a model has shaped the evolution of regional institutions elsewhere.

Still, we know relatively little about diffusion processes among IOs. In this article, I will discuss a number of reasons for the relevance of this approach, basic features of an explanatory framework, and the consequences of diffusion among IOs. This article will end with an outlook on future trends and challenges in research on diffusion among IOs.

How Diffusion Became Relevant for IOs: Two Major Trends in Global Governance

Since the end of the Cold War, global governance has undergone fundamental changes. In the following, I will highlight two developments that contributed to the growing relevance of diffusion among IOs. First, international organizations are more important than they have been ever before. We have witnessed a growth in the number of institutions beyond the nation state, and, more importantly, a rise in the authority transferred to the international level. Member states delegate more and more tasks to supranational bodies, expand the use of majority voting, and continue to broaden the scope of IO mandates (Hooghe et al. 2017; Zürn 2018; Hooghe et al. 2019). One of the consequences of this trend are growing geographical and functional overlaps between IOs, increasing the likelihood of institutional cooperation and competition. In the field of security politics in Europe, for instance, we observe collaboration between NATO and OSCE (see also van Willigen and Koops 2015). High-level meetings like the EU-Arab League summit become more common. In 2016, the OECD initiated an IO partnership, including secretariats from more than 50 IOs ‘to foster dialogue on shared challenges and support common understanding on good practices in international rulemaking’. There are also signs of growing competition between IOs, as in the case of the Asian Infrastructure and Development Bank and other development IOs (Wang 2017; for the relationship between overlaps and interface conflicts, see Zürn et al. 2018).

Second, it has been argued that growing IO authority has led to a politicization of global governance (Zürn 2018). Today, IOs are under enormous pressure from different stakeholders. Once the realm of technocrats operating under the radar of public scrutiny, IOs like the World Bank, the IMF, and the WTO became the target of large-scale protests and campaigns during the 1990s. In recent years, challenges often come from member state governments questioning individual IOs – as in the prominent case of Brexit – or the existence of multilateral arrangements per se (Weiss 2018). Overall, the growing prominence of efficiency and performance reviews among states (Simmons and Kelley 2020) has also begun to expand to the world of IOs. Since 2010, the Aid Transparency Index ranks development aid agencies and IOs. And since 2002, the Multilateral Organization Performance Assessment Network (MOPAN), a network of like-minded donor countries, assesses the organizational effectiveness of IOs.

These challenges from key stakeholders questioning the effectiveness and legitimacy of IOs make diffusion more relevant and more likely. They urge IOs to legitimate their activities and structures. One way to deal with such pressure is to take a closer look at the past behavior of peer groups. Emulating what similar actors did in similar situations may give an organization more legitimacy, resources, stability, and enhanced survival prospects.

Mechanisms and Pathways of Diffusion Across IOs

The main goal of most diffusion studies is to find evidence for interdependent decision-making. In research on policy diffusion, two follow-up questions are particularly prominent: why policies diffuse and which pathways are relevant for the transfer of models and ideas from one actor to another (Graham et al. 2012). A large number of diffusion mechanisms have been discussed, which can be summarized in four broader categories: learning, emulation, competition, and coercion (Gilardi 2012; for alternatives, see Simmons and Elkins 2004; Graham et al. 2012). Learning refers to processes where decision-makers use experience from other actors or their own past. It can be based on the fully rational processing of information on successful models, or, given costs and limitations that human beings face, on cognitive shortcuts that facilitate decision-making. Emulation follows the logic of appropriateness: decision-makers conform to what is defined as appropriated in a relevant peer group. Competition is a diffusion mechanism where decision-makers strategically adapt to the previous behavior of their rivals, to reduce a competitive disadvantage. Coercion, or conditionality, refers to norm pressure from powerful actors that pushes others into the adoption of certain norms or standards.

While these mechanisms apply to almost all levels of diffusion, the pathways vary with the type of actor involved. As Sommerer and Tallberg (2019) argue, we may see the same processes of learning, competition, and emulation, among global governance institutions. At the same time, the way in which information flows from one IO to another differs substantially from processes of diffusion among nation states. Understanding which channels matter is an important step towards a better understanding of when and under what conditions diffusion across IOs occurs.

Sommerer and Tallberg (2019) divide pathways of diffusion across IOs into formal and informal connectivity. No single variant is associated with a particular mechanism of diffusion, as conventionally understood; all of them may function as a channel for learning, emulation, competition, or coercion.


Formal connectivity refers to IOs that are organizationally interlinked. Such linkages are more likely to create direct opportunities for communication and exchange on each other’s experiences. IOs may be linked through their memberships. In this logic, states draw on information gained in one IO when confronted with a similar policy or design choice in another IO. One illustration is the compliance system of the Montreal Protocol on ozone depletion, which subsequently was used as a model for the Kyoto Protocol on climate change by states that were members of both agreements (Gehring and Oberthür 2009, 134). IOs may also be organizationally linked to each other through institutional arrangements and partnerships. As the IO partnership of the OECD illustrated, it has become relatively common that IOs recognize each other’s existence, mutuality of interests, and functional interconnectedness by engaging in official relations.

IOs do not only get inspiration from those IOs they have organizational links with. The logic of informal connectivity suggests that IOs are more likely to adopt a particular policy or design when other IOs in the same reference group do the same because those IOs’ experiences could be considered most relevant (Simmons and Elkins 2004; Strang and Soule 1998, 275). IOs active in the same issue area normally have commonalities in the societal problems they address. In the area of regional integration, it is known that IOs have adopted designs with an eye to models in other parts of the world (Alter 2012; Börzel and Risse 2016). Likewise, the North Atlantic Fishery Organizations has been able to learn from the Fisheries Commission for the Mediterranean (Hollway 2015).

Similarly, IOs rooted in the same region can be considered to be members of the same cultural reference group. These commonalities go beyond overlapping memberships and build on regional community attributes such as shared collective identities and historical experiences (Checkel and Katzenstein 2009; Risse 2010). The basic notion is that ideas about appropriate models reflect deep identity concerns. For example, IOs based in Asia are said to embrace a specifically ‘Asian way’ of cooperation, distinguished by informality and intergovernmentalism (Kahler 2000).

Finally, IOs may be informally connected because of proximity in their headquarters’ location. There is a high concentration of IO headquarters in cities such as New York, Geneva, Brussels, London, Paris, and Vienna. Even if IOs have little else in common, being located close to each other entails greater opportunities for informal interaction (Grigorescu 2010, 87). As IO staff meet and discuss in social and political arenas, experiences and norms travel more easily. One example is the exchange of staff and lessons between Brussels-based NATO and the EU (Biermann 2008, 172).

In addition to pathways defined by connectivity, Table 1 includes a third category of diffusion channels. In this context, scholars have discussed the learning from miracle models – in the absence of direct ties (Meseguer 2005) or the influence of large fads, fashion, and herding within a population (Levi-Faur and Jordana 2005). These cues may as well lead to interdependent decision-making of IOs.

What Are the Consequences of Diffusion Among IOs?

Convergence, understood as the growing degree of similarity across units (Bennett 1991; Knill 2005), is often regarded as the outcome of diffusion. However, convergence is not necessarily the result of diffusion; it can also be caused by actors reacting to similar, but independent pressure. An exclusive focus on exact imitation, blueprints or the ‘passive downloading of some new rules and institutional software’ (Risse 2016)  as relevant consequences of diffusion may lead to a biased understanding of diffusion effects. As some authors have argued, the consequences of diffusion processes often involve active processes of translation, interpretation, and resistance to particular rules and regulations (Solingen 2012; Acharya 2004). Recent approaches to the study of convergence are therefore more nuanced, and account for various types of convergence, ranging from strict imitation to adaption and inspiration (Sommerer and Tallberg 2019; see also Bennett 1991).

In research on globalization, the trend towards greater homogeneity in rule-making and institutional design often has a negative connotation. According to the metaphor of a ‘race-to-the-bottom’, scholars and the public fear that tax revenues and protection standards are driven down when governments underbid each other in the strive for a competitive advantage, until they reach an equilibrium at the ‘bottom’ – e.g., low labor standards or no environmental protection (Drezner 2001; Holzinger and Sommerer 2011; Holzinger et al. 2008). However, there is some evidence for the opposite dynamic of a ‘race to the top’, exemplified by the so-called California effect (Vogel 2005, Prakash and Potoski 2006). Both scenarios could apply to the case of cross-IO diffusion, when institutional competition incentivizes decision-makers in one IO to lower (or strengthen) standards of accountability, transparency or participation.

A second aspect of the normative assessment of convergence as a consequence of diffusion centers on the advantages and pitfalls of growing homogeneity. In anti-globalization discourses, the McDonaldization (Ritzer 1993) of the world is a popular slogan, and we also find pessimistic interpretations of strong convergence trends in evolutionary models of diffusion and learning. Whereas a certain degree of imitation is important for growth and development of all kinds of societies, too much imitation threatens their survival by making them less resilient and more vulnerable, analogous to monoculture in farming (Henrich and McEltrath 2005). In the study of comparative regionalism, we find negative portrays of the diffusion of models from the Global North – at the cost of local models (Acharya 2004; Risse 2016). This critique resonates well with recent debates on the retrenchment of liberal norms and fundamental changes in the global order (Ikenberry 2011; Dunne and Flockhart 2013; Acharya 2014).

But the economic theory of rational herding and social learning also allows for a more positive view of growing homogeneity. Under extreme uncertainty, it is rational for decision-makers to imitate behavior of their peers, assuming that fashions and fads represent the aggregate learning of larger collectives. The so-called ‘conformity bias’ describes the inclination to adopt – under uncertainty – the most common behavior within a group (Boyd and Richerson 1985; Kameda and Nakanishi 2002). If we apply this to the context of IOs, it could be argued that the current situation of growing pressure for performance as well as popular challenges lead to greater institutional robustness.

Future Avenues of Research on IO Diffusion

There are reasons to believe that diffusion matters for IOs. Growing organizational density in global governance, accompanied by more collaboration and competition and rising challenges from a wide range of stakeholders make IOs more sensitive to the behavior and experience of their peer groups. Much like other organizations, firms, and governments, IOs frequently adopt similar institutional designs, policies, and norms. But so far, empirical evidence is scarce, and future research on IO diffusion will have to deal with three different challenges.

First, diffusion scholars need to demonstrate that the logic of interdependence and connectivity applies to a wider range of cases. Expanding this research analogous to the enormous body of literature on policy diffusion across nation states will have to go hand in hand with the identification of pathways, motivations, and consequences that are specific to IOs.

Second, the debate on the consequences of IO diffusion will have to become more nuanced. Two aspects are particularly interesting in this regard. In the light of the broader debate on changes in world order and liberal retrenchment, it could be argued that the post-Cold War phenomenon of the spread of Western policies, norms, and institutions, has already peaked, and that we will see less diffusion, or different patterns and pathways of diffusion in global governance. Alternatively, IR scholars could look more closely into the potential benefits for IOs from growing interdependent decision-making in times of crisis. It may be interesting to see if and how diffusion and learning may contribute to overcome internal obstacles to collective decision-making, and how they affect reform capacity, performance, and legitimacy of IOs.

Third, future studies on IO diffusion will face the same methodological challenges as diffusion research in general. For quantitative research, recent advances in longitudinal network analysis could be applied to the study of IO diffusion as well (Manger and Pickup 2016; Minhas, Hoff, and Ward 2016). There is also a potential for experimental methods in studying the motivations behind diffusion, which have been difficult to credibly isolate in earlier quantitative work (Gilardi 2012; Graham, Shipan, and Volden 2013; Maggetti and Gilardi 2016). For case study designs, it will be important to go beyond the narrow focus on studies involving the spread of the EU as a model. In general terms, the rising interest in the comparative study of IOs and the growing availability of data on IO design and policies will facilitate the empirical study of this phenomenon.


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