Connecting the dots
A network analytical perspective on the
formation of transnational regulatory
networks
This thesis is written for the master Public Management at Leiden University under the supervision of M.J.A. van der Heijden MSc MA and dr. J. Schalk.
Zeping Oerlemans, S1272322 August 10, 2018
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Hans Oerlemans Ilse Verduijn Iris van Mulbregt Liping Oerlemans Maaike van Vliet Thiebault Oudendijk
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Table of content
1. Introduction p. 4
2. Literature review p. 10
2.1 Transnational regulatory networks p. 10
2.2 Network forms and characteristics p. 13
2.3 The academic field p. 17
2.4 The importance of network structure p. 19
2.5 Network formation p. 21 2.6 Summary p. 23 3. Theoretical framework p. 24 3.1 Network embeddedness p. 24 3.2 Network density p. 25 3.3 Network centralization p. 27 3.4 Clique structure p. 30 3.5 Summary p. 35 4. Research design p. 36 4.1 Case selection p. 39 4.2 Data collection p. 46
4.3 Social network analysis p. 50
4.4 Conceptualization and operationalization p. 51
4.5 Summary p. 56
5. Research context p. 57
5.1 Internationalization of the stock market p. 57
5.2 International cooperation between regulatory agencies p. 60
5.3 The purpose of Memoranda of Understanding p. 63
5.4 Summary p. 65
6. Analysis and results p. 66
6.1 Social network analysis 1990 p. 68
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6.3 Social network analysis 2010 p. 80
6.4 Comparison throughout the years p. 86
6.5 Summary p. 89
7. Discussion p. 90
7.1 Interpretation of the results p. 90
7.2 Limitations p. 97 7.3 Future research p. 100 8. Conclusion p. 102 9. Bibliography p. 103 10. Appendix p. 111 Appendix A p. 111 Appendix B p. 115 Appendix C p. 127 Appendix D p. 139
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1. Introduction
In recent decades, globalization of markets and infrastructures has led to the rise of transnational regulatory networks in several areas, such as telecommunication, police operations, environmental protection, the financial sector, and data protection (Bach and Newman, 2014; Bach et. al. 2016; Newman and Zaring, 2013). As Slaughter (1997: 192) pointed out, the erosion of national boundaries has limited the power of national governments within those boundaries and increased their dependence on foreign regulators, since ‘the flows of capital, pollution and weapons are too great and sudden for any single regulator to control’. This tendency has weakened the internal effect of national policies and strengthened its impact on other countries (Majone, 1997: 268). The disappearance of strict national boundaries has resulted in an increased interdependency between nations, since transboundary infrastructures require international cooperation, and wicked problems, which affect multiple states, can only be solved with joint response (Boughton, Lombardi, and Malkin, 2017). This interdependency has generated different forms of international cooperation, varying from institutionalized multilateral platforms such as the European Union or the United Nations to more informal bilateral agreements regarding information exchange between nations. Besides some exceptions such as the European Union, nations are generally unwilling to delegate power to official supranational bodies. In recent years, transgovernmental networking among national regulatory authorities is seen as a solution for the dilemma between politically barred centralization and necessary policy harmonization, since it provides an opportunity for international regulation without formal centralization of national power (Eberlein and Grande, 2005: 100-103). The emergence of regulatory networks seems irreversible, since transnational infrastructures enhance interdependency between nations and continue to require regulations across national borders. Therefore, it is expected that this type of international cooperation will further expand over time, creating dense ties between national regulators. Remarkably, scholars have neglected the formation of transnational regulatory networks, while this phenomenon is crucial for comprehending global administrative patterns and understanding the functioning of a network in general. This thesis aims to contribute to the knowledge of this aspect of transnational regulatory networks.
In general, existing academic literature on transnational regulatory networks contains especially valuable theoretical assumptions, while empirical evidence for these assumptions remains
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relatively scarce. Over the years, regulatory networks have mainly been scrutinized as a form of governance, while the concept of networks as the structure of relationships enjoyed limited attention. For example, scholars have sought functional explanations for transnational regulatory networks (Slaughter, 1997; Eberlein and Grande, 2005; Blauberger and Rittberger, 2015; Bach et. al. 2016) and analyzed the influence of regulatory networks on international policy harmonization and global governance (Newman and Zaring, 2013), the importance of and the mechanism behind information exchange within these networks (Majone, 1997), the motivation of regulatory agencies to operate on international level (Bach and Newman, 2014) and the effects of network position on domestic policymaking, policy learning and policy convergence (Bach and Newman, 2010; Maggetti and Gilardi, 2011; Cao, 2012). However, the literature regarding the formation of transnational regulatory networks remains limited. Saz-Carranza, Iborra and Albareda (2015) are some of the few scholars who have analyzed this aspect of transnational regulatory networks empirically. Their article, based on in-depth semi-structured interviews, argues that the size and power of network administrative organizations (NAO) in a network is positively related to the level of interdependency between network participants and their dependency on third parties (Saz-Carranza, Iborra and Albareda, 2015: 459). Although their qualitative research method provides rich data regarding the changing role of the NAO, the level of interdependency and its exact effect on the role of NAOs remains unclear. More importantly, their selection of interview respondents constitutes a weak spot within the research. Although snowball sampling of interview respondents is an effective method of finding relevant respondents, it could also lead to a one-sided perspective of a phenomenon, since it is likely that respondents provide access to respondents who are similar-minded. Especially when these new respondents are informed by their already interviewed colleague, bias could occur. While Saz-Carranza, Iborra and Albareda (2015) look at networks as a form of governance and focus on the development of network governance and power division within transnational regulatory networks, I argue that the formation of transnational regulatory networks should be scrutinized from a perspective which considers networks as structures of relationships.
Strikingly, the mechanisms behind network formation in general have also not been included in most articles in public administration journals. Although there are some prominent exceptions to this observation (Issett and Provan, 2005; Provan and Kenis, 2008; Andrews et.
al. 2011), most literature about network formation comes from other fields (Isset et. al., 2011: 168). Some exemplary disciplines are sociology (Galaskiewicz, 1985; Watts,
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1999; Powell et. al. 2005; Kossinets and Watts, 2009; Rivera, Soderstrom and Uzzi, 2010), economics (Glückler, 2007) and management (Hite and Hesterly, 2001; Provan, Fish and Sydow, 2007). However, the characteristics of networks in other disciplines appear to be distinct from public sector networks. For example, it is believed that network relations in the public and private sector evolve in fundamentally different ways. According to Isett and Provan (2005: 162-163), trust between private sector network actors results in a tendency towards informal interaction, while the need for formal contracts remains constant among public sector organizations. This can be explained by the regulatory requirements in the public sector environment (Isett and Provan, 2005). Furthermore, the units of analysis in networks studies in the field of sociology are often informal groups of friends or other forms of interpersonal contacts in which network actors participate on voluntary basis and could easily form and break connections without extensive consequences. According to Newell and Swan (2000: 1292), these social networks are often primarily based on personal contacts and interpersonal exchange. In contrast, public sector are often more underpinned by formal agreements and formally identified roles and coordination mechanisms (Newell and Swan, 2000: 1292). Although this difference also applies to transnational regulatory networks, this network type is comparable with informal social networks in terms of network origin: both can be categorized as a voluntary network, since both are created bottom-up by their network participants (Provan and Kenis, 2009: 449). According to Provan and Kenis (2009: 449), network participants of voluntary networks are more convinced by the need of collaboration, compared with participants of networks in which cooperation is dictated by a governmental body. Although this similarity indicates that social networks and transnational regulatory networks are comparable to an extent, public administration researchers should conduct their own empirical research by testing insights about network formation from other scientific fields on public sector networks. After all, the characteristics of public network actors and the dynamics of public network relations differ from the investigated networks in the already existing literature. Knowledge about the driving mechanisms behind transnational regulatory networks is crucial to understand their formation, which explains the current structure of these networks. It is important to scrutinize the network structure as an outcome variable, because the construction of network connections between national regulatory agencies is considered to be decisive for the functioning and the outcomes of the network (Zaheer and Soda, 2009: 1). Since academics have not reached consensus regarding the effects of networking on performance by solely focusing on the performance outcomes of public sector networks (Provan and Milward, 1995;
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O’Toole and Meier, 2004; Hicklin et. al. 2008; Meier et. al. 2015), it is time to look beyond a standardized definition of network performance and focus on the initial intention of the network. This thesis argues that the knowledge of network formation forms a crucial foundation for conducting research regarding network outcomes. Without having an understanding of the mechanisms behind network formation, we cannot distinguish and will continue to compare fundamentally different public networks (Zaheer and Soda, 2009: 1). For example, a public sector network in which network participants aim for policy harmonization is incomparable with a network in which network actors desire to increase their personal number of resources through the network, because network actors have different reasons to connect with other network participants, which leads to differences in network structure, network functioning and network performance outcomes. By identifying the purpose of networks in the first place, by looking at the mechanisms behind network formation, we are able to distinguish and compare the performance outcomes of networks which are essentially similar. Therefore, we should first analyze the network formation of public sector networks, before studying its outcomes. By conducting research regarding the mechanisms behind tie formation, we can explain the relationship and interaction between different nodes, which is necessary in order to devise causal theories about network structure, network governance and ultimately, network performance. As stated before, the necessity of this knowledge also has a more practical reason. Since globalization has forced governments to engage in transnational collaborations, it is crucial to gain a complete comprehension of network behavior and network outcomes to understand the interactions and administrative relations that have developed over recent decades. Only by recognizing the mechanisms behind the formation of transnational regulatory networks, we can predict and anticipate on the relationships and interaction between national regulators, in order to improve network outcomes. The formation of transnational regulatory networks has thus far not been recorded or researched sufficiently. This thesis aims to fill in this gap in the literature by examining the formation of transnational regulatory networks. This thesis will analyze the formation of transnational regulatory networks as a whole from a network analytical perspective, to explain the structural characteristics of these networks. This network approach looks at the network as a structure of relations and aims to describe, explain or compare its configuration (Provan and Kenis, 2008: 232-233). Therefore, the relationships that form the structure of the network are the unit of analysis (Milward and Provan, 1998: 388). It is important to scrutinize the network from this perspective, because the structure of the network is crucial for the functioning and efficiency of the network (Hafner-Burton, Kahler and
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Montgomery, 2009: 569). According to Knoke and Kuklinski (1982: 11) the structure of communication shapes the rate of knowledge diffusion and determines which network participants receive new information early. In most research characterized by this approach, scholars have conducted an egocentric network analysis and scrutinized the network from the perspective of a single node or dyad. The main weakness of this type of research is that it only provides information about a part of the network, while it is necessary to expose the functioning of the network as a whole (Provan and Kenis, 2008: 232). However, scholars who aim to study whole networks as units of analysis often look at networks from a ‘network as a form of governance’ perspective. According this approach, networks are considered as mechanisms of coordination, which provide a solution for market and hierarchy failures (Provan and Kenis, 2008: 232). Although these functional explanations are valuable, they do not contribute to an understanding of network structure, and therefore of network performance. While most literature regarding transnational regulatory networks has looked at either an egocentric network analytical approach or a whole network ‘as form of governance’ approach (Saz-Carranza, Iborra and Albareda, 2015), the academic field lacks a network analytical approach of transnational regulatory networks from a whole network perspective. This thesis will fill this gap in the academic field and aims to improve the understanding of network formation and network functioning on a macro-level, by analyzing the formation of transnational regulatory networks as a whole through social network analysis. Therefore, the central question this research proposes is:
What are the driving mechanisms behind the structure of transnational regulatory networks?
To analyze the driving mechanisms behind network formation, the international regulatory network of the financial regulators in the field of securities is examined. In this thesis, I have compared the network structure of the financial regulatory network in 1990, 2000 and 2010, based on bilateral Memoranda of Understanding (MoU) between the national regulators from 1980 until 2010. For these three years, I conducted a social network analysis and compared the structural characteristics of the network. The transnational regulatory network in the field of securities is chosen because the financial sector is among the most irreversibly interwoven, interdependent and developed fields of international cooperation. Therefore, the results of this research provide insight into the evolutionary process of the formation of transnational regulatory networks in other fields, and could be used for theorization about the formation of transnational regulatory networks in other fields. Since the academic field lacks knowledge
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regarding this subject, the findings of this thesis are valuable for the understanding of the formation of transnational regulatory networks in general.
This thesis will first give an overview of the existing academic research regarding the origin and formation of transnational regulatory networks in general. The third chapter will provide the theoretical framework and hypotheses of my research. Since the literature on network formation is limited in the field of public administration, this part of the thesis will mostly rely on articles from other disciplines. The fourth chapter will elaborate on the research methodology, case selection and data collection. The fifth chapter informs the reader about the internationalization of the securities market and its effect on the role of financial regulators. The results of my research can be found in the sixth chapter and will be further discussed in the seventh chapter. Finally, a conclusion to my research will be provided, together with some closing remarks.
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2. Literature review
Globalization has led to an increased interdependency between countries in several fields (Newman and Zaring, 2013). The necessity of transnational cooperation has resulted in different forms of international cooperation, such as supra-state, sub-state and non-state actors. While supranational authorities, such as the European Union and the United Nations, have difficulty expanding their power (Slaughter, 1997), sub-state actors, such as national regulators, increasingly cooperate on a less institutionalized basis with their counterparts abroad. This literature review will focus on the latter form of cooperation and provides an overview of the current academic scholarship regarding the origin and functioning of transnational regulatory networks. The observed network characteristics are crucial to determine the driving mechanisms behind the network formation, which lead to specific structural characteristics of transnational regulatory networks. In order to gain a complete understanding of these networks, the following part of this thesis will discuss transnational regulatory networks in general, various network characteristics and the importance of network structure for information exchange within the network.
2.1 Transnational regulatory networks
Transnational regulatory networks consist of multiple national regulatory agencies, which aim to implement coherent international policies (Bach et. al., 2016). Although scholars like Keohane and Nye (1977) detected their existence already in the 1970s (Newman and Zaring, 2013), the phenomenon gained increasing attention after it was mentioned by Slaughter in 1997. She observed how functionally distinct parts of disaggregated nation states, such as courts, regulatory agencies, and executives, sought rapprochement with their counterparts abroad (Slaughter, 1997: 184). According to the interdependence theory, governance networks are interorganizational mediums, which provide interest mediation between interdependent, but conflicting actors, who have their own rule and resource base (Levi-Faur, 2012: 8). The networking behaviour of national regulators enables them to develop transnational strategies to solve problems which affect multiple countries, such as internationally organized terrorism, global warming and bank failure. Because countries are unable to fight these problems alone, they are highly dependent on the behaviour of other countries (Gulati and Gargiulo, 1999; Newman and Zaring, 2013). Together, these national regulators form transnational regulatory networks in which they discuss these issues, exchange information and harmonize policy
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regulations to counteract transnational problems. In this so-called joint problem solving, national regulators behave as quasi-autonomous foreign policy actors, steering the international strategies according to their own ideas (Bach et. al., 2016: 20). Slaughter (1997) argues that the decisions regulators make together are in general not formally binding. However, they do lead to policy harmonization, since individual members often implement the internationally designed regulations (Majone, 1997: 265; Eberlein and Grande, 2005: 105; Maggetti and Gilardi, 2011: 843; Bach and Newman, 2014: 396) and aim for policy harmonization and cooperation (Saz-Carranza, Iborra and Albareda, 2015: 450). According to Newman and Posner (2016: 125), regulatory agents use transnational soft law as a political resource to steer domestic policy in the direction they prefer. In this way, the regulatory network coordinates global standards (Newman and Posner, 2016: 130). Since it is crucial to have similar standards to regulate and control transnational infrastructures, policy harmonization is an important result of the interaction within these regulatory networks.
Besides the creation of similarity in standards and regulation, knowledge exchange also appears necessary for the functioning of the transnational regulatory network because the information level and performance of individual network actors depend on the information and performance of others (Majone, 1997; Gulati and Gargiulo, 1999; Newman and Zaring, 2013). According to Porter (2011: 177), consistent ideas among diverse regulators are necessary for international cooperation, since like-minded institutions are more likely to implement similar regulations. Therefore, the more actors share their thoughts, the harder it is for single regulators to ignore these ideas (Porter, 2011: 177). Eberlein and Grande (2005: 101) even argue that ‘information is the most important resource for informal coordination through transnational regulatory networks’, since regulatory policy is knowledge-based, and the availability and dissemination of credible information appears to be the most effective instrument to harmonize national policies. In other words, knowledge exchange is crucial, because national agencies that possess similar information are more likely to implement similar policies, which is necessary to regulate transnational infrastructures. This statement is supported by a study of Majone (1997: 264), who confirms that regulatory strategy is based on information and states that the transfer of credible information drives the regulatory network. The mechanism of knowledge exchange is applicable in multiple fields, such as customer protection, health risks, and insurances, and appears crucial to counteract transnational problems and improve transnational needs by sharing best practices. Since regulatory strategies are knowledge based, network agencies act as information brokers (Eberline and Grande, 2005: 101). Clearly, academic research has shown
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that exchange of knowledge is essential for the functioning of transnational regulatory networks. However, the question arises why transnational regulatory networks are formed in the first place.
While supranational institutions such as the European Union have an evident authority and are more efficient in international policymaking, governments have shown a preference for the more cumbersome form of transnational regulatory networks over recent decades (Slaughter, 1997). This choice can be explained by the desire of national governments to retain the decision-making authority over their country and people. As Bach et. al. (2016: 20) point out, national bureaucracies are only willing to cooperate on an international level, if ‘the shift to a higher level of governance does not endanger their autonomy, reputation, or legitimacy.’ Although national powers are unwilling to delegate power upwards to superior institutions, wicked problems and transnational infrastructures require a consistent response and international cooperation. According to Eberlein and Grande (2005), transnational regulatory networks provide a solution to this dilemma. They argue that transnational regulatory networks stimulate international cooperation and policy harmonization, while allowing national governments to maintain formal authority. In this way, international regulations are not forced upon countries, but formed naturally by dense collaboration and the exchange of best practices. The transfer of knowledge influences the behaviour of national regulatory agencies and facilitates the development of behavioural standards, working practices and shared expectations (Majone, 1997). The advantage of transnational regulatory networks lies in the possibility for national governments to maintain their autonomy, while simultaneously harmonizing policies on an international level. This explains the existence and increasing success of transnational regulatory networks.
In brief, national regulators design non-binding international policies together and implement their individual interpretations of these policies on a national level. In this transnational cooperation, the exchange of information between network actors appears to be crucial in policy harmonization, which is necessary to regulate and control transnational infrastructures. The success of this type of international cooperation can be explained by the possibility that it provides for national governments to maintain their autonomy in this policy harmonization process. Before I discuss the role of the network structure in the level of knowledge exchange and explain why particular networks are successful while others are not, I will elaborate on the different network forms and characteristics in the next paragraph to clarify the nature and characteristics of transnational regulatory networks as a whole, after which I will discuss the
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role of network structure in enabling a certain level of knowledge exchange, and explain why particular networks are successful while others are not.
2.2 Network forms and characteristics
Scholarship within the field of public administration has distinguished three types of public sector networks: the policy network, the collaborative network and the governance network (Isset et. al. 2011). First, a policy network exists of interdependent public and private interest groups which have a similar interest in public decisions within a particular area of policy (Newman and Zaring, 2013: 248-249). According Howlett, Mukherjee and Koppenjan (2017: 233-234), actors in policy networks ‘develop and contest strategic or technical policy ideas and concepts to pursue their own interest and ideas in the context of institutional or collective arrangements in which policy processes unfold’. They are interdependent, aim for the same goal and focus on decision making in their own interest (Isset et. al. 2011: 158). The second type of networks, the so-called collaborative networks also consist of public and private actors, but concentrate on the provision and production of collaborative goods and services, such as public health care and national security, for the general public. Cooperation within a collaborative network is necessary, because individual agencies are unable to deliver these goods and services alone (Isset et. al. 2011: 158). According to Torfing (2012: 3), the third network type, named governance networks, can be defined as ‘horizontal articulation of interdependent, but operationally autonomous actors from the public and private sector, who interact with one another through ongoing negotiations that take place within a regulative, normative, cognitive, and imaginary framework; facilitate self-regulation in the shadow of hierarchy; and contribute to the production of public regulation in the broad sense of the term.’ He mentions think tanks, strategic alliances, public boards, and committees as examples of governance networks (Torfing, 2012: 5). While network actors of policy networks focus on the actual policymaking, and the network actors of collaborative networks concentrate on the production of goods and services, the actors of governance networks only interact by coordinating organizations toward a common goal (Isset et. al. 2011: 158).
Although the characteristics of transnational regulatory networks might measure up to all three types of public networks, this thesis considers transnational regulatory networks as a typical example of governance networks, since they are focused on regulation in terms of policy harmonization. In line with the definition provided by Torfing (2012: 3), the network actors in transnational regulatory networks, which solely consist of national regulators, are
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interdependent, since they rely on each other’s information and performance. Furthermore, transnational regulatory networks have a horizontal network structure, in which the network actors remain autonomous, because no one actor has the authority to force others to think or act in a certain way. Although network participants have a common goal, they often have different ideas about transnational regulatory strategies, and aim to steer international strategies according their own ideas (Bach et. al., 2016: 20). Therefore, deliberations in transnational regulatory networks, which aim to achieve a common understanding of problems, challenges and solutions, can be seen as a form of negotiating. According to Torfing (2012: 4), the self-regulatory capacity of governance networks is controlled by public authorities that influence institutionalized negotiations in networks and often threaten to take over when governance networks are unable to deliver. This also applies for transnational regulatory networks: the dynamic between regulatory agencies, national governments and supranational institutions is often described as ‘double delegation’, because the national governments and supranational institutions (such as the European Union) are both sources of delegated authority (Coen and Thatcher, 2008). Since overarching organizations as well as national governments formally restrict the self-regulatory capacity of national regulators, transnational regulatory networks indeed operate ‘in the shadow of hierarchy’. Interaction and self-coordination among various national regulators could lead to international policymaking, without the hierarchical coordination of formal policy-making institutions (Scharpf, 1994). Lastly, transnational regulatory networks also contribute to the production of public regulation by joint problem solving and harmonizing policies. In conclusion, since transnational regulatory networks meet all relevant characteristics, they can be categorised as a form of governance networks.
Furthermore, the existing network literature also distinguishes voluntary networks from mandated networks (Provan and Kenis, 2009). As stated before, voluntary networks are generated by the network actors themselves, who are willing to participate in the network and recognize the importance of cooperation, while mandated networks are established by a third party, which is often a government agency (Provan and Kenis, 2009: 449). According to Gage and Mandell (1990: 36-37), cooperation on a voluntary or exchange basis is fundamentally different from cooperation on a mandated or power-dependence basis, and affects the coordinating mechanisms within the network. While coordination in voluntary networks is initiated by the participating organizations, cooperation in mandated networks is coordinated by interorganizational legislation or a third party (Gage and Mandell, 1990: 36). Although it could be argued that national regulators are forced by the globalization process to cooperate,
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since they are unable to control transnational infrastructures on their own, this thesis claims that transnational regulatory networks can be categorized as voluntary networks, because the cooperation between these network actors has developed naturally over time and the network itself lacks an overarching coordinating body with formal power. Therefore, national regulators decide autonomously with which regulators they want to connect, and take responsibility for their own resource availability: credible information and best practices from other regulators. These individual choices result in a network structure which affects the information diffusion through the network (Provan, Fish and Sydow, 2007: 502). In order to better understand the development of such a network structure, it is worth investigating on what grounds network participants select their partners.
Besides the different types of public networks and the network origin, as described above, the academic literature also makes a distinction between formal and informal networks (Isset et. al. 2011). While both formal and informal networks aim to share information, build capacity and solve problems, the most important difference lies in the formalization of the collaboration. On the one hand, formal networks officially record and thus secure their cooperation, for example with treaties, contracts, joint agreements or Memoranda of Understanding, while on the other hand informal networks do not bind their members by such arrangements (Isset et. al. 2011: 162-166). The meaning of the formalization of network ties seems ambiguous. On the one hand, scholars have argued that a closed structure of formal public sector networks results in a greater level of trust among its participants (Isset et. al. 2011: 164). Since organizations prefer stable relations (Gulati and Gargiulo, 1999: 1440), informal public sector networks have the tendency to become formalized over time (Isset et. al. 2011: 165). From this perspective, the formalization of a network tie indicates an enduring cooperation with a trustworthy partner. On the other hand, research from the private sector has pointed out that network relations become more informal over time (Isset and Provan, 2005). They have pointed out that the need for formalization of agreements indicates a level of distrust among network actors (Isset and Provan, 2005). In the case of transnational regulatory networks, the interaction between network participants takes place on both a formal and informal level. The operationalization of the network ties will be discussed in the research design.
Whether a network is formal or informal, voluntary or mandated, the strength of the relationships within the network is important for the functioning of the transnational regulatory network. This is because it indicates the level of trust and information-exchange between the network actors, since network actors are not likely to enhance a relationship after receiving
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information of low quality (Majone, 1997: 273). The strength of network ties varies between strong, weak or absent, and is defined by ‘a combination of the amount of time, the emotional intensity, the level of mutual confiding and the reciprocal services which characterize the tie’ (Granovetter, 1973: 1361). Some scholars claim that information will be transmitted over a larger part of the network when passed through weak ties, since it will spread through more various cliques in which network actors densely cooperate (Granovetter, 1973: 1366). The article from Granovetter (1973: 1378) argues that strong ties lead to general fragmentation, while weak ties lead to integration among cliques within the network. However, in the case of transnational regulatory networks, strong ties are more valuable compared to weak ties, because strong ties indicate a certain level of trust. This is crucial, because trust is essential for effective network agreements and the creation and diffusion of information, since network actors are more likely to share information with, and build on knowledge from connections they trust. Because the information in transnational regulatory networks is essential for policy harmonization and the quality of international regulatory strategies, network actors will carefully assess the credibility of their information. In transnational regulatory networks, network actors tend to rely on information from connections that they have strong ties with, since they have proven their trustworthiness in former interactions. Although information could spread faster across the network through weak ties, it will have limited impact in the process of policy harmonization, because network actors prefer the information from sources that they have an enduring relationship with. Therefore, the strength of network ties indicates the diffusion of credible knowledge in transnational regulatory networks.
In brief, transnational regulatory networks are stable governance networks, in which network participants cooperate on both formal and informal levels. The interdependent, but autonomous, national regulators aim to achieve a common understanding of transnational problems, challenges and solutions, and rely on information from partners who have proven their trustworthiness in former interactions. Since transnational regulatory networks are established in a bottom-up approach, these networks can be categorized as voluntary networks, in which the participants recognize the need for cooperation. Now the phenomenon of transnational regulatory networks has been explained, the next paragraph will discuss the existing literature regarding this subject.
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2.3 The academic field
While scholars have examined a wide variety of aspects of transnational regulatory networks, such as explaining the phenomenon of transnational regulatory networks (Bach et. al., 2016), the issue of delegation (Coen and Thatcher, 2008; Blauberger and Rittberger, 2015), the relation between network position and policy convergence (Cao, 2012) and the role of regulatory networks in global governance (Newman and Zaring, 2013), much of the current literature on transnational regulatory networks consists of theoretical conceptualizations or anecdotal studies, which are not backed with rigorous empirical evidence (Bach and Newman, 2014: 396). For example, Newman and Zaring (2013) discuss and compare transnational regulatory networks from the perspectives of international relations and international law. Their contribution provides insight into the general development of knowledge regarding regulatory networks and international policymaking in these fields, but the study remains anecdotal. Furthermore, Bach et. al. (2016) focus on the functional explanation of transnational regulatory networks by looking at transnational forms of governance from a bureaucratic politics perspective and paying attention to the institutional rivalry in the policymaking process. Although this research is also solely anecdotal, it recognizes the lack of systematic empirical knowledge on the actual development, functioning and effects of transnational regulatory networks (Bach et. al, 2016: 9). Even classics, such as the articles of Slaughter (1997) and Majone (1997) do not provide empirical evidence in their research. Since the academic literature regarding transnational regulatory networks generally consists of theoretical assumptions, the current literature cannot provide a solid foundation for further research, because scholars cannot build future research on unproven hypotheses. Therefore, academics must conduct empirical research, to confirm and increase our understanding of transnational regulatory networks.
Although the overwhelming majority of the academic research regarding transnational regulatory networks lacks empirical evidence, a few exceptions should be mentioned. For example, the longitudinal study of Maggetti and Gilardi (2011), which looks at the impact of network centrality on the domestic adoption of internationally developed standards, is one such exception. Based on a social network analysis of the European Regulatory network of national securities regulators, their study argues that both voluntary and compulsory international standards are adopted consistently by network participants, and that regulators from countries with relatively large financial industries often have a more central network position (Maggetti and Gilardi, 2011: 843). This suggests that the network position of individual participants
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affects their role in the network, and proves that transnational regulatory networks indeed lead to policy harmonization. These findings were in line with an earlier study by Bach and Newman (2010: 514), which also found that network participation has a positive, statistically significant effect on the adoption of transnational regulations, based on systematic empirical evidence from the international network of securities regulators. Besides providing these scientific findings regarding policy harmonization, Bach and Newman (2014) also empirically investigated the motivation of individual regulators to engage in international policymaking networks in a later study, by comparing the development of the International Organization of Securities Commissions (IOSCO) and the International Association of Insurance Supervisors (IAIS). They concluded that national regulators are more likely to participate in these networks when they are independent from domestic policymaking institutions and operate in a sector which has, in case of financial regulators, a relatively large share in the GDP (Bach and Newman, 2014: 411). In other words, the importance of the regulated sector and the domestic political situation affect the willingness of national regulators to cooperate on an international level. Although this scholarship provides valuable evidence-based insights, empirical research of transnational regulatory networks is highly exceptional. More empirical research should therefore be conducted in order to improve our understanding of these networks.
Furthermore, most researchers have investigated public sector networks with an egocentric network analysis, and scrutinized the network from the perspective of a single node (Provan and Lemaire, 2012). They focus for example on the formation of dyadic ties (Siciliano, 2017), performance outcomes (Hicklin, O’Toole and Meier, 2008) and the impact of a single organization on the network or the impact of the network on a single organization (Provan, Fish and Sydow, 2007). The analysis of the network from the perspective of a single node provides an in depth understanding of the behaviour of that particular network actor. However, it does not contribute to our comprehension of the functioning of the network as a whole, since a single network actor cannot capture the complex interactions throughout the network. Although the dominance of this research perspective has provided valuable insights into the behaviour of single network actors, it has also resulted in a limited understanding of the functioning of transnational regulatory networks as a whole. In order to gain evidence-based information and complement our knowledge regarding public sector networks from an egocentric perspective, researchers should empirically and systematically investigate networks from a whole network perspective.
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It is also remarkable that the overwhelming majority of published studies looks at transnational regulatory networks as form of governance. This approach considers networks as alternative organizational form for markets and hierarchies (Powell, 1990). While markets have a spontaneous coordination mechanisms and hierarchies have clear lines of authority, networks do not confine their participants within solid structures. They do, however, contain dense cooperation between network actors. Research which looks at network from a ‘network as form of governance’ approach, focuses mainly on the interaction within the network and the role and behaviour of network participants. In contrast with this approach, the network analytical perspective focuses on the objective characteristics of the network, such as the density and the centrality. Since these characteristics could further our understanding of the functioning of the network (Hafner-Burton, Kahler, and Montgomery, 2009), it is also important to investigate networks from a network analytical perspective.
2.4 The importance of network structure
Several scholars have defined networks as ‘a group of three or more organizations connected in ways that facilitate achievement of a common goal’ (Provan, Fish and Sydow, 2007; Provan and Kenis, 2008; Provan and Lemaire, 2012). The presence of a common goal distinguishes networks from other forms of collaboration, such as multiple dyadic interorganizational relationships. Although not all participants need to be connected with each other in a network (Provan and Lemaire, 2012: 640), the presence and characteristics of the existing connections determine the patterns of communication and information exchange, because nodes which are connected are more likely to share information and therefore harmonize policies and strategies. Since the ties between network actors indicate the information flows in transnational regulatory networks, the existence of these connections is crucial for the diffusion of knowledge among network participants (Hafner-Burton, Kahler, and Montgomery, 2009). In other words, network actors with many connections have more information resources and are more likely to receive credible information in comparison to isolated network participants. Research of private sector networks has confirmed this statement: Keister (1998) has empirically shown that firms with close relations in a business group improve their performance with the information of others. She argues that these cooperating firms become more similar and tend to use the same technology over time (Keister, 1998: 429). Cao (2012) also found empirical evidence for the effect of dense collaboration and knowledge exchange on policy harmonization. Based on his study on international markets, he claims that ‘the similarity of network positions induces convergence in domestic economic policies as a result of competitive pressure’ (Cao, 2012:
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410). As the previous paragraph pointed out, this conclusion regarding policy harmonization is confirmed by empirical research regarding transnational regulatory networks on European level (Maggetti and Gilardi, 2011).
While the position of regulatory agencies in the network reveals something about their resource availability on micro level, the overall network structure determines the diffusion of knowledge and the level of policy harmonization throughout the network as a whole (Sandström and Carlsson, 2008). Therefore, the network structure determines the level of efficiency of the distribution of information and resources across the network, and thus determines the outcomes of the network on macro-level (Jackson and Watts, 2001: 265; Hafner-Burton, Kahler, and Montgomery, 2009: 569). For example, since nodes tend to share high-quality information with network actors they have an enduring relationship with (Majone, 1997: 272-274; Gulati and Gargiulo, 1999: 1440), information will spread relatively easy through a transnational regulatory network when network actors have a higher average of network connections. Because transnational strategies and policy harmonization are more likely to occur when network actors possess similar information, the outcome of transnational regulatory networks is indirectly affected by the density of the network. However, when networks are too dense, they cannot function effectively. Another structural characteristic which influences the distribution of knowledge through the network is the level of network centralization (Hafner-Burton, Kahler, and Montgomery, 2009: 569). According to scholarship, centralized networks operate more efficiently, because information can quickly pass across the farthest reaches of the network (Newman and Zaring, 2013: 251). This could have a positive effect on the realization of transnational strategies and policy harmonization, depending on the intentions of the central network actor. Furthermore, the presence of cliques in the network also affects the outcomes of the network, since network participants tend to share information within their sub-groups (Kinne, 2013). Therefore, it is likely that policy harmonization takes place within the groups, and not across the whole network. As the three examples have pointed out, the structure of a network is crucial for the network outcomes. In the next chapter, I will further elaborate on different network structures and their effects on the functioning of transnational regulatory networks.
Since the structure of networks has proven to be crucial for the level of knowledge exchange and policy harmonization in transnational regulatory networks, it is important to understand the driving mechanisms behind the formation of the network structure. Only by fully comprehending which factors affect the structure of the network, we are able to explain why
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particular networks are successful, while others are not. Therefore, the relationship between network formation and network structure needs to be scrutinized. However, academic literature regarding network formation within the field of public administration has remained limited. The following paragraph will provide an overview.
2.5 Network formation
Although it is necessary to understand the driving mechanisms behind network structure, scholars have pointed out that the academic literature in the field of public administration on the formation of whole networks in the public sector has remained limited (Isset and Provan, 2005; Provan, Fish and Sydow, 2007). So far, only Saz-Carranza, Iborra and Albareda (2015) have conducted an empirical research regarding a structural aspect of transnational regulatory networks. They focus on network governance and scrutinize the role of network administrative organizations (NAO) in the policy-mandated networks in the field of telecom and energy. The main argument of their research is that the development of network governance is not pre-determined, but a result of the bargaining position of the network actors, which is affected by the size and power of the NAO and the level of interdependency between network participants, and their dependency on third parties (Saz-Carranza, Iborra and Albareda, 2015: 459). In other words, the development of network governance is determined by power bargaining. This statement is supported with a qualitative research method of semi-structured interviews, which have provided in-depth data regarding the changing role of the NAO. However, a qualitative research method will provide a subjective view of the level of interdependency and its exact effect on the role of NAOs, since respondents have their own interpretation of the situation. Furthermore, their method to find interview respondents is a serious weakness. Although snowball sampling of interview respondents is an effective method to find relevant respondents, it could also lead to a one-sided perspective of a phenomenon, since it is likely that respondents provide access to respondents who are similar-minded. Therefore, the qualitative research of Saz-Carranza, Iborra and Albareda (2015) should be complemented with a quantitative research that scrutinizes the actual structure of the relationship between network actors, to find more empirical evidence regarding the development of networks from a network analytical approach. This literature review has pointed out that the understanding of network formation is crucial to explain the network structure, which affects the performance and outcomes of transnational regulatory networks. However, scholars have mainly focused on the existence of the phenomenon itself (Keohane and Nye, 1977; Slaughter, 1997; Newman and Zaring, 2013; Bach
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et. al., 2016), its functioning (Majone, 1997; Eberlein and Grande, 2005) and outcomes (Hafner-Burton, Kahler, and Montgomery, 2009; Cao, 2012). Besides the work of Saz-Carranza, Iborra and Albareda (2015), the development of structural network characteristics has not been recorded so far. As stated before, the structure of networks should be the foundation for network comparison, because it provides the basis for potential network performance and indicates similarities in mechanisms behind network formation. Since the structure of a network appears to be crucial for the level of information exchange and policy harmonization in transnational regulatory networks, the current academic knowledge is insufficient to explain why some networks are successful, while others are not. In order to comprehend the present network structures and performance outcomes, we should focus on the driving mechanisms behind network formation.
Overall, two main shortcomings in the public administration literature regarding transnational regulatory networks can be identified. First, most research regarding transnational regulatory networks has only remained conceptual and lacks empirical evidence. This is problematic, because scholars cannot build their future research on unproven hypotheses and theoretical assumptions. In addition, most researchers have conducted an egocentric network analysis, and analyzed the network from the perspective of a single node (Provan and Lemaire, 2012). Second, scholars have primarily examined transnational regulatory networks from a ‘network as form of governance’ approach. As this literature review has pointed out, networks should also be scrutinized from a network analytical approach. This thesis will contribute to this gap in the academic field, by scrutinizing the network as a whole and providing empirical evidence of the mechanisms behind network formation with a social network analysis. A network analytical perspective, based on empirical observations of the formation of transnational regulatory networks, will contribute to our understanding of network functioning and network evolution, since it analyzes relevant characteristics of the network structure, such as network density and network centrality. With this research method, network structure will be examined as the result of the behavior of network participants (Newman and Zaring, 2013: 250). Since this thesis aims to investigate the mechanisms behind network formation, the following chapter will provide an overview of three characteristics of network structure and the possible mechanisms behind their development.
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2.6 Summary
Transnational regulatory networks consist of multiple national regulatory agencies, which aim to harmonize regulatory policies and cooperate on an international level. Although there is a substantial amount of literature regarding this subject, most articles contain solely theoretical propositions of crucial subjects such as network formation and network structure, and lack empirical evidence in general. This thesis aims to fill this gap and will provide empirical data for the theoretical assumptions about the driving mechanisms behind network structure. The following chapter will combine the literature of transnational regulatory networks, network structure and the mechanisms behind tie selection to formulate hypotheses regarding network formation and network structure.
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3. Theoretical framework
As the literature review has shown, the network structure determines the level of efficiency in the distribution of information and resources across the network (Hafner-Burton, Kahler, and Montgomery, 2009: 569). However, the academic field of public administration lacks knowledge regarding the formation of transnational regulatory networks, although this is crucial to understand the driving mechanisms behind network formation. This thesis aims to fill this gap in the academic literature and examines the driving mechanism behind the formation of transnational regulatory networks. This research will mainly rely on sociological, economical or managerial literature, due to the scarcity of research concerning network development in the field of public administration. Within this theoretical framework, I will provide hypotheses regarding the development of the network from a whole network perspective and the origin of three characteristics of network structure: the level of overall network density, the level of network centralization, and the presence of network clustering. To comprehend the formation of networks and network positions in general, I will start with a paragraph about network embeddedness.
3.1 Network embeddedness
The transnational regulatory network exists of multiple national regulators, which operate in the same field and sometimes cooperate on a bilateral level. Although network participants prefer stable relationships, which are characterized by trust and rich exchange of information (Gulati and Gargiulo, 1999), nodes have no capacity to attach to all other network actors, since it takes time to build trust and nourish relations between network participants. Therefore, network participants have to be selective in deciding with whom they want to form a relationship. According to Bathelt, Malmberg and Maskell (2004: 43), network actors first need to develop a shared institutional context, which enables joint problem-solving, learning, and knowledge creation, before establishing ‘network pipelines’, through which information can flow. The more network participants interact positively, the more trust they build and the more embedded their connection will become (Glückler 2007: 624). Over time, these embedded relationships accumulate into a network that becomes a growing repository of information on the availability, competencies, and reliability of prospective partners (Gulati and Gargiulo, 1999: 1440). The structure of these embedded relationships illustrates how information will flow through the network, because nodes will share information with network connections they
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have a relationship with. This network structure of embedded relationships will influence with which network participants nodes prefer to establish new connections. In the words of Gulati and Gargiulo (1999: 1441): interorganizational networks are the evolutionary products of embedded organizational action, in which new alliances are increasingly embedded in the very same network that has shaped the organizational decisions to form those alliances.
The level of structural embeddedness of single nodes in the network indicates their level of trustworthiness, reputation and influence (Provan, Huang and Milward, 2009: 874; Kinne, 2013). This can be explained by the mechanism in which organizations use past experience with linkage partners as an indicator of likely future behavior and trustworthiness (Isset and Provan, 2005: 151). If network actors receive low-quality information from a network partner, they are not likely to intensify this relationship (Majone, 1997: 273). However, when network actors provide each other with credible information, connections will be formalized over time. In his longitudinal study, Gulati (1995) observed this mechanism over a period of twenty years and confirmed that previously connected firms are likely to strengthen their alliances. Familiarity with goals and capabilities appeared to be the most important reasons (Gulati, 1998: 301). This is confirmed by Zaheer and Soda (2009), who found that individual specialists prefer to replicate former connections in future cooperation, when their past experiences appeared to be pleasant. The frequency of repetition does not only measure the strength of a relationship, but also indicates the level of trust and the social embeddedness between nodes (Glückler 2007: 624; Rivera, Soderstrom and Uzzi, 2010: 99). This correlates with the level of information exchange: a high frequency of repetition results in a high level of information exchange (Rivera, Soderstrom and Uzzi, 2010: 99). Since network actors tend to intensify their relation with a positive experience, the embedded relationships will be formalized over time (Gulati and Gargiulo, 1999). In transnational regulatory networks, these embedded relations will be formalized with a bilateral Memorandum of Understanding (Slaughter, 1997; Newman and Zaring, 2013). Formalized relationships indicate a dense connection with a high level of trust and information exchange between the network actors, which is important for the functioning of the network. This tendencyhas consequences for the level of network density over time. The next paragraph will elaborate on this matter.
3.2 Network density
The structural aspect of network density indicates the overall connectedness of nodes within the network (Provan, Fish and Sydow, 2007: 507). This is calculated by the existing number of network ties, divided by the total possible network ties. In a network with a high density level,
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the average number of connections between nodes is relatively high. As the literature review pointed out, a high level of network density could be beneficial for the functioning of transnational regulatory networks. Information could spread relatively easily through a network when the network actors have a high average of strong network connections, because nodes tend to exchange high-quality information with network actors who have proven their trustworthiness (Granovetter, 1973; Majone, 1997: 272-274; Gulati and Gargiulo, 1999: 1440). Since transnational strategies and policy harmonization are more likely to occur when network actors possess similar information, a high network density could have a positive effect on the development of international strategies and policy harmonization. In networks with a low level of network density, however, information will be diffused to a lesser extent, because network actors have fewer network partners with whom they have agreed to share their information. This could have negative effects on the network outcomes, because information exchange is crucial for the functioning of networks. In short, the level of network density is crucial for the level of information exchange in a transnational regulatory network.
Based on the characteristics of transnational regulatory networks, as pointed out by the literature review, an increasing number of formalized connections between nodes over time can be expected. First of all, transnational regulatory networks are formed because the interdependency between nations, caused by globalization, has forced national regulators to cooperate (Slaughter, 1997). Over the years, transnational infrastructures have become denser on multiple levels, which increases the functional necessity of international cooperation. In other words, the expanding globalization has weakened the position of single governments and increased the need for national regulators to cooperate with their counterparts abroad. Secondly, the literature has pointed out that embedded relationships will be formalized over time (Gulati and Gargiulo, 1999). Because governments are forced to cooperate with each other, it is likely that some will build embedded relationships over time. According to the theory, these embedded relationships in transnational regulatory networks should be formalized with a Memorandum of Understanding over time. Third, because national governments are unwilling to delegate power upwards (Bach et. al. 2016), I expect that the role of transnational regulatory networks will be preferred over supranational organizations in international policymaking. The need for cooperation in transnational regulatory networks will result in more connections and a denser network over time. In line with these observations, Venkatraman and Lee (2004) found empirical evidence that the overall network density tends to increase over time in the private sector. For all reasons mentioned above, the first hypothesis is formalized as follows:
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(H1) The structure of transnational regulatory networks will become denser over time.
Although I expect transnational regulatory networks to become denser over time, it is also important to keep in mind that network participants have no capacity to maintain an unlimited amount of network actors, since it takes time to build trust and nourish the relation between network participants(Gulati and Gargiulo, 1999). Furthermore, a large number of external relations constrains the autonomy of individual nodes, since nodes are forced to take the opinion of their relations into account to maintain closely connected. Therefore, it is also possible that network participants select a limited number of nodes to form a relation with. However, these observations do raise questions. For example, which network participants will form connections? What is the reasoning behind these choices? And how will this affect the network structure of transnational regulatory networks? The answers to these questions will reveal the mechanisms behind network formation. Some scholars argue that network formation can be explained by the process of preferential attachment, which leads to high network centrality. The next paragraph will elaborate on this structural characteristic.
3.3 Network centralization
Network centralization calculates to what extent the structure of the network is organized around particular focal points. This is not only determined by the connections in the network, but also by the position of the individual actors in the network (Scott, 1991: 92; Provan, Fish and Sydow, 2007: 485). Scott (1991: 92) describes it as ‘an expression of how tightly the graph is organized around its most central point’. While network centralization looks at the overall structure of the network, network centrality describes the extent to which a single organization is connected to other organizations, and identifies which nodes in the network are more centrally connected than others (Provan, Fish and Sydow, 2007: 507). Although network centralization and the network centrality are fundamentally different, they are strongly and positively connected with each other. Both the overall network structure and the position of single organizations in the network affect the information that is transferred through the network (Provan, Fish and Sydow, 2007: 502). Since short connections between nodes lead to a more proximate network and close lines of communication, a high network centralization could result in more efficient cooperation, depending on the capabilities and willingness of the central actor. A network structure which is highly centralized could be beneficial for the exchange of information, because its hub-and-spoke structure allows a quick transfer of knowledge across the farthest reaches of the network (Freeman, 1978; Newman and Zaring, 2013: 251). Therefore, a centralized network structure is beneficial for networks in which
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information exchange is crucial. On the other hand, a centralized network structure is also vulnerable, because other network participants depend on a hub for information (Everett and Borgatti, 2005). According to Yamagishi, Gillmore and Cook (1988), the key node becomes more powerful, as the network becomes more centralized. Because all information passes through the central network participant, this actor could take advantage of his position by influencing the approval of standards at the network level and directing the flow of information within the network in his favor (Yamagishi, Gillmore and Cook, 1988: 838; Maggetti and Gilardi, 2011: 383). Furthermore, if the central node disappears, the network will quickly break down. Networks which are less centralized have a more robust network structure, but may produce inefficient and insufficient coordination. In line with this statement, Hafner-Burton, Kahler, and Montgomery (2009: 569) argue that network structure determines the level of efficiency in the distribution of information of resources across the network. Nowadays, centrality is one of the most important and widely used conceptual tools for analyzing social networks, and can be measured in multiple ways (Everett and Borgatti, 2005: 57).
A highly centralized network is the result of a ‘rich get richer’ principle and follows the mechanism of the preferential attachment hypothesis, which states that popular nodes are more likely to receive new ties in the future than those with fewer ties (Wagner and Leydesdorff, 2005; Glückler, 2007; Zaheer and Soda, 2009). This mechanism can be explained by several theories. According to the theory of ‘resource dependency’, centralized networks are the result of a tendency in which network actors aim to attach to nodes which possess valuable resources (Galaskiewicz, 1985). In networks driven by the resource dependency mechanism, tie selection is a competitive process. Nodes with highly valuable resources are relatively popular to form connections with, since actors aim to obtain resources from the network. However, nodes have a limited capacity for external relations and will therefore aim to connect with the most valuable network participants who show mutual interest. This principle reinforces the centrality of popular network actors, because the central nodes could benefit from this mechanism with a cumulative advantage (Rivera, Soderstrom and Uzzi, 2010: 103). Multiple network actors offer partnership to an already popular node, because this relation will also increase their own resource availability and popularity. However, the central node will choose the most resourceful among all options and will therefore increase its own value and popularity even further. This mechanism will eventually lead to asymmetry in the network, from which a dominant core could take advantage. Furthermore, preferential attachment could also occur, because actors looking for new connections use an actor’s degree as a performance indicator (Rivera,
