Chapter 6.4 has offered a detailed summary of the major insights and interpretations elaborated in this thesis. The presented theoretical as well as empirical evidence suggests that the progress in the gas market liberalization is essentially the result of the growing power of the Commission and an increasing number of member states and interest groups. The theoretical analysis through agent based simulations shows that the suggested combination of explanatory factors leads to the observed outputs only under the condition that the Commission remains the most powerful actor in terms of resources and consequently – because network creation highly depends on resources – the most central actor in terms of the flow of information. The insight that the Commission’s leadership was decisive for greater gas market liberalization has been emphasized by many other scholars; they have not however tried to look at this process in combination with the increasing group size. On the contrary, the studies that have analyzed the effect of enlargements on the speed of the decision-making have argued that the decision-making in the European Union is slowing down. The major conclusion thus suggests that generalizations about the role of the group size on the speed of the decision-making should be cautious: As long as the structures used for the flow of information remain stable and contain a central actor, the increasing size does not have to affect the speed of the decision-making.
Moreover, the conducted simulations in Chapter 5 offer new theoretical implications. First of all it was shown that different theoretical strands could be combined under the Rational Choice framework by disaggregating the process of decision-making in three steps: (a) preference distribution, (b) network creation and (c) dynamics of networks and choice; and by assuming that the micro mechanisms behind these processes are based on actor’s utilities. The simulations thus
represent a rare combination of Rational Choice and network analysis and offer a basis for more sophisticated models.
The quantitative analysis in Chapter 6 offers a new operationalization of policy change. This was particularly challenging given that time series analyses of policy change are rare. I have presented the theoretical argument that SQ can be systematically measured by looking at legislative acts in force at a given period of time and that then policy change can be defined as a difference between SQ at time t and SQ at time t-1. The application of this operationalization strategy was shown to be viable and easy to conduct and it thus paves a way for large scale policy change analysis. Moreover, it was utilized as a dependent variable in binomial zero inflated regressions in Chapter 6.3.2 to systematically test major explanatory variables and to show that increasing number of member states is associated with the increasing probability of policy change.
The major weaknesses of the current analysis – as previously indicated in Chapter 6– is the short period included in the quantitative analysis and imperfect operationalization of certain variables of interest. Therefore, in chapter 6 I have suggested to consider the quantitative analysis as a pre-test for a larger data-set collection. Generally, I have tried to overcome data problems of a dynamic analysis of policy change by combining simulations (to explain micro processes), qualitative and quantitative data collection (to explain macro processes). Based on this, there is a variety of possible extensions of this work.
In chapter 6.4 it was shown that the suggested combination of micro-processes can replicate the empirical dynamics of policy change. The ultimate test of this simulation model would however be to see whether it can predict policy outcomes, similarly to the analysis conducted by Stokman
& Berveling (1998). However, this would require a larger data set with better operationalizations of preferences/choices and networks. Albeit, for the gas sector such data does not exist (as of August 2013), such a test could be possible within the agricultural sector, where different network data sets are available (Henning, 2009). Better data on choices and preferences could also enable an extension of the binary choice mechanism to a more complex one that would measure choice on a continuous scale.
The qualitative analysis in Chapter 6.2.2 has shown that member states prolong negotiations by introducing new issues into the general debate. This was repeatedly utilized by France by introducing a debate on “flexible liberalization” or “third party access” for unbundling. The future simulation could therefor take this strategy into consideration. The basics for theorizing about this process based on cost benefit analysis (when does it pay of to introduce a new concept into the general discourse) are provided in the study of discourse dynamics by Leifeld (2013a).
Additionally, the current analysis has treated liberalization as an equally salient issue for all member states. The previous studies have however shown that issue salience is an important explanatory factor for the outcome of negotiations in the European context (Schneider, et al., 2010). Future analyses could thus also introduce this mechanism as an attribute of an issue that influences the strategies of agents.
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