Data analysis

literal but idiomatic. When no suitable translation was possible, the Spanish term remains.

4.6. Data analysis

Narrative analysis

The data thus gathered as field notes, documents, interview transcripts and summaries was then organised around codes of interests using Nvivo 10 software for qualitative data analysis. To attempt to answer who were the main actors involved in the Uruguayan cannabis reform, the category

was used to organise data about the links, roles and perceptions of actors involved. It was broken down according to the six types of actors of interest defined in the sample, and more specifically to the groups or individuals involved. It included data about who were the advocacy coalitions, the political brokers and entrepreneurs, and the transference agents.

The category actors was then related to the question of what cannabis was regulated for in Uruguay.

This category included data about the problem definition of illegal cannabis for the different political actors involved; the moral foundations appealed to; the different objectives that cannabis regulation should pursue; the political tools that should be included in the law, and how to evaluate them.

The question of why cannabis regulation came to prominence and set policy agendas was disaggregated into two main categories. One included the political strategies deployed by those actors of interest as lobbying efforts, strategic venue shifting, mass mobilisation and public campaigns. It also included data about the cognitive agreements of this campaigning in terms of linguistics, framing and

T s, events not directly related with the

Uruguayan cannabis debate but that had an influence in the domestic political process. Some of the ‘

is an example of an emerging analytical code that evolved as a category of interest as I started to delve into data collected. Thus, a second wave of interviews was arranged with Latin American informants to further explore this topic.

The other two research questions relate to the derivations of the political process for the further implementation of the law and for the global debate. Data for this category was organised around

short and long- n and

the criteria according to which implementation should be evaluated. It also included opinions on the expected impact of the Uruguayan reforms for the international conventions arena as well as for other regional and domestic political processes, particularly in Latin America.

Still, as a causal process tracing study, time was another important analytical perspective underpinning the research questions. Thus, the analysis categories listed above were subsequently related with different periods of the political process in order to assess the relative importance of a given piece of evidence by reflecting on the necessity and sufficiency of the causal claims for inferential purposes. More specifically, as aforementioned, I continuously contrasted three key steps in the

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law of December 2011, sponsored by Uruguayan civil society. Secondly, the elaboration of the monopoly for its law of August 2012, sponsored by the Uruguayan Executive Power.

And finally, the approved market law of December 2013. This strategy provided

connecting the leading research questions: Who were the actors involved and how they networked?

How were the problem of illegal cannabis and the suitable political solution framed? How did cannabis regulation come to prominence and set policy agendas?

Network analysis

Social Network Analysis provided a powerful tool to study the conformation of advocacy coalitions in the cannabis debate, since it allowed focusing on the links between nodes rather than on the actors themselves. Each node being able to represent either an individual, an institution or a group, depending on the case. There are different ways of defining a network, and many times this is done by combination. The ties between individuals can be made up of a wide sample of relationships that go from resource interchange to affective evaluation. The most commonly used criteria are: (i) position-based, defined by the membership of certain institutions; (ii) Event-based, defined by the

participation in an event of interest; (iii) Relational-based, defined by chain-referral (Wasserman &

Faust, 1994; Borgatti et al., 2013).

In the context of this research, a network analysis is proffered, tracking down evidence of joint participation of political actors (both, individuals and groups) in cannabis regulation-related activities,

by previous works, and the development of the interactions among them through time. Furthermore, network analysis is a means to shed new light on the structural distribution of the linkages within the coalition, in order to analyse the importance of actors that have the ability to bridge gaps between agents that are not otherwise connected. Thus, network analysis is used to investigate the existence and evolution of advocacy coalitions in the Uruguayan case and the role of specific actors as brokers, transfer agents and entrepreneurs.

There are, however, important limitations affecting the reliability of the data gathered for network analysis that need to be highlighted. In causal process tracing terms, the network diagrams may be interpreted as a gun (Blatter & Blume, 2008); that is, a but not necessary

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necessary condition for being part of the coalition; there may have been other unidentified members as well. An additional limitation of network analysis is that it can be a highly time consuming methodological tool, whilst it also demanded considerable knowledge of relevant field data to be meaningfully constructed.

The period under consideration goes from February 2011 until the approval of the cannabis law in December 2013. Throughout this period, the joint participation of political actors in a defined sample of cannabis related events of interest was systematised using UCINET 6 software for social network analysis, developed by Lin Freeman, Martin Everett and Steve Borgatti. The activities included in the analysis range from the organisation group of the Global Marijuana March the Uruguayan version, to the ad hoc cannabis regulation advisory group summoned by the National Drugs Committee to develop the new legal cannabis framework (see annex 4). Thus defined, the links between actors represent their attendance to meetings. Actors attending many meetings together will appear closer in the diagram. Actors that never attended a meeting together are not connected by links.

Furthermore, by triangulation of the data collected, weights were attributed to the different types of participations in these events, distinguishing between organisers, speakers, and participants at one- off activities and active and passive members of serial group meetings. The first distinction was made under the assumption that organizing a one-off activity together is an indicator of a closer and more decisive link between participants than, for example, to participate as an invited guest. The weights for the serial meetings were operationalised as follows: first, setting a benchmark. Attendance at more than one meeting of the group would qualify one to appear in the diagram. I made this decision under the assumption that if an actor only went to one meeting then they were not substantially involved in the coalition. Secondly, for the actors surpassing this benchmark, the number of times they participated in the meetings was also weighted, aiming at differentiating the degree of integration of each actor within the coalition. Thus, by systematising the presence of different political actors in these events, the network analysis diagram presented shows the politica actions over time, as defined by the Advocacy Coalition Framework.

As it is analysed in this thesis, the advocacy coalition network diagram offers additional evidence on three important aspects for the research questions: first, it informs about who the actors pushing for

cannabis regulation were in the Uruguayan debate. Secondly, network analysis helps to expose the different roles and groupings of political actors within the coalition as a structure of relationships.

Thirdly, it reflects the degree of coalition cohesiveness through the number of links connecting actors.

Concluding remarks

This study generally aims at understanding why and how the Uruguayan political elite decided to put this country in the worldwide headlines becoming the first nation in creating an extensive legal framework for the cannabis market. More specifically, it questions who the main actors were behind the cannabis reform and how they networked. How the problem of illegal cannabis and its corresponding political solution was framed. How it succeeded in setting the legislative agenda and what might be the consequences of this political process for the implementation of the law and, more generally, for cannabis policymaking.

In this way, this research attempts to offer a new and complementary insight within existing literature about drug policy, more centred on policy designs than on the political and social processes that made changes possible. In order to compare and contrast different visions and positions on the topic as well as enhancing the internal validity of the conclusions arrived participant observation was triangulated with interviews and documents, aiming at studying cannabis policymaking there where policy is made.

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representatives, policy makers and civil society where different issues regarding the creation and implementation of the new cannabis law were discussed. Additionally, I conducted around 52 formal and informal interviews with key actors of the political process, both for and against the reform, to review and elaborate thoroughly the position of the research participant over certain topics of interest. Lastly, I systematised and reviewed a diverse set of documents, both of public and private access, which included parliamentary transcripts, memorandums of meetings, transcripts of radio and public demonstrations manifestos, among others.

The adoption of within-case causal process tracing as the general approach to study cannabis regulation in Uruguayan led me to reflect on the most suitable sampling procedures to employ. By combining purposive and chain-referral sampling I identified six types of actors of interest, involving Uruguayan legislative, executive and judicial power members, national and international civil society representatives and cannabis activists and a group of professionals; lawyers, a journalist, a sociologist, a historian, a hemp entrepreneur and a Pharmacies Union representative. Having defined the population of interest, I discussed more specifically ethical issues involved in entering and leaving this over-researched political elite level sample, proposing a framework of mutual give-and-take between researchers and participants fostering the research process.

The analysis strategy followed, including the rationale underpinning narrative and network analysis of the data collected. I described some of the codes used to organise the data around the research questions, informed by a temporal dimension. In this way, I compared and contrasted the political processes behind other policy options that were available and considered by the Uruguayan parliament in the period under study. Lastly, I explained how an event-based network analysis comprising information about the shared participation of political actors in different cannabis-related

events may enhance our understanding of advocacy coalitions previously identified in the literature on the Uruguayan case. Thus, the period under consideration goes from February 2011 until the approval of the cannabis law in December 2013, including elite networking activities, public demonstrations, seminars and workshops, lobbying and advisory groups, among others. This innovative analytical strategy facilitated additional evidence on three important aspects for the research questions: who the actors pushing for cannabis regulation were, their different roles and groupings of political actors within the coalition as a structure of relationships, and the degree of coalition cohesiveness through time.