Methods of data analysis

I used the qualitative analysis of a single case study design to evaluate the network effects in the Metro Manila concessions to illustrate how relational networks impact on network outcomes. A case study allows for an in-depth study of the network effects of PPP on the Metro Manila sewerage services sector to reveal the complex interactions of multiple actors at different levels (Yin 2009; Creswell 1998). While multiple case studies are deemed more compelling and more robust (Yin 2009, p.53), a single case study is more appropriate for a revelatory case, i.e. the subject matter to be observed and analysed was ‘previously

inaccessible to social science inquiry’ (Yin 2009, p.48). This thesis can be considered a revelatory case because the case of the Metro Manila concessions has not been studied from a network perspective. Existing studies have analysed the Metro Manila Water

concessions from a principal-agent and organisation-set perspective and mainly examined the water aspect and not the sewerage services (cf. Fabella 2012; Malaluan & Wu 2008; Hale 2006; Cuaresma 2006; Argo & Lacquian 2004; Esguerra 2003).

The analytic strategy I employed for this research was to follow the theoretical propositions of the case study (Yin 2009, p.130). The analytical framework presented in the first part of this chapter was derived from network governance theories, which defined the general objectives and design of the case study. It is imperative to identify the theoretical proposition and formulate the research questions prior to data collection as it helps organise the entire case study and focus attention on the necessary data and ignore the unnecessary data (Yin 2009, p.130). However, the analysis of case study evidence is challenging because it is one of the least-developed qualitative research method and investigators usually commence their case studies without having an idea of how to analyse the data to be collected (Yin 2009, p.127).

I also adopted the program-level logic model as a data analytic technique for this thesis. This logic model traces the events surrounding the implementation of a public program intervention intended to produce a certain outcome or sequence of outcomes (Yin 2009, p.150). This model also looks into the immediate, intermediate and ultimate outcomes of a policy, as well as the rival chain of events and the value of spurious external events, if any. In this case study, PPP is the public program intervention that the Philippine government adopted. Through this model, I will purposely narrate in Chapter 4 the complex chain of events in the Metro Manila sewerage service over an extended period of time leading to the sector’s current state. In section 4.2 of Chapter 4, the history of sewerage services in Metro Manila was narrated, highlighting the major legislations, policies and programs adopted and implemented by the Philippine government. The effect of these government decisions and actions are explained in Section 4.3 by mapping out the resulting institutional

framework of the Metro Manila sewerage network. As a result, there is no single government agency in the Philippine government that has oversight on all aspects of sewerage service. These developments over time have shaped the sewerage sector’s current institutional design What was created over the years of were numerous agencies and interagency bodies that have no clear delineation on how these agencies collaborate in performing their sewerage-related functions towards a common goal, if there is one. As a result, the sewerage sector in the Philippines has no precise configuration.

Through the program-level logic model, this research explores the main and rival explanations as to why the intermediate outcomes were not achieved by organising the

Metro Manila sewerage sector in arenas of interaction. The arena is where mutually dependent actors define problems, formulate solutions and make crucial decisions that impact on network outcomes (Provan & Milward 1991; Koppenjan & Klijn 2004; Gray 1985). In each arena, the main and rival explanations are identified by establishing the main problems encountered by the actors in the arena, explaining how the network features of PPP contributed to these and discussing how the network features created negative network effects and its impact on the concessionaires’ performance. The following section further explains how the program-level logic model was operationalised in analysing the data.

3.6.1 Organising the data

The first step is to convert all the recorded interviews into written text. Transcribing the interviews allowed me to become familiar not only with the contents but also on the tone or emotion of the respondents during the interview. Interview notes and summaries were also taken during the interviews. For all other sources of evidence, a brief description and summary were also written. Since this research guarantees the anonymity of interviewees, electronic and hard copies of the interviews will be coded using a three-part identification number: (1) type of actor; (2) year of fieldwork; and (3) interviewee number. There are six types of actors in the Metro Manila sewerage network: (1) government officials (GO), (2) private concessionaires (PC), (3) non-government officials (NG), (4) local government officials (LG), consumers (C) and elite respondents (E). The field notes gathered during the direct observation are labelled according to date and activity.

3.6.2 Converting texts to explanations

After the recordings were converted into texts, the next step is to convert these texts into explanations to answer the research questions (Gläser & Laudel 2013). The first step in converting texts to explanation is to link the raw data to the research question. This entails identifying, locating and structuring the relevant data gathered during the data collection process. Identifying and locating raw data includes two tasks. First is to select the texts that are relevant for answering the research question and second is subsume these texts under the appropriate category. The categories can be derived from theory and changed or supplemented according to empirical information in the text or vice-versa. I grouped the texts under the three different headings. I familiarised myself with the interviews by listening to them a number of times and re-reading the transcripts and field notes. This made it easier for me to locate the relevant texts in each file once I decided on the

according to the categories or nodes identified. I went through a number of categories before settling on the final categories below (Table 3.5):

Table 3.5 Categorising interview texts

Decision making

areas/code Actors Issues/network effect Network features

Tariff setting(TS) Private

concessionaires Regulator International arbitration panel Contract implementation Regulatory risk Dispute resolution  Resource interdependence  Goal congruence  Network management STP construction (STP)  Private concessionaires  Other private actors  National government agencies  Local government units  Local communities  Consumers Land acquisition Informal settlers Permits &licenses acquisition Right of way Competency and integrity of local officials Buy-in of local governments on the project Interagency monitoring

(IM)  Private concessionaires

 National government agencies  Non-government actors/activist groups  Interagency monitoring body

 Absence of data and system to generate that data  Unclear interagency coordination processes  Different interpretation of problems/rules

After the relevant texts were tagged to the categories, the next step was to search for patterns in the data. The patterns in the data include (1) more-than-once-occurring

sequences of events, (2) more-than-once-occurring combinations of conditions, processes and outcomes and (3) conflicting accounts of events or processes (Gläser & Laudel 2013). The NVivo software is able to populate the number of times a certain category has been tagged and the number of sources. Searching for patterns serves as a tool to triangulate data and test internal validity. For example, an actor cited ‘permits and licenses acquisition’ as a major cause of delay for STP construction. I used this as one of my categories and all texts or statements in the transcripts that mention this category are tagged. At least eight different respondents from the private, national government agencies and LGUs have mentioned ‘securing permits and licenses’ as a source of delay. This step also provides rival explanations as LGUs were interviewed on their processes in issuing permits and licenses.

Once patterns have been identified, the third step is to integrate them. Integrating patterns means determining if the patterns are different or can be merged into one or link those

data that do not fit into the pattern, i.e., how do those data that do not form a pattern fit into the overall story? In this research, integration was done by subsuming the actors, network features and network effects under the different decision making arenas. The issues raised by respondents are confirmed or disproved by validating with other sources of evidence. The empirical evidence is then juxtaposed against theory, assertions are

presented and conclusions are made on how network relationships impact on the outcomes within an arena and the broader sewerage services network.