This chapter introduces the reader to the problem of policy prioritisation and why quantitative/computational analytic frameworks are much needed. We explain the various academic- and policy-oriented motivations for developing the Policy Priority Inference research programme. We apply this computational framework in the study of the SDGs and the feasibility of the 2030 Agenda of sustainable development.

This chapter formulates an analytical toolkit that incorporates an intricate – yet realistic – chain of causal mechanisms to explain the expenditure–development relationship. First, we explain several reasons why we take a complexity perspective for modelling the expenditure–development link and why we choose agent-based modelling as a suitable tool for assessing policy impacts in sustainable development. Second, we introduce the concept of social mechanisms and explain how we apply them to measure the impact of budgetary allocations when systemic effects are relevant. Third, we compare different concepts of causality and explain the advantages of an account that simulates counterfactual scenarios where policy interventions are absent.

This chapter provides a comprehensive framework to understand and quantify structural bottlenecks in a setting of multidimensional sustainable development. First, we formalise the idea of an idiosyncratic bottleneck when thinking in a hypothetical situation where a government has all the necessary resources to guarantee the success of its existing programmes (i.e., the budgetary frontier). Second, we compare the development gaps between the baseline and counterfactual outputs to assess how sensitive are the different indicators when they operate at the budgetary frontier. Third, we combine this information with the historical performance of indicators to develop a methodology that identifies idiosyncratic bottlenecks. Finally, we elaborate on a flagging system to differentiate between idiosyncratic bottlenecks according to the ‘urgency’ to unblock them.

This chapter studies the feasibility of the SDGs to improve our understanding of the empirical link between government expenditure and development outcomes. First, we explain the strategy to produce prospective (counterfactual or otherwise) analyses with the computational model and two metrics to evaluate advances in development gaps. Second, we present simulation results showing the development gaps by 2030 when the historical budget, in real terms, is preserved during the remaining years of the current decade. Third, we conduct sensitivity analyses that involve changes in the overall budget size that modify the value observed at the historical period used for calibration. Fourth, we present some reflections on the results.

This chapter analyses the connections between public funding, the rule of law, and multidimensional development. First, via simulation, we document a negative relationship between the budget size and the proportion of embezzled resources (or wasted resources due to inefficiencies). Second, our result suggests that reallocating public funds from other issues to programmes associated with the rule of law can mitigate corruption up to a certain point. Third, we find that the worse the country’s performance, the easier to remain in a development trap, as it becomes more cumbersome to realise a successful allocation profile (i.e., to decipher the proper mix of rule-of-law funding and overall budget size).

This chapter provides the reader with three reflections about the Policy Priority Inference research programme and its potential to make a difference in the real world. First, we synthesise the results found throughout the book and their implications for sustainable development. Second, we elaborate on systematic guidelines for deriving policies from the various analyses presented throughout the book. Third, we discuss the technical capabilities needed to adopt this toolkit and advocate for the training of computational social scientists.

This chapter presents a cutting-edge study of multidimensional poverty since it fully exploits highly granular data on expenditure (government programmes) matched with social development indicators. First, we explore how economic well-being and various socioeconomic rights, in Mexico, have benefited from domestic income and remittances of households located in the deciles 1 to 5 of the income distribution. Second, we analyse the degree of substitutability of remittances (or personal income in general) vis-à-vis spending on social programmes.

This chapter identifies accelerators and bottlenecks by estimating indirect budgetary effects at a systemic level (i.e., with the help of a network of interdependencies). First, we provide algorithms for the detection of bottlenecks and accelerators. We identify an accelerator by performing counterfactual expenditure increments on a particular policy issue while leaving the remaining ones with their original budgets. Then, a policy can be conceived as a systemic bottleneck when the removal of funding indirectly hinders the performance of other policy issues. Second, with Mexican data on 76 SDG targets, we identify 20 systemic bottlenecks and 33 accelerators. Third, we find that there does not exist a significant correlation between clogging/acceleration potential and naïve conjectures to promote development systemically (budget sizes and network centrality).

This chapter investigates how federal transfers can boost subnational development. We analyse the case of Mexico and its 32 federal states. For this, we assemble a balanced dataset with 103 social, economic, and environmental indicators for each state. First, we study how federal transfers impacted state-level development during the sample period. Second, we analyse how changes in the distribution of transfers across states affect the indicators’ average evolution when attempting to foster all SDGs or each of them. We find that ‘fiscal contributions’ – a particular form of government transfers aimed at equalising regional disparities – exert an average impact on SDGs of around 25%–45%. Likewise, our simulations indicate that it is possible to achieve substantial impact gains when using an ‘optimal fiscal transfer’ to allocate the total federal transfers across SCGs.

This chapter introduces the reader to the public datasets that we employ in most of the applications developed in the book. This information is our main input to provide a worldwide view of the state of sustainable development and how it responds to government expenditure. In light of this global database on development indicators, we also describe the most popular analytic tools and their limitations. Finally, we reflect on the main empirical challenges that researchers face when studying sustainable development with these data and motivate the methodological proposal of the book.

Introduction

This chapter presents Chantal Mouffe’s theory of plural agonistics with a focus on its relevance to information literacy research. Plural agonistics is positioned on the radical strand of democratic theories (see also Chapter 1 by Buschman). But, contrary to other radical theories, it does support the representative liberal form of democratic rule (Mouffe, 2013, xiii). The theory builds on the collaborative work of Ernesto Laclau and Mouffe (2014), in which they set out to inquire into why left politics was unable to take account of social movements not based on class. They suggested a radicalisation of democracy as a response to the essentialist view of class they identified as dominating the left: ‘What we stressed was the need for a left politics to articulate the struggles about different forms of subordination without attributing any a priori centrality to any of them’ (Mouffe, 2018, 3).

It has been pointed out that both information literacy practice and research suffer from a lack of theoretical awareness when connecting the concept to democracy (see also Chapter 1 by Buschman). James Elmborg has stated (2006, 196) that ‘[m]uch of the conflict inherent in information literacy as a critical project can be traced to contested definitions of “democracy”’. Plural agonistics is here suggested as a democracy theory that can help us to elaborate the possible connection between information literacy and democracy. However, neither information literacy nor libraries are specifically mentioned by Mouffe. Before moving on to why and how this theory is proposed for understanding information literacy, it can be helpful to present the basic tenets of the theory.

Outline of the chapter

Next, antagonism and hegemony will be introduced, two important concepts that Laclau and Mouffe developed and from which Mouffe’s theory of plural agonistics was built. The democratic paradox will then be presented, followed by the role institutions have when addressing the democratic paradox. A second part follows with a focus on plural agonistics and information literacy. Passionate decisions and democratic institutions constitute the first topic, followed by a discussion of an agonistic view on consensus and compromises, how politics and ethics should be understood and the impossibility of neutrality when advocating democracy. A closer look at an agonistic view of identity and a description of how chains of equivalences should be formed follows before suggesting what an agonistic take on information literacy research would entail.

Introduction

The variation theory of learning is a theoretical framework that can guide information literacy research. The value of variation theory to information literacy research is that it can shed light on information literacy specifically in relationship to learning through the identification of patterns of variation that may enable learners to learn as intended. Developed from an educational research agenda (Marton and Booth, 1997; Marton, Hounsell and Entwistle, 1997; Marton and Tsui, 2004), variation theory is well suited to the study of information literacy in formal learning contexts. Grounded in the belief that reality is created through interaction between individuals and the world (Marton and Booth, 1997, 12–13) and that knowledge is awareness of phenomena created through such interactions (Marton, 1994), learning is defined as changes in awareness enabled by encountering variations or differences (Marton, 2014; Marton and Tsui, 2004). The theory focuses on specific parts of the learning process, including intentions for learning, how it is enacted in a classroom or other learning situations and the learners’ lived experiences of learning. Variation theory allows for exploring the relationship between information literacy and learning in various ways, such as focusing on it as the sole outcome of a learning situation, or as a part of learning in a disciplinary learning context. Recognising that learning occurs in a myriad of contexts, variation theory may be adaptable to the study of information literacy outside educational settings, including playing a role in addressing information-focused challenges, such as misinformation, equitable access to information and so forth, facing the world today.

Variation theory

Origins

Variation theory guides research and practice that examines learning environments to reveal what students have learned, but also what was possible for them to learn within a learning situation. The development of variation theory was directly informed by the research findings from studies applying the phenomenographic approach developed in the 1970s by Ference Marton and colleagues at the University of Gothenburg in Sweden. While there are different approaches to phenomenography, they all focus on identifying variations in human experience of the same phenomenon (Marton, 1981). This type of phenomenographic research was primarily developed to explore and describe learners’ experiences in educational settings (Marton, Hounsell and Entwistle, 1997) The typical outcome from this kind of phenomenographic research, called an outcome space, is a set of categories that describe the varied ways of experiencing the phenomenon being studied.