Education 2030 (SDG 4)
Datafication in education governance has become established in recent decades as the prime mode of knowing and reforming complex education systems around the world. The rise of large international assessments created a wealth of statistical information and thus allowed states and transnational agencies for the first time to construct comparative knowledge about education performance. One of these global education monitoring exercises is the construction of the Education 2030 agenda, or otherwise the Sustainable Development Goal 4 (SDG4), which promises to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” (United Nations General Assembly, 2015, p. 14). SDG4 represents the single biggest attempt to bring together a vast array of actors and countries in order to construct universal education indicators, as well as to decide on the appropriate methodologies and data sources. It is a country-led, global exercise – led by UNESCO but with the collaboration and close involvement of all major International Organisations (IOs). Given its global and collaborative scope, it presents its leaders and participants with enormous technical and political problems.
Through an in-depth analysis of texts and interviews, this case study explores the conundrum of securing accountability of this global performance monitoring project through ensuring the objective validity of its measurement tools, whilst promoting the democratic and equal participation of all actors. UNESCO, as the custodian agency of SDG4 has a double accountability obligation to participating countries: first, the robust and objective monitoring of progress towards the SDG4 goals, and secondly, the participatory and democratic, equitable process in which all member countries have a voice and stake in the project. As a result, although the UNESCO Institute of Statistics has been significantly reinvigorated in relation to its statistical capacity, it has also put great emphasis on the participatory, inclusive and consensual aspects of the agenda. This double responsibility does not always happen without friction; on the contrary, it has been causing a significant amount of tension in the relationship between and within some of the major IOs, as well as between IOs and other stakeholders including developing nations.
For more details on this case, please find extended analysis and discussion in the articles ‘Prophets, Saviours and Saints: Symbolic Governance and the Rise of a Transnational Metrological Field’ (PDF) and the chapter by Fontdevila and Grek ‘The construction of SDG4: Reconciling democratic imperatives and technocratic expertise in the making of global education data?’ (PDF)
Global Poverty Measurement (SDG 1)
Ending poverty is one of the key challenges of sustainable development. The realisation of this goal is most commonly – both discursively and materially – linked to the production of high-quality poverty knowledge. The standards of quality of such knowledge are closely linked to the governing paradigms employed by International Organisations (IOs), which are prioritising specific evidentiary standards in decision-making. Consequently, international poverty knowledge is quantified, highly technical and relies strongly on objectivity as the core epistemic tenet.
At the same time, the quantification of poverty knowledge is strongly contested. The UNICEF Innocenti report describes the measurement of poverty as a ‘crisis in monitoring’ (2015). Indeed, there has been profound disagreement and controversy around the measurement systems of poverty – both in academic and policy worlds. One of the factors accelerating this crisis is the increase in the number of approaches to measurement promoted by the International Organizations. Just in the last twenty years, the number of global measures of poverty increased from one (the popular dollar-per-day International Poverty Line introduced by the World Bank) to eight different monetary and multidimensional approaches.
This case explores – through document analysis and semi-structured interviews – the dynamics of poverty governance in the situation in which multiple measurement approaches compete. In the face of the multiplicity of different measures the International Organisations employ various strategies to assess, create and communicate the epistemic, political and strategic values of poverty indicators. Consequently, the process of measurement – and the controversies around it – is a domain of navigating different legitimating forces. These issues are explored in details in the forthcoming paper: ‘The legitimacy of experts in policy: navigating technocratic and political accountability in the case of global poverty governance’ and the work-in-progress: ‘Knowledge Controversy in Calculable Spaces: Valuation Practices and the Market of Indicators in Global Poverty Measurement’.
European Education Area 2025
The concept of Europeanisation of education, although evident in the measurement practices that have been governing European education since the start of the 21st century, was for a long time a contested term. Although subsidiarity in education is still the rule, yet we see recently concerted efforts by the EU to establish a European education area by 2025. The policy initiative aims to enhance learning mobility and educational opportunities in the EU, reinforce
the cultural dimension of the European Union and bolster youth participation.
What are the data requirements to construct this single governing entity? Which are the organisations that come together to build the statistical architecture which will inform the policy work surrounding this new policy aim? What is the role of the OECD and its various international comparative assessments in assisting the Commission in this task? This METRO study examines in-depth the interdependencies of IOs in governing education in Europe, in order to make sense of the processes of the construction of metrics and their impact on IOs themselves. In particular, the case examines the Education and Training 2020 strategic framework, the preparations towards the making of a European education area 2025, and quality assurance processes developed in the field of Higher Education in Europe.
Statistical Capacity Development
The Sustainable Development Goals (SDGs) follow on the heels of a more condensed global development agenda, the 2000-2015 Millennium Development Goals (MDGs). The MDGs required unprecedented internationally-regulated quantified data to be curated and verified by International Organisations (IOs) to monitor progress on health, poverty, education, economic well-being, and other development goals. In the process of monitoring the MDGs, the lack of data in many countries or sub-national regions was highlighted as a problem that development agencies must put on their agendas. This lack of official data was particularly stark in the face of the rapidly changing technology landscape that has led to a “data revolution” in many parts of the world, which has constructed elaborate alternate data and meta-data collection systems alongside official statistical systems.
With these needs and inequalities in mind, United Nations member states and IOs put on the SDG agenda the support for statistical systems in the Global South that collect routine data about country-level and particularly vulnerable sub-national populations – that is, statistical capacity development – and incorporated it as an indicator for monitoring within SDG17. Simultaneously, statistical capacity development is also presented as necessary for the functioning of the global development agenda as a whole, maintaining and creating the infrastructure for the 231 unique indicators to be monitored by custodian UN agencies.
Based on interviews with key figures within IOs, network analysis of advocates of statistical capacity development, and critical discourse analysis of key texts, this case investigates the debates and processes of developing global consensus on principles and standards for statistics, statistical systems, and their development, including in the part of the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs), the UN Statistics Division, and others. At the heart of these debates is the tension between the “empowering” or “democratic” nature of data – to “make people count” – and the work of creating universal standards for measurement, as well as tensions between different practices of statistical estimation and representation. For more on institutional partnership and frictions around statistical estimation, please see ‘Metric partnerships: Global burden of disease estimations within the World Bank, the World Health Organisation, and the Institute for Health Metrics and Evaluation’ (PDF), and for an overview of the anthropology of metrics, please see the Cambridge Encyclopedia of Anthropology entry, ‘Metrics’ (PDF).