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Session Submission Type: Group Panel
After the adoption of the Sustainable Development Goals (SDGs) all national statistical agencies, data producers, researchers and citizen-led data initiatives are now preparing to support policy-makers, donors, and the global development community to monitor progress towards a wide range of targets from ending poverty to promoting peaceful and inclusive societies for sustainable development.
One of the new developments of this broader and more complex agenda is the existence of cross-cutting issues, among them gender equality and disparities linked to other dimensions. This implies a key difference with the previous agenda: the need to get disaggregated indicators by a number of dimensions other than sex, the only dimension consistently monitored under the MDG framework. As a consequence, most indicators will need to be disaggregated by sex and gender, age, income quintiles/deciles, disability, ethnicity and indigenous status, economic activity, location or spatial disaggregation, and migrant status. For these and other dimensions equity indicators have to be produced.
As the global community strove towards defining and framing the new development agenda, concerns around the current global statistical capacity to respond to the demand arose. Crystalizing these concerns, the United Nations called for a data revolution for sustainable development. The underlying assumption was that traditional statistical systems (e.g. governmental and international organizations’ management information systems) were caught off guard by the revolution in information technology that took place over the past decade. This has been conflated by many commentators with the Big Data trend that was happening around the same period and generated a strong yet confused momentum in support of data for development.
Today, the statistical capacity of the global education community is still far from being able to apply Big Data techniques to measure and monitor education. National and international statisticians are not data scientists yet, in spite of the increasing complexity of their work. Despite the growing importance of data and monitoring for evidence-based policy design and international reporting, countries and international stakeholders have yet to overhaul the global statistical capacity to respond to the pressing needs associated with the SDG4 monitoring framework. This is particularly true for the monitoring of equity in education where the definition and production of indicators remain to be agreed upon and the critical lack of quality data is hindering appropriate monitoring of the issue
This panel builds on UNESCO Institute for Statistics work around defining a data revolution to measure equity in education and reviews some of the statistical and methodological challenges linked to the ambition of monitoring equity in education for SDG 4. Starting from monitoring requirements at the international level it provides an overview of what could be the implications for the so-called data revolution and how the global community needs to answer to the unprecedented burden that official statisticians are faced with worldwide.
What is progress towards SDG target 4.5 and equitable quality education? - Patrick Montjourides, UNESCO Institute for Statistics; Saïd Voffal, UNESCO Institute for Statistics
International initiatives to measure education equity: neglecting national systems? - Stuart Cameron, Oxford Policy Management
Measurement and reporting of inequality in education - Friedrich Huebler, UNESCO Institute for Statistics
Private funding and equity in education - Jean Claude Ndabananiye, UNESCO