Also published by Norrag.
The gaps in education data have become a recurring theme in this blog. Indeed, most observers would agree that if data on education were a human body, it would be a sick patient at the moment. We see the gaps in the data each day, and the struggles of statisticians as they try valiantly to plug those gaps. And this is the reality: we lack the basic data of sufficient quality to track global – or in many cases, national – progress towards the educational goals.
As a recent post reported, countries around the world are only able to gather about half the data needed to monitor progress towards Sustainable Development Goal 4 on education. The first edition of the Sustainable Development Data Digest, produced by the UNESCO Institute for Statistics (UIS) cited the UIS assessment of data availability, which found that most countries have education data that can be disaggregated by sex, age and location. Few, however, have any data disaggregated by wealth or disability status.
We think we can all agree on the urgent need to come up with better data and better coverage to track and monitor education goals – a need that has been intensified by the SDG 4 agenda. This agenda does not demand indicators that are necessarily more ‘difficult’ in some fundamental way. For every indicator or measurement issue, there is a model instrument or measurement tool that has been tried in the past and that could be adapted or scaled up. But the new agenda means that the task ahead is much bigger, given the demand for more nuanced data to track progress on the SDGs.
We cannot duplicate efforts
There is, however, some disagreement on possible solutions. One idea that is being hotly debated is the creation of a new data body of some kind. But before we try to set up a brand new institution (just 14 years from the 2030 deadline for the SDGs), we need to be clear about the ultimate goal here. It is not, we maintain, to plug every hole in the data, or to come up with new indicators that would be “good to know” but are not truly necessary for the SDGs. Statistics are important tools that must be as accurate as possible. But they are just tools to achieve the new global education agenda.
We must take care that we do not duplicate initiatives to address these issues or increase the transactional costs (the costs of producing data) for countries that are already struggling to keep up with the demand for statistics. While there is clearly a pressing need for global methodologies and metrics, their absence is only a symptom of the over-arching data problem. It is not the root cause. The cause in many, many countries is the lack of well-developed statistical systems to track and monitor their own educational progress. Some countries lack the political leadership and commitment to sustain such efforts; some lack the funding for statistical activities, resulting in limited technical skills and human capital and data that are too sparse. Some countries lack both leadership and funding.
Tackle the root causes of poor data
In our view, the top priority is to tackle the root causes of poor data. Three responses need to be in place: funding; strengthening the institutional setting; and defining the core statistics that need to be delivered. On this last response, we need to clarify the basic data that are urgent and relatively easily available, those that are urgent but that need more innovative data gathering approaches, and those that are simply less urgent.
The immediate task is to significantly improve the quality of the basic data – i.e. the 11 global monitoring indicators that cover the main issues such as school enrolment and completion – without ignoring the larger set of thematic indicators.
The task is already underway. Three complementary initiatives, the Global Alliance to Monitor Learning (GAML); the Assessment for Learning (A4L) initiative; and theCommission on Financing of Global Education (chaired by Gordon Brown, the UN Special Envoy for Global Education) are calling for work to establish the standards and methodologies needed to produce good data and the raise the financial resources that are needed for the task ahead. With enough resources, the UIS and its partners can also train national statisticians to use these tools to produce and use basic data while creating demand for data within their countries.
The GAML brings together national education authorities, assessment agencies, citizen-led initiatives, the education community and donors. Its first job is to develop an internationally-comparable measure of reading and mathematics to monitor learning outcomes. A4L will be a vital source of support. This international platform supports national learning assessment systems that improve assessment at national and global levels and will channel assistance to developing countries through sustainable capacity-building at the system level. Together, these three initiatives are articulating the different functions and interactions that are needed across the global education community to help countries produce data that have real impact.
Help countries strengthen their own statistical systems
The UIS has the greatest depth of experience in working with countries to produce internationally comparable data and is well-equipped to support the global approach that is already in motion. As shown by its work on learning outcomes, it works with countries to build global consensus while addressing their specific needs and contexts.
There are, indeed, many questions about how to help countries strengthen their own statistical systems; how to coordinate the work of the many institutions that gather and analyse data; how to provide better guidelines and adapt existing guidelines to current needs; and how to initiate a discussion about national plans to improve educational statistics. These are the very questions that we are trying to answer right now, informed by our close work with countries, as we work in partnership to pursue the education goals for 2030.
The key initiatives mentioned above are calling for some things that are similar, and some that are different. For these initiatives to create maximum synergy and avoid confusion amongst partner development agencies and governments, it may be wise to analyse the specific content of the initiatives and to compare and contrast the various ideas being put forth. In a forthcoming blog, we will present a brief analysis, in matrix form, of where there are synergies, and where there might be duplications (or different ways of doing similar things). Enhanced dialogue will be needed to bring together the best of the initiatives and thus generate a clear agenda for action.