Every child counts

By Oliver Fiala & Lilei Chow

Last year, two-year-old Umelma (pictured above) received her measles/rubella vaccination as part of a Save the Children vaccination campaign in Wajir county, Kenya. Unfortunately, what should be a routine process is often not in the Northeast of Kenya as GRID, our Child Inequality Tracker, shows. Only 4 out of 10 children were vaccinated with basic vaccines in Wajir, significantly less than the national average of more than 7 in 10.

Data on inequalities like these is extremely important for increasing accountability to children like Umelma, ensuring that they are granted the same chances in life as children living in more privileged areas. However, as our analysis in this blog shows, the availability of such data is still extremely patchy, despite multiple promises from the international community for improvement.   

Measuring progress towards sustainable development

In 2015, world leaders from 193 countries came together to set out the 2030 Agenda for Sustainable Development – a blueprint to create a more prosperous, sustainable and inclusive world. This landmark agreement, of which the 17 Sustainable Development Goals (SDGs) form the backbone, is supported by a data framework to track and report on progress and gaps. Perhaps the most important principle underpinning the SDGs is the pledge to Leave No One Behind, and to reach the furthest behind first. This principle is a commitment by world leaders to reduce inequality and tackle discrimination. It is a recognition that a child living in Wajir, for example, counts as much as a child living in Nairobi, London or New York.  

To breathe life into this principle, data needs to reflect the different conditions which different groups of children are facing. For those of us who are tracking progress on the SDGs, it means understanding who and crucially, why, certain groups are falling behind or being excluded from broader development gains. This is essential for policy makers to make better decisions and to address specific vulnerabilities facing the most deprived and marginalised children. And it allows civil society organisations and citizens to hold governments and other actors to account for their commitments and policy decisions.

What does this mean in practice? In effect, monitoring inequalities requires disaggregated data, which means indicators on health, education, child protection and other areas need to go beyond national averages and reveal group-based inequalities (for instance by gender, socio-economic status, geographic location, ethnicity or other characteristics). Global and national level statistics can be helpful in showing aggregate progress, but they mask persistent, underlying inequalities and much slower rates of improvement for the hardest to reach or most marginalised populations. In different contexts and for different thematic areas, the disaggregation dimensions may look slightly different, but in almost all circumstances a national estimate alone won’t tell us what we need to know.

The 2030 Agenda recognises explicitly that measuring progress across all groups in populations requires timely and accurate disaggregated data. The global indicator framework for the SDGs was agreed by the Inter-Agency and Expert Group on SDG indicators (IAEG-SDGs) and will be complemented by regional and national frameworks to measure progress more meaningfully at all levels. Over the past five years, UN agencies, other multilateral organisations and national statistical offices (NSOs) have significantly increased their investments and efforts in monitoring and reporting on SDG implementation using disaggregated data. Most recently, the 51st UN Statistical Commission in March 2020 recognised data disaggregation as one of the key priorities for the IAEG’s work this year, which we very much welcome.

Mind the gap: Disaggregated data in the SDG database

However, despite some progress in setting up the SDG indicator framework, disaggregated data is still the exception rather the norm. To assess the extent of this problem, we looked at the current version of the SDG global database and checked for the 51 child-relevant indicators what disaggregation dimensions were included. A background paper of the IAEG-SDGs from January 2019 helped us to distinguish between “minimum required disaggregation dimensions” (e.g. those which the indicator requires by definition, such as education outcomes for girls and boys requiring sex disaggregation as a minimum), and additional dimensions identified as relevant (“additional disaggregation”, such as also disaggregating education outcomes by income group, ethnicity or migrant status).

Five years into the implementation of the SDGs, the results of this analysis are disappointing. Of the 38 child-relevant indicators which require at least some minimum disaggregation dimensions, only 12 indicators have all the required dimensions included, with data covering most children. Another 17 indicators have some of the dimensions included and/or the data covers only a small proportion of all children. 9 indicators do not have any disaggregated data.

This picture gets even gloomier if we look at the 39 child-relevant indicators, where additional disaggregation dimensions have been identified as relevant. For 3 of these at least some disaggregation exists. For 36 indicators, none of the additional dimensions of disaggregation are currently included in the SDG database. Just to mention one example: indicator 3.7.2 measures adolescent birth rate (ages 15-19), a crucial indicator for girls’ wellbeing, as complications during pregnancy and birth are the leading cause of death for this age group. Education of girls, the number of living children, marital status, geography and socio-economic status have all been identified as relevant additional disaggregation dimensions. However, the SDG database currently lacks any disaggregated data beyond the national average.

The reasons for this lack of disaggregated data are complex and manifold. Some barriers may be technical, with various databanks not properly harmonised with each other. The UN Statistical Division, UN agencies and other multilateral and donor agencies, as well as many National Statistical Offices, have made significant investments over the last few years to improve data standards and make it easier to harmonise different data systems. But expanding disaggregated data will largely depend on more frequent and comparable household surveys as well as better disaggregation of administrative data. Innovative solutions such as phone surveys, geospatial data and citizen-generated data can also play an important role, although significant gaps remain in coverage, perceived legitimacy and methodologies.

COVID-19 poses another risk to the availability of disaggregated data. Detailed insights about various groups of children and other population groups are often reliant on household surveys. To curb the spread of the virus, many door-to-door surveys have been paused, which will likely reduce the availability of disaggregated data in the near future.

However, perhaps the biggest barrier to better disaggregated data on the SDGs is lack of political will. Producing disaggregated data is expensive and can be time-consuming. Governments in low and middle-income countries often cite a lack of capacity in national statistical systems for reporting on the SDGs. But it’s also important to note that population groups that are most likely to be left behind are often the most disenfranchised within political systems.

Data for the Decade of Action

As the Decade of Action and Delivery begins, and with just 10 years to go to meet the SDGs, we need a global SDG database that raises the ambition on data disaggregation. Quality and timely disaggregated data is the key to more effective SDG implementation and evidence-based policy making.

This is being highlighted at this week’s IAEG meeting, and we hope this will lead to much more urgent action than we have seen to date. NSOs, UN custodian agencies, civil society and donors need to redouble efforts to collaborate to move this agenda forward quickly - to identify areas that need to be prioritised and to close the data financing gap. Otherwise, we are at risk of reaching the end date of the 2030 Goals without the ability to know which targets have actually been met, and which children have been left behind.

It is only when children like Umelma and her peers are counted and visible to policy makers through data that we will meet the SDGs and our promise to Leave No One Behind.