The revolution is coming, and disabled people are part of it.
Data is exciting. Satellite images of trees can help stop deforestation. Mobile-phone usage is a way to measure poverty. Data is important. You need to keep track of basic things like births and deaths, or the economy. Nigeria’s GDP grew by 90% overnight because of dodgy data. The “data revolution” has caught on as a phrase because people about the ways of getting new data, combining data in different ways, being able to make more comparisons and see things they hadn’t before.
You know what I’m going to say now. No, except for a general remark on marginalised groups, the panellists did not mention disability. Fortunately plenty of other people have.
A disability data revolution is needed. Disability is complex: no gold standard measure exists, different measures exist for different purposes, and the use of different measures in different countries makes international comparison of prevalence or outcomes difficult. However, more is now known about disability prevalence, recently estimated at 15% or 1 billion people worldwide, 80% of whom live in LMICs. The ground for a data revolution has been prepared.
If that’s not enough, go see the background note over at ODI on old-age, disability and mental-health in the post-2015 data framework.
Poor data on disability is part of the exclusion disabled people face.
From the little data that is currently available we know that people with disabilities are missing out. […] Without [more] data we can’t determine the impact programmes are having on this group of people. We’re also prevented from being able to identify barriers and put in place the necessary changes.
As the panellists on Development Drums say, if the no one is there when the tree falls, has it made a sound? If we don’t measure it, we can’t keep track of when disabled people are being excluded. Recent conversations I’ve had in development projects in Bangladesh have shown two sides of this phenomenon. A Programme Manager said that there was no specific target for supporting disabled people in his project and that’s why it hadn’t been a focus. A disability activist said that if programmes don’t monitor the number of disabled people supported then we can’t hold them to account. As the linked articles say, disability measurement has been pretty complicated and inconsistent in the past – but now we have better tools and we are always pushing for more people to use them.
But this kind of data isn’t always what we need.
Data itself doesn’t make change. Databases do not knock on doors, make phonecalls, or push for institutional reform. We already have plenty of data that we’re not using very well. Will the revolution change that?
Actually, data – in the sense of numbers and measurement – isn’t always the sticking point. Alison Croft asks if we need to know how many disabled children there are in order to promote access to education and, surprisingly, the answer isn’t obvious. We can do quite a bit for the education of disabled children without knowing how many there are. We can provide curricula that use various media; we can equip teachers with problem-solving, individual-centred teaching styles. An example Croft gives of a pragmatic approach is allowances for teachers in Botswana to provide extra support for children having difficulty learning.
The contrast she makes is between responsive interventions (which do not necessarily need exact numbers) and planned interventions (which do). We don’t need to know how many disabled children there are to prepare teachers to treat learners as individuals. We do if you’re providing a specific support like sign-language interpreters.
We don’t need to measure everything – there are other ways to learn.
Croft’s argument is both a warning and a relief. It’s a warning because surveys might delay or be done instead of service provision – and the results might mislead people about the phenomenon being measured. Disability suffers from this problem – plenty of people find the 15% figure impossible to relate to their personal experience; surveys with lower numbers give us an excuse for inaction; and in the middle of this back and forth it’s so easy to say we need more data and do nothing more.
The argument is a also relief because it means we can start work now – we don’t have to wait for surveys that prove things with absolute numbers and cost effectiveness. She recommends that we start with learning lessons from local interventions and based on this working out what’s needed at a national level. This isn’t data that can be aggregated, and lessons from how teachers and schools have worked with specific disabled children aren’t, I think, part of what’s seen as the “data” in the data revolution. But maybe they should be.