In May 2016 Melinda Gates announced that the Bill & Melinda Gates Foundation would commit $80 million to close gender data gaps. I work at the foundation’s office in India, and I work on data. However, several people, including those close to me, asked, “This is great. But, of all the things you could work on, why data?”
Let me try to answer that.
Without data, there’s no way of measuring progress on gender equality goals. We know Prime Minister Narendra Modi has constituted a group of ministers to examine the draft National Policy for Women, which aims to create a framework to develop policies and programmes to ensure equal rights, resources and opportunities for them.
We also know that a successful shaping and tracking of that policy will depend on good data.
The simple answer is: data shapes our ability to know what the problems are, it helps us better understand them and solve them. Let me use an example to illustrate this.
A report from the sample registration system bulletin shows increasing gender gaps in infant mortality in Bihar. The infant mortality rate is the number of infants who die in their first year, for every 1,000 live births. For Bihar, that number is 42. Put another way, 4.2% of all babies born in Bihar die by their first birthday. That number is clearly too high and many groups are working to reduce it.
But when we break down the data by gender, we see that 3.6% of boys and 5% of girls are dying. There is no biological basis for this difference, and the difference is huge.
Since this data is available, policy makers in Bihar can try to address it. Data has made something invisible, visible.
However, the fact that this data has surprised many, points to other data gaps. Every child should have its birth registered and receive a birth certificate, which in turn allows them to access entitlements and services. Birth and death registrations should have highlighted growing mortality gaps earlier but only 64% of births and 24% of deaths are registered in Bihar. Hence the surprise and dismay at the widening gap.
The absence of data, even from birth, makes the realities of women and girls invisible.
As Bihar looks at solving this problem, more and different data is needed. We need data on what is underpinning the difference in mortality. Why are fewer girls surviving? Is it different vaccination rates, levels of spending and care seeking, or access to nutrition? Again, data can help. Once the raw data from the National Family Health Survey is released, we can use that data to pinpoint the underlying factors.
Once we know those factors, there is still the need for more data – from evaluations – to understand what might bring about a change. If, for example, it is son preference that is being expressed through less spending on sick girls, how can the government of Bihar address this? What has worked elsewhere? Here also, we see a gap. There is an investment in women and girls – indeed there are hundreds of large-scale programmes in India and other countries. However, resources are not always prioritised to robustly evaluate whether these programmes work – and for whom.
This prevents us from separating good programmes from bad, and funds well spent from wasted resources. Is it cash transfers, better service delivery, education programmes, health insurance, or putting more money into the hands of women through work or asset creation that will help? Or is it creative entertainment programmes and information drives? Or some mix of the above? We lack data on what works and what doesn’t, especially at scale. That is one reason we will work with interested government partners who want to rigorously evaluate their efforts to improve gender equity.
This evidence will help policymakers in India, and elsewhere, who are trying to drive change.
But we still have blind spots that make it difficult for even well-intentioned policy makers to work towards a change. For example, the Population Council is leading a study on understanding adolescents and young adults in Bihar and Uttar Pradesh.
This data is showing the overwhelming ways married 15 to 19-year-old adolescent girls are being left behind. The data from UP, for example, shows these girls want to space their pregnancies but lack access to contraceptives (24%) and face sexual violence by their husbands (30%). Upon marriage, their lives worsen. Only 13% of married adolescent girls in UP can move about unescorted, 9.2% show signs of moderate or severe depression and 8.9% seriously contemplated committing suicide. Though these findings are alarming, surfacing these realities means we can better support adolescents.
Of course, data alone doesn’t lead to better policies. There is no point generating data if it’s not used. But in my experience, government and other stakeholders want information that helps inform their programmes and tells them how to do better.
Data not only measures progress, it should also inspire action. So on International Women’s Day this year, let’s share the data that inspires us to act and point to gender data gaps that remain.
Katherine Hay is deputy director, Measurement and Gender Equality at the Bill & Melinda Gates Foundation, India.