Searching for #Genderdata about the Women of the Mekong
As I write the River Runs with Her series, I try to find data that helps explain some of the issues women in the region are facing. For example, how many women are experiencing violence? How much access do women have to contraception? How many of their daughters are finishing secondary school? What percentage of women are members of government? These aren’t just random questions – this is the type of fundamental information that helps governments and donors focus on issues that matter most in communities, create new policies and direct funds for programming that can help improve lives.
For the Mekong countries, I thought I could make a tidy chart summarising each country with some key indicators and the latest data on poverty reduction, health, education and gender equality. I’ve worked in the past as a journalist, and have spent many years working in and around UN agencies. I understand how difficult it can be to collect good data.
So, I was happy to see the UN Secretary General’s first Report on the Sustainable Development Goals (SDGs), issued this past summer. The SDG indicators underscore the importance of collecting and disaggregating data by sex. I dove into the report, looking for gender data on the Mekong countries. The report, perhaps understandably given the SDGs are just one year old, was fairly short and very globally focussed.
I looked a little closer, and was glad to find the UN SDG Indicators website. There’s some good material here including clarity about the state of the 230 indicators meant to measure the progress on the 17 development goals, downloadable excel sheets, and opportunities to parse data by region, country or indicator. Everything is there. But, it’s high on transparency (excellent!), and low on data visualisation. Finding and analysing data would take hours of sifting through hefty excel spreadsheets.
One thing in the spreadsheets was very clear – while disaggregated health and education data were relatively complete (based solely on the fact there were data values in most of the cells, and, much of this data has been collected for years); there was very little data for a critical SDG – number 5, the SDG for gender equality. Goal 5 pushes for big gender issues including an end to violence against women, child marriage, female genital mutilation; recognizes the value of measuring unpaid labour, the need for sexual and reproductive health rights, women’s participation in institutions including government and ownership of resources such as land. For this goal there is some data, but many huge gaps.
Next, I checked the World Bank’s gender data portal, which does a very nice job on data visualisation. Here, you can see “featured indicators” (some of which are SDG indicators though they’re not marked as such), and parse by country or indicator. There are clear charts and tables that quickly tell you how a country compares to others in the region. There’s even a dedicated space where you can find what data is missing. Type in “unpaid hours of domestic work, female” and you’ll find that only three countries had data (or, had submitted data) in 2012, the last date for which numbers are available for this indicator on the World Bank site.
At the UN “Global Goals Week” in New York this week, #genderdata was definitely on the agenda. The UN Foundation with support from the Bill and Melinda Gates Foundation and others have launched (or perhaps, relaunched) “Data2x” – which was originally introduced by Hillary Clinton in 2012. So far, the group has done impressive research to identify and analyse gender data gaps, and is building partnerships to “power the gender data revolution.”
In tandem, or, maybe it’s all part of the same movement – it’s a bit hard to tell on first pass, UN Women launched “Making Every Mother and Girl Count.” It’s not a forced numeracy campaign, but rather a 5-year programme to “generate, prioritize and use gender data,” with an initial focus on 12 countries. UN Women has announced support from Australia and USAID.
The agency’s Executive Director, Phumzile Mlambo-Ngcuka underscored the depth of need. “Right now, we do not have the data to monitor 80 per cent of the indicators for SDG 5 on gender equality.” She went on to say that “only 13 per cent of countries dedicate a regular budget for gender statistics. Out of US$131 million committed to statistical capacity-building by the OECD-DAC, only 2 per cent is devoted to projects that specifically address gender equality.”
Indeed, a lot of work remains, but there is impressive commitment. The Bill and Melinda Gates Foundation has offered US$80 million to help close the gender data gap, and Melinda Gates frequently speaks to the issues.
There’s also some very good recent work to highlight.
For example, UNFPA recently announced an initiative to collect better data about violence against women in the Asia and the Pacific Region. The agency realized that women won’t necessarily tell researchers if they’ve suffered violence – sometimes because their husbands, who are often the perpetrators, are home when researchers visit, and the women are afraid to speak. The kNOwVAWdata initiative (also supported by Australia) will train scores of researchers in the region to conduct interviews with women in a way that is discrete and respectful, so that women feel safe and empowered to talk about their experiences.
The Lancet has also recently released a new series on maternal health. This series goes deeper than a similar series from a decade ago, and finds that the gaps in care and mortality between rich and poorer women continues to grow – both between, and within countries. The series points to the over-medicalization of pregnancy and child birth in middle-income and wealthy countries, leading to “too much, too soon”, resulting in too much spending on interventions that aren’t backed by evidence and may harm women; and “too little, too late” for poor women, who are giving birth in facilities or at home with poor quality medical supplies and care.
Something to consider: Both the UNFPA and the Lancet initiatives are excellent and support a focus on improving policies, programming and monitoring for women. They involve a lot of money and time (all well spent). But they address just a handful of the SDG goals and indicators. This points to the level of commitment required to collect and use high quality gender data for each for each of the SDG goals.
I counted 37 indicators that specifically ask for data disaggregated by “sex” and there are well over 40 that require specific information about women and girls. This requires an enormous financial commitment, and there is no time to waste. As we saw with the MDGs, 15 years can go by very fast.
So for now, I’ll spend some time in the weeks ahead finding and compiling available development data about women in the Mekong countries. In the meantime, these other data issues are also on my mind:
SDG data for all countries: For the Millennium Development Goals, the focus was on collecting data from poorer countries. The SDG data teams will now collect data from all countries – that means wealthy countries will also have to account for development, or lack of it, for their poorest populations.
In Canada, for example, indigenous peoples are too often in a desperate situation. Many communities have no water or proper sanitation. Indigenous people are disproportionately the victims of violence. Unemployment is high. Donor countries like Canada will have to work to collect and provide data to the UN that’s going to be embarrassing and frankly, shameful (and Canada recognizes this). The U.S. was in the spotlight last week when new data showed that its maternal mortality rates are increasing and are worse than Iran, Vietnam, Russia and Romania.
Who’s in Charge? If I was a journalist trying to find SDG gender data, I wouldn’t know where exactly to look. There are many groups involved (which is good) but no clarity about who is responsible for what, or, which data I can rely on as being the most up-to-date and accurate. As I mentioned, Data2x seems to be relaunching; UN Women has a big initiative – they may be linked, but that wasn’t explicitly clear.
The World Bank’s gender data portal looks great - but is it out of date? And is it reporting on SDG indicators? Another partnership called the Global Partnership for Sustainable Development Data also held an event at the UN in NY this week – though it seems to comprise other partners and doesn’t include donors like the BMGF or big agencies like UNICEF or WHO (unless it’s a question of updating their website).
As a very small step, I’d suggest the SDGs indicator table should include a column about who is responsible for collecting and reporting the data. I’m sure someone is working on that. Eventually, SDGs gender data should be a 1-stop shop for anyone looking, with opportunities for clear data visualisations.
How will communities collect and use their own data? On one of the many websites I read last week, there was a message about the importance of communities ultimately being involved in researching and using their own data to set priorities. A couple of thoughts: Based on discussions I’ve had with many women living on the Mekong, most don’t know about the SDGs and about their government’s responsibility to improve their lives. They certainly wouldn’t know how to access or analyse data.
Alarmingly in this region, there is a shrinking space for civil society – and fewer advocates and activists who can help women and communities know their rights. On the upside, visit any village leader in Lao PDR, and they will tell you exactly how many households are in their community. A school principal will tell you exactly how many pupils are enrolled. So, there are structures in many countries in this region that can gather and use data to improve people’s lives. There is a lot of advocacy work required to create systems that engage communities in their own information gathering and priority-setting.
How low can we go? There’s a lot of talk about “levels” in the UN - like “global, regional, country and community” levels. And we know that it’s one thing to collect country-level data. But if we want to dig deeper and find not only the state, or province, but the village, ethnic community or hidden population that needs services, we need data that goes to the lowest possible level. Or, to flip that idea, data that reveals the needs of the pointy top end of the demographic triangle – the needs of those who are most neglected.
One story to illustrate this – I remember driving from village to village on a measles campaign in western Nepal a few years ago. The campaign was well organized – there were clear plans on how to reach children in every school. As we drove along twisty mountain roads, we passed scores of children including young girls working on the side of the road, breaking stones with their parents. I asked how those children would be reached with measles vaccine. The answer wasn’t terribly convincing. I doubt those children were included in the microplan, though they were probably some of the most at-risk of serious complications or death if they became infected with measles.
I personally would love data about the women who live in the towns and villages along the Mekong river. I know that’s really ambitious. There are governments to convince. Money to raise. Incredible technical hurdles to overcome. Capacity to build.
I applaud the people who are now tackling the issue of gathering #genderdata for the SDGs. I’m not a statistician and admire the work done so far, including the analysis of what we have, and what we still need. There are great minds on this, and a great deal of commitment from professionals who have committed their lives to gender equality.
There is a long way to go, but the journey has begun. It’s time for all to stand in support of serious investments in #genderdata for the SDGs.