The survey, included in a new report by Equal Measures 2030, was released on the sidelines of the General Assembly. The report presents the results of a survey of policy makers in five countries – Colombia, India, Indonesia, Kenya, and Senegal – and seeks to provide a baseline picture of what policymakers know – or think they know – about gender equality in their own countries. “Understanding the perspectives of policymakers on gender equality – as well as the extent to which these perspectives are grounded in data and evidence – is a crucial part of understanding where change needs to happen in order to keep us on track to reach the SDGs for girls and women by 2030,” says the report.
The data shows how knowledgeable policy makers are about the challenges facing women and girls in their own country.
Alison Holder, the executive director of Equal Measures 2030, explained in a panel discussion at the the release of the days that the survey wasn’t meant to “trip up policy makers”, but to understand where policy makers are at in their understanding of key issues. Figure 1 below shows the extent to which policy makers believe that gender equality is given enough attention in policy. When the responses are broken down by gender, as shown in Figure 2, important nuances – which bear an impact on policy making – are revealed.
Despite a large portion of policymakers reporting progress on gender equality, there were significant differences between the men and women surveyed: while nearly eight in ten men believe that men and women in their country are more equal now than five years ago, only 55% of women agreed this was the case. More than twice the number of women than men felt the situation had not changed or had worsened (44% of women compared with 19% of men).
“They Know They Don’t Know”
At the heart of the effort behind the Equal Measures 2030 survey is a desire to bring data and information to the fore; to ensure that the rights of women and girls are part of the vocabulary a common understanding, based on relevant, accurate and smart data is essential. Aside from being a standalone SDG, gender equality issues also cut across all 17 SDGs, and impacts all aspects of a girl or a woman’s life. At the practical, policy making level, this means that, every day, decision makers deal with issues that affect women and girls, no matter what ministry or agency they work for. One of the key themes discussed is the need to go beyond “roadmaps and good ideas”, as Katja Iversen, CEO of Women Deliver noted.
In order to successfully reach the SDGs, there needs to be a solid foundation, building blocks that allow for the elaboration of well-informed, evidence-based, data driven policy. Figure 3 below reveals that policy makers are aware of the shortcomings of existing data to inform their understanding, while Figure 4 confirms the fact that policy makers do not have adequate information. As AB Albrectsen, the CEO of Plan International, said during the event: “they know they don’t know.”
Perception vs. Reality
To further illustrate the disconnect between policy makers’ understanding of key issues affecting women and the reality of how these issues play out in their country, the survey also gauges the accuracy of policy makers’ estimates of the statistics around these key issues. Figure 5 below demonstrates that there is a wide gulf between perception and reality, and underscores the vital importance of improving data systems – at a structural level – to bridge that gap. As Albrectsen noted, “if policy makers actually think that 70% of the labor force is constituted of women when its only 27%, no surprise the issue is not prioritized.”
Eliminating Barriers to Better Data
Claire Melamed, executive director of global partnership for sustainable development data, who delivered the keynote, expressed her frustration around the use and availability of data for informed policy making. The use of data to define and communicate problems is well understood by the global policy making community, as well as by advocates and the media. We also have an increasingly better of appreciation of how smart data can improve policy responses.
For instance, Melamed mentioned how, during the Ebola epidemic in Sierra Leone, data revealed that women were much more likely to die from the disease. This led to a more adequate response, which took into account social and cultural factors and allowed for a more focused response. Yet, these examples are one-offs, and a data-driven approach is not yet systematized. “We are at a point where we understand this [the lack of widespread use of data for policymaking] is an issue, and that its a critical part of the SDGs. I’m impatient to do something about it.”
For data to take its full place as at the heart of infrastructure for policy making, a series of barriers have to be eliminated. Politically, funding needs to be made available to properly fund and resource national statistical offices. Only about 2% of Official Development Aid (ODA) is spent on data collection – and most if this is not spent on building up data systems, but more on one-off surveys, understanding the impact of programs, policies, etc. All of which are essential, but do not necessarily support or integrate with national systems. It is also important to find new ways to do things, through greater collaboration. Data creators and aggregators should be around the table with data interpreters and processors, as well as policymakers and other stakeholders. In practice, this means being better organized to ensure data collected at the grassroots and in communities is intelligently and strategically transposed to inform policy solutions.
“Data Is Not The Point”
When the data conversation started, people felt it was a technical question – “that’s the easy bit”, that can be solved, according to Melamed. “The really hard bit”, she continued, is getting the politics right – creating the demand for the data and putting in place the mechanisms to allow data to be leveraged to improve people’s lives. The best way to do this is to not talk about data, she explained, because “data is not the point.” The point is the improvement of people’s lives, if you can show that data can help policy makers in helping reach policy goals.
What are the barriers to actually bring those things to scale and make them a much more systematic part of the way we do things? According to Melamed, we still have a choice about the kind of data economy and society we want to build. We don’t yet have well-established systems and patterns of resource use that are being hardwired, so there is an opportunity to use data as an instrument of change. “Are we going to build a world where the benefits of the data economy are equally shared, where data is serving as an equalizing force in society, or are we content to live in a world where data is driving down the same tracks, where data is serving to confirm patterns of inequality we already know about?”