Last week, we were delighted that over 300 guests and four panelists joined us for a webinar about the launch of our new report, “Mapping Gender Data Gaps: An SDG Era Update.” In an effort to respond to the many insightful questions from the audience, we compiled answers to several questions that we didn’t have time to fully discuss during the webinar.
Which gender data gaps are top priority to close and what are the next steps we need to take?
In 2014, we identified 28 gender data gaps. In our 2020 report, we identified 48. Intuitively, this may seem that we have made no progress in the last six years. However, as is often the case in data analysis, we have to look at the bigger picture.
The Sustainable Development Goals, adopted in 2015, pushed the international community’s aspirations, expectations, and demand for data, including for gender data. Not only do we now need data about women and girls, but we are challenged to think beyond women and girls as one homogenous group but as a group that holds many different experiences based on age, race, gender expression, sexual orientation, socio-economic background, health, geographical location, and more.
This means that, simultaneously, the data needs — and especially the gender data needs — have expanded considerably in the SDG era. Data gaps are knowledge gaps—a lack of information on issues that are considered important by a community. The fact that there are now many more gender data gaps identified is a reflection of this growth in how we think about women and girls as a group. Though no one wants to see more gaps, recognizing that there are gendered aspects to achieving the SDGs is a positive shift in and of itself.
Attempting to prioritize this long list of gaps is overwhelming and, indeed, not really possible. Various approaches could be taken to attempt to prioritize; for example, we could determine which gaps could easily be closed within the confines of existing large-scale data collection instruments and for which methodology has already been tested and agreed upon and focus on those gaps first.
Alternatively, we could assess which data is most in demand and use that to leverage key policy decisions that would benefit the greatest number of women globally. For example, the imbalance in unpaid work between women and men is a global phenomenon and improved data on this area would likely benefit all women to a greater or lesser degree.
Finally, we could look at groups of women and girls whose needs are greatest and prioritize the data that would make the biggest difference in their lives. For the poorest women in countries with underdeveloped infrastructure, they may be better served by data on proximity to hospitals, schools, and roads and access to water and electricity than by detailed information on the hours they spend on childcare.
Ultimately prioritization is really not possible. In our mapping exercise, we chose to catalog the most pressing data gaps as relayed to us by experts in their fields and based on current scholarship without ranking in order of importance. As a platform aiming to serve a wide and disparate community of data, gender, and domain experts, we hope that this will provide inspiration for others who are focused in their fields to act on the data gaps most pertinent to their work. Overall, within the 48 gaps we’ve identified in this report, we simply cannot prioritize one domain over another, just as we cannot prioritize one SDG over another.
What is the private sector’s role in collecting gender data and filling gender data gaps?
The private sector has always played a crucial role in data systems, providing data mandated by governments as well as collecting information on their own activities to inform internal and industry-wide decision making. But in recent years, the role of the private sector in supporting and generating data for “social good” has come into sharp focus.
The sweeping mandate of the Sustainable Development Goals with the simultaneous rise of digital data has led to increased demand for the private sector to play its part in providing nuanced and timely data to fill gaps in knowledge, including in relation to gender. At the same time, the private sector faces justified scrutiny to fill data gaps in a way that does not exploit or harm the very communities we are trying to learn more about.
One way Data2X has engaged with the private sector has been by exploring the potential of digital data — much of which exists due to private sector-driven sources — to fill gender data gaps. Through support of a wide range of projects, we have learned that:
- Digital data offers unique insights on women and girls.
- Gender-sensitive digital data could be scaled and effectively integrated with traditional data. Indeed, this blending of data types is critical to deriving the best value.
- There are roadblocks to overcome; a crucial next step is to identify and correct bias in digital datasets.
- We must put in place the specific measures required to protect the privacy of women and girls.
- We must ensure that women and girls are central to data governance mechanisms.
The specific role of each private-sector company will depend on many factors — whether they are data holders as a result of their products or services, their relative size as employers, their sector, and geographical location of operation. A recent report carried out by the Ladysmith Collective on the role that the technology community can play in supporting gender data found several distinct roles for these entities: share privacy-preserved data; process and visualize existing external data for new insights; create and use new data products and tools and; strengthen the capacity of feminist and women’s organizations.
In this era of digital data, the pace of data collection, analysis, and application has increased exponentially, but the risk of excluding others has also increased exponentially. We need to ask crucial questions about who is involved, who benefits, how these new data systems are being built, and the ways in which those systems shape decisions that affect people’s lives. In this context, the role of the private sector is to support transparent, equitable data collection and use that helps fill data gaps in a way that doesn’t hurt or take advantage of vulnerable communities.
How has progress been made in financial inclusion gender data gaps?
In the last few years, some progress has been made on women’s financial inclusion (WFI) data. Two types of gender data are critical to understanding WFI: demand-side data, which is survey-based, and supply-side data, which is client data from financial service providers. Whether demand-side or supply-side, there are still significant gender data gaps on women’s financial inclusion data at national levels, in both Global South and Global North countries.
Beyond the Global Findex, demand-side gender data has increased with surveys like FinScope and national demand-side surveys (such as Mexico’s). The International Monetary Fund’s Financial Access Survey has supply-side gender data for some countries and more countries are starting to sex-disaggregate their supply-side data (like Rwanda, for example). Global-level data on women’s entrepreneurship, particularly on finance for women-owned SMEs can be found at the IFC SME Finance Forum’s database.
Additionally, the private sector is a key gender data producer of WFI data. Financial service providers (FSPs), including digital FSPs and FinTech companies’ sex-disaggregated supply-side data from their client portfolios, can unearth insights for a market approach to increase women’s access and use of quality financial services. Gender data on the types of accounts or products women hold and use and on women’s loan repayment rates can be used to build a business case to serve women clients and entrepreneurs and to develop tailored services that meet their specific financial needs. The Financial Alliance for Women’s report on the Economics of Banking on Women demonstrates the importance of this private-sector data.
How do we balance efforts to increase the production of new gender data with efforts to increase the use of this data?
This is a perennial question – how do we balance the production of data vs use of data? We feel strongly that we need concrete investment in both production and use of gender data, as there is no point in producing data if this data is not used. To help ensure this balance occurs, data use needs to be foremost in the mind of data producers. To maximize the use of gender data, we need to focus data production on policy-relevant questions that address the critical needs of large groups of women and girls. And we need to ensure data quality, transparency, and the ease with which the data can be translated and communicated in policy terms. Lastly, we also need to mine existing data sets on policy-relevant topics that can be sex-disaggregated and used to understand the varied lived experiences of women and girls.
Have additional questions that weren’t covered here? We welcome the opportunity to communicate with Data2X partners, allies, and friends and encourage you to email email@example.com with any queries related to our work.