Gender Data Revolution

The goal of achieving gender equality by investing in women’s empowerment has slowly been gaining momentum with world leaders voicing their commitment to the UN Sustainable Development Goals (SDG) in September of 2015. Yet, a crucial ingredient in the mix has largely been overlooked. To this day, there are not comprehensive and reliable gender data nor gender-specific assessment tools put in place, thus making the achievement of these ambitious goals in 15 years unrealistic. According to Buvinic, gender data gaps currently exist across five global domains - health, education, economic opportunities, political participation and human security - making it extremely difficult for international organizations such as the UN to track progress on gender issues. What is more, only three of the 14 proposed SDG indicators for gender equality are currently widely available.[1] If we do not possess the tools to distinguish between men’s and women’s political, economic and social lives across countries, we would not be able to identify the underlying causes of gender inequality and thus, fight for systemic change.

DATA COLLECTION

There are countless of biases hidden in the way development organizations choose what, when and how to measure change that could prove to be detrimental to the project’s success. The data collection phase must not be taken lightly or done half-heartedly, in a rush to get to the implementation phase, as it is often the case. According to Buvinic and Levine, “substandard data” is even more “insidious” that no data at all as it could often “misrepresent reality in such a way as to make women appear to be more dependent and less productive than they actually are.”[2] For example, studies looking at labor force participation in developing countries may often overlook the great percentage of women engaged in informal employment. These women remain “unnoticed” by surveys that use indicators measuring the “primary economic activity” of a person, thus grossly underestimating the economic contribution of women, for whom paid work is often a secondary occupation after being a “housewife.”[3] Other examples of flawed study designs can be found in socioeconomic and agricultural surveys which usually use the head of the household, assumed to be male, as the “anchor for the household roster,” with other family members defined “in relation to the (male) head.” As a consequence, female-led households, which comprise a substantial portion of households below the poverty line in developing countries, go unreported and could miss on benefits provided by anti-poverty programmes.[4]

THE ROLE OF GENDER DATA

According to Buvinic and Levine, we have seen the most notable advances in gender equality in the areas of education and sexual and reproductive health, where better data is available, whereas areas with poor data, such as economic participation, have seen much fewer progress.[5] A UN Statistics Division survey of 126 countries has shown that 80% of those countries regularly produce sex-disaggregated statistics on education, while 65-70% produce statistics on sexual and reproductive health and fertility. However, only 30-40% regularly produce statistics on informal employment, unpaid work and violence against women.[6] As a consequence, over the last years, we have seen tremendous gains in girls’ education and school enrolment rates with the launch of the Millennium Development Goals in 2000. As Buvinic and Levine proclaim, “gender equality became synonymous with girl’s education.”[7]

While data by itself would not lead to the advancement of gender justice, it does provide the necessary evidence to indicate progress and convince policy-makers to invest in interventions that target women as the primary beneficiaries. The SDGs already represent a great improvement effort from the Millennium Development Goals whose only measure of sex as a category was school enrolment. Yet, much more could be done in the domain of data collection and analysis if we want effective anti-poverty initiatives free of gender biases.

CLOSING THE DATA GAP

Closing the gender data gaps will require a great deal of political commitment, technical advances and financial investment as well as a diverse team of data analysts, demographers and computer scientists among others to pool resources and overcome the challenges of data collection, clarity and interpretation. Understanding and thus, achieving gender inequality could not happen before we start investing in the production of comprehensive, reliable and inclusive gender data that could lead to more effective and successful social interventions. As the senior director of the UN Foundaion’s Data2X initiative declared: “There’s no gender equality without data equality.”