The recently released Economic Survey of India for the year 2017-18 devoted a chapter to the analysis of progress made in achieving gender equality. It’s a mixed bag. While on the one hand, the percentage of working women has declined from 36 per cent in 2005-06 to 24 per cent in 2015-16, on the other, the desire for a male child has resulted in an estimated 21 million “unwanted” girls. The survey highlights the importance of gender-disaggregated data in examining socio-economic growth.
Sue Desmond-Hellmann, the Chief Executive Officer of the Bill & Melinda Gates Foundation, spoke to Indianexpress.com about the need for better methodologies in digging out gender-disaggregated data and the complications that skewed data generation can lead to.
What has data, whatever is available, indicated to you in your work in India?
The first thing we realised we want to do is to generate data and knowledge on gender inequality. We are a data-driven organisation and it’s very clear that one of the reasons women have been left behind is that the data generated is not gender disaggregated. It is not uncommon that we have pulled data that is outsized for men and neglects women, particularly women who are in the more marginalized areas of society. We have made a commitment to investing so that data that comes out is not biased towards men, but does include women.
What are the key sectors where you have noticed the largest gap in terms of data generation, vis a vis gender?
There are several sectors where critical gender-disaggregated data is missing, and, importantly, repeated data isn’t available to help us assess change over time or design interventions.
One, we lack data on women’s formal financial service access and usage. Two, health expenditure surveys at the household level don’t give us the information we need about how women are taking care of their health, including mental health. Data on the productivity differences between women’s and men’s farms, and market price realization by women farmers is another gap area. The proposed national time use survey can help provide better estimates of women’s work participation. Further, it would be good to better understand how norms and attitudes are changing across adult women and youth.
What are the changes needed in methodology when it comes to ensuring gender-disaggregated data is available and dug?
There are cost-effective ways to introduce gender-disaggregated questions into large national surveys by asking questions about who owns, who uses, who transacts – within the household. Applying a gender lens also requires ensuring that the right mix of women are included in the sample. This generates estimates of different sub-groups of women, especially those who are the most marginalized. Finally, this also demands attention to the context related to resources that women do or don’t have access to, as an outcome of long-standing social and economic inequality.
What would be a specific example of a survey in India where women have been excluded from data generation? And how do we draw an arc between surveys that miss women and gender equality?
Let’s take a global example first. The #metoo movement has given us an indication of how widespread harassment is. We know that harassment can impinge on women’s ability to advance in their careers, get information about new opportunities, and work in safe and enabling environments. And yet, no survey has been able to truly capture the extent of harassment women face.
Many surveys by governments tend to be household surveys without disaggregation by a member. So, for example, the national debt and assets survey in India doesn’t give us any data on women’s debt or assets owned by women individually. What this means is that we cannot estimate women’s ownership and use of different kinds of assets, or women’s debt patterns. The absence of data prevents stakeholders from being able to develop sound policies or relevant interventions.
In the face of societal and attitudinal issues, there are somethings that data will never show. How much can data alone do and drive in this context?
Actually, good data and evidence can tell us a lot. In the best cases, they not only reveal, but help us better understand the underlying biases, cultural norms, and attitudes in societies that hold back women and girls. Now, it’s true that even where data does exist, it’s often sexist. For example, survey questions can sometimes be based on traditional views of gender roles, and as a result, miss women and girls entirely, or undercount and undervalue their economic and social contributions to their families, communities, and countries. So, it’s vital that the data that’s gathered is accurate and comprehensive. Even then, all the best data in the world doesn’t do any good if it sits on a shelf collecting dust. It is only effective if it is used to influence decision-making and accountability—and, ultimately, to give women and girls access to healthcare, greater decision-making power, and increased economic opportunity. We have to gather good data, analyse it, then put it to use.
#GenderAnd is dedicated to the coverage of Gender across intersections. You can read our reportage here.