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Gender Disaggregated Data - Missing Link for Building an 'Equiverse'

Updated: Jan 12

Gender Insights Series #1knowledge, information and data collated from TalentNomics India’s Annual Leadership Conferences

 

TalentNomics India’s Annual Leadership Conferences havebeen making a significant contribution in building awareness around the need for gender data as well as showcasing various examples of the collection and analysis of such data. Enhancing the availability of data is one of the prerequisites for achieving a gender balance in the World– be it in the form of numerical measurement of gender-disaggregated statistics that reveal the size of gender gaps or in the form of sharing inspirational stories and best practices of solutions that work.



What is Gender Disaggregated Data?

Gender data or gender-disaggregated data, refers to information and statistics collected and analyzed with a specific focus on gender. It involves breaking down data into categories based on gender, typically distinguishing between men and women, but it can also include non-binary, transgender, and other gender identities, depending on the context and objectives of the data collection.


Gender-disaggregated data can be collected in various forms, like

-     Demographic Data: data on the population's birth, death, age, location,

-   Economic Data: relayed to employment, income, entrepreneurship, access to financial resources etc.

-    Education Data: related to education, school enrolment, literacy rates, and the types of courses or fields that individuals pursue.

-    Health Data: related to diseases and symptoms, health outcomes, access to healthcare services, maternal mortality rates, etc.

-     Social and Cultural Data: related to cultural norms, gender-based violence, representation in media and politics, and other social factors.


Such data can be collected at micro-levels, such as within an organization or specific projects; or at macro-levels encompassing regions, countries, and the entire globe. Data can be collected by recording quantitative parameters as well as conducting qualitative surveys and informal discussions.


The lack of gender-disaggregated data

However, there has been a severe lack of reliable gender-disaggregated data, which has proved to be a huge barrier posing several problems and challenges, hindering our ability to understand and address gender disparities and inequalities. When data is not disaggregated by gender, it becomes difficult to identify and acknowledge existing gender disparities. This can lead to a false perception that gender-based issues are not significant, which in turn can hinder efforts to address these inequalities.


Organizations like UN Women, the World Bank, and other international agencies have developed guidelines and tools to promote gender data collection, analysis, and dissemination. For instance, the UN Sustainable Development Goals (SDGs) include specific indicators related to gender equality and women's empowerment, emphasizing the importance of gender data. Multilateral organizations are also playing and can continue to play a pivotal role in supporting the private sector to address the disparity in their gender statistics. For example, UN Women has been working with private companies to advise them on reviewing their policies and practices from a gender lens through a tool called Women Empowerment Principles.


Collecting Gender-disaggregated data: Insights from

TalentNomics India’s Leadership Conferences

Although some small steps have been initiated, a serious effort toward the collection of gender-disaggregated data is urgent. Following are some areas where gender-disaggregated data is vital, as brought out during deliberations at various TalentNomics India’s Leadership Conferences:

 

To enable targeted Government policies and programs –  There is a need to generate sex-disaggregated data to ensure that policy changes are gender-responsive, data-driven, and evidence-based. Better gender data will give a more complete statistical picture of the relative situation and status of women and men of diverse backgrounds, intersecting with issues like age, literacy, employment status, etc. Such data would provide solid evidence to understand women's requirements, how to vocalize them, and how to meet them. And this data must be used for ex-ante and ex-post gender impact assessments at the policy level. This can facilitate policymakers to think through the gender implications of a particular issue, differentiate manifestations of a problem from root causes, and identify evidence of what has worked.


“In Bangladesh, an analysis of data by the World Bank noted that an increased demand for women in the labor market, in particular for women with a basic level of education in the garment industry, was a key factor in stimulating girls’ education. The shifts in the labor market and economic opportunities, in turn, changed parental attitudes, societal norms and cultural practices towards women’s education.  The government of Bangladesh responded accordingly with an increased supply of education services because data showed an increased demand from Bangladeshi families to educate girls. Therefore, this showed how gender assessments based on data helped to uncover existing or predicted gender differentials, both around issues that policies need to respond to as well as around how any given policy proposal may impact differently on women and men from specific population groups” 

 

To track funding for women’s entrepreneurship– Gender disaggregated data on capital disbursements is one of the biggest challenges and financial organizations must try to work out how to collect that data, what are the gaps in data, what are the challenges of data collection and how they, as investors, can try to address that.


 “The Bangladesh Bank makes it mandatory for all the commercial banks to give at least 10% of their loans to women. But every year the percentage of disbursement is not more than 2-3%. So, the first thing that the government should be doing is to create clearly visible data on how many women actually need loans because access to finance is extremely tough in Bangladesh. All Banks want collateral but aspiring female entrepreneurs are unable to provide that because there is no one to vouch for them.”

 

To measure the success of DEI policies in corporate organizations-  While workplaces have been implementing several measures to enable women to be employed and promoted in their organizations, the lack of proper data collection prevents tracking and assessing the impact of these policies and measures. It is vital, therefore, that organizations collect measurable data disaggregated by gender.


Axis Securities, as part of their efforts to enable employee wellbeing, performed DEI data analytics around how many women were leaving, getting hired and promoted to senior roles etc. Just by measuring and having an agenda behind it helped them open doors for more women’s participation” 


To address women’s specific health challenges – gender-disaggregated data is needed for better decisions and outcomes for women’s health and engagement in economic activities. Inadequate gender-disaggregated data can hinder the identification of health disparities between men and women, the allocation of resources for maternal health, and the assessment of violence against women and girls.


To make Artificial Intelligence (AI) and technology to be gender-neutral - Big data and AI can be leveraged to build enabling technology for women. But this requires addressing the biases in data collection that is used as inputs in the algorithms behind the technological innovations. We need to create a better gender balance in the datasets that are used for building technology, especially AI. For example, AI tools for personal recruitment or talent acquisition often use historical data, which is biased because there are a lot of male data sets and not female data sets.

 

If you have any stories or best practices to share, where gender-disaggregated data has enabled solving gender-related challenges or helped create pathbreaking solutions, then do share with us in the comments box.



-By Shravani Prakash, Founder, Ellenomics (Outreach Partner for TalentNomics India)

                                                                                                         

 


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