Beyond Pink and Blue: Analyzing Gender Data Using Frameworks
Let’s journey back to the Stone Age, where a group of hunters and gatherers set out to find food for their community. They’re just looking for sustenance, but who is the alpha?
As most often guess, the men were the hunters and the women were the gatherers. Does this suggest that early men held a more dominant position? In this case, we’re not just observing hunting and gathering; we’re uncovering the earliest traces of gender-based division of labor and power dynamics.
Fast forward to today, and gender data analysis operates in the same way. The previous part of this blog series focused on the fundamentals and collection of Gender Data. In this part of the series, I will introduce various Gender Data Frameworks that will help you analyze your data with a focus on gender.
I have limited experience on the subject and this is a summarization of my learnings from the course Gender Data 101 held by data.org.
What is Gender Data Analysis?
Gender data analysis goes beyond “sex-counting”. It exposes the many differences between genders, in regards to gender roles, power dynamics, and opportunities, to name just a few examples.
Jump the hurdles
To avoid any misinterpretation, it is essential that you understand your data before you begin the analysis.
- Metadata: Metadata is information about your data. It includes general information about the dataset, like the methodology, outliers if any, description, etc.
- Semantics: Understanding the semantics of words or phrases in context, such as ‘Average,’ ‘Median,’ or ‘Mode,’ is better understood when knowing the total population or sample size.
When sharing your data, it is best to specify the metadata and semantics. The following template is a useful tool to help you do so: ‘We All Count’ Data Biography template.
Gender Analysis Frameworks (GAF)
Gender Analysis Frameworks provide different structures to guide you in the analysis of data, while keeping the focus on gender. I will introduce you to three of these frameworks, namely Jhpiego’s Gender Analysis Framework, the Harvard Analytical Framework, and the Gender at Work Framework.
Jhpiego’s Gender Analysis Framework for Health Systems
Jhpiego categorizes lives into four domains, all of which intersect with power. Power informs who has, who can acquire, and who can expend the authority to acquire assets.
Example: Consider a community trying to improve their maternal healthcare. Using Jhpiego’s GAF for Health Systems, we can categorize our learnings about this community like so:
Looking at the framework, a number of things could be changed to improve maternal healthcare in this community, such as:
- Implementing financial inclusion programs to empower women economically and enable them to afford healthcare services;
- Conducting awareness campaigns to challenge traditional beliefs and engage men in supporting maternal healthcare.
Harvard Analytical Framework for identifying gender roles
The Harvard Analytical Framework aims to identify the type and amount of work done by individuals in a household or community, by focusing on three areas of inquiry: Activity, Access & Control, and Influencing Factors.
The example below illustrates how we can use the Harvard Analytical Framework to summarize our analysis of women’s participation in a rural farming cooperative:
By utilizing the Harvard Analytical Framework, we can gain a stronger understanding of the complexities surrounding women’s participation in a rural farming cooperative and can use this to develop targeted strategies to address gender-related challenges.
Gender At Work Framework for uncovering opportunities for change
The Gender at Work Framework highlights the interrelationship between gender equality and the ‘rules of the game’ held by power dynamics within communities.
As can be seen in the illustration above, the top two quadrants relate to the individual, the right quadrants relate to changes in an individual’s conditions, and the top left quadrant is the individual’s consciousness and capability. The bottom two quadrants are related to the system.
As an example, let’s use the Gender at Work Framework to analyze a community of weavers:
- Consciousness and Capabilities: More women find training programs inaccessible that restrict their opportunities for skill development.
- Informal Norms & Exclusionary Practices: Men are typically decision-makers while women are often excluded from leadership roles, limiting their influence over production decisions and income distribution.
- Resources: Access to weaving materials is a challenge for many. Women, in particular, face resource constraints as they have limited access to credit and loans to invest in their weaving businesses.
- Formal Rules & Policies: While there are policies in place to promote gender equality in the weaving industry, their implementation remains inconsistent.
Based on this information, the situation of weavers in this community could be improved through:
- Individual development: Implementing training programs that are accessible to both male and female weavers.
- System development: Introducing gender quotas for leadership positions in weaving associations and cooperatives to ensure women’s representation in decision-making bodies.
If this framework interests you, read the sub-part of this blog which is a short case study on the financial lives of women in rural Paraguay.
What’s next?
Knowing how to use a framework based on the data you’re working with is important, but it is not everything. A key aspect of gender data analysis is understanding what you’re trying to unravel from your data. That being said, if your focus is mostly on how to engage your audience, then stay tuned for the final part of this series during which I will introduce you to the best practices for visualizing data.
I am very grateful to the course organizers who provided such insightful and valuable content which has helped me write this blog!
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Fields Data is a humanitarian data-preparedness organisation leveraging local expertise to mitigate the effects of disasters.
