Advancing Inclusion with Measurement

What is not measured does not get done. This panel explored the importance of data to advancing women’s financial inclusion and the role GBA plays in closing the gender data gap.

Key Points

• There are two main issues regarding gender data gaps: the availability of data and the quality of data. Women’s work is usually not valued by society and therefore has not been measured. In addition, survey instruments and questionnaires have been designed with the wrong preconceptions about women, and therefore the results are biased or incorrect.
• Measuring the demand side of the financial data equation is well covered with tools such as Findex. In every region women have less account penetration than men. However, there is a gap in terms of supply-side data.
• Aggregate supply-side data can be important as it can help measure the market opportunity, help understand customer behavior and build the business case.
• The capability of banks to break down data by sex is mixed, but improving. Only 55% of banks interviewed in a recent GBA study have existing capabilities to disaggregate data by sex, but 35% are using pragmatic solutions.
• Capturing women’s data can seem challenging, but it does not have to be costly nor take a lot of time. Often, existing systems can be adjusted to ensure sex disaggregation.
• Establishing a baseline is key because it allows a bank to measure performance and evolution, set targets, and establish KPIs.
• GBA found in a recent survey that a majority of members that have the data are using performance measures such as number of customers, portfolio, products per customer and NPLs, but only 50% are tracking revenues and profitability – which are key to building the business case.
• Some panelists believed that it makes sense to promote an international set of standards for woman-owned businesses and SMEs. However:
  • Having standardized definitions can be complex for institutions to apply, particularly as contexts and markets vary widely globally.
  • Some definitions can have unintended consequences. For instance, in the US, utilizing a definition focused on ownership forced many women-owned businesses to grow by taking on debt and not equity, so as not to impact their “women-owned” certification.
  • Adding layers of complexity to a definition can be costly, making many banks unlikely to adopt them.