“All financial responsibilities will rest on her shoulders, and she will not be able to save any money,” responded Grace, when asked “What will happen if a woman has control over family decisions?” This sentiment was a recurring theme in our WEE-DiFine-funded study, “Adapting and Validating WEE Indicators in an Experimental Study of Savings” conducted in Uganda.
The goal of our study was to identify specific measures of economic empowerment related to women’s savings behavior using two complementary modes of validation. In the first phase, content validation, we applied qualitative methods to derive a comprehensive list of WEE indicators relevant to the everyday savings practices of Ugandan women. In the second phase, construct validation, we substantiated these indicators through their statistical correspondence with women’s observed savings behaviors, including their ability to set meaningful goals, to mobilize savings toward those goals, and to use the saved money as intended. We found several factors related to WEE measurement that were extremely local and not widely discussed in existing tools. They were, however, very prominent in our survey responses and repeatedly selected by feature selection algorithms. Our takeaway is that the local context is absolutely vital for rigorously measuring women’s economic empowerment.
Content Validation Insights: Bringing Hidden WEE Indicators to the Forefront
The content validation phase identified over a hundred WEE indicators. Here we focus on those that appear most frequently in our survey data,and that are rarely addressed in existing WEE measurement frameworks.
Balancing Power and Responsibility
Decision-making power is normally viewed as an indicator of empowerment, but it brings financial consequences that can undermine women’s ability to save. This is especially true in Uganda, where the communal nature of households continually introduces new responsibilities and roles for women. Many women, like Grace, discussed the burdens that come with decision-making power, recognizing that there is a point beyond which the associated responsibilities become too heavy. Decision-making power cannot be assessed as a WEE measure without an understanding of what it entails and who bears the financial costs.
Fear as a WEE Factor
Fear significantly impacts women’s sense of agency in deciding how much to save for the future. These fears include divorce, illness, and, most prominently, a fear of aging. Many women expressed worries regarding whether relatives or children would still be available to care for them as they grew older. This uncertainty prompted them to adopt savings strategies aimed at securing their financial stability in later life, especially considering their lack of retirement funds.
Mental Accounting and the Role of Trusted Knowledge
Women can find it challenging to organize and manage their financial resources—a behavioral aspect strongly tied to mental accounting. Navigating an overwhelming and often unreliable influx of information complicates their decision-making, hindering their ability to stay focused on their financial goals. Training on savings practices, local business issues, budgeting, and related topics are highly valued in helping them achieve realistic financial objectives. Additionally, women emphasized the need for clear and straightforward information from financial providers to facilitate comparisons of products and services, enabling them to make better financial choices.
Construct Validation: Identifying the Most Relevant WEE Indicators for Savings
Using our rich data set of over a hundred WEE variables and account-level data, we applied ML feature selection algorithms to identify the WEE indicators that resonate most with our participants throughout their savings journeys.
The results (Table 1) clearly confirm that Grace’s observation about the burden of financial responsibilities and its relevance to savings behavior was not just a personal anecdote, but a vital issue for women in Uganda. Most new indicators discussed during the content validation phase were consistently selected across all ML feature selection algorithms and ranked among the top 5 of the 25 selected WEE constructs.