Economic empowerment

Government initiatives and technological advancements have helped to make large numbers of consumers financially included, but that number includes more men than women. Women lag behind men in use of financial tools and mechanisms across every measure, including mobile phone ownership, mobile money use, and bank account ownership.

Since 2017, the FII survey series has included a set of questions focused specifically on measures of economic empowerment. A primary aim of these questions is to track the differences in economic empowerment for men and women, and how these are connected to factors like education and use of financial services.

Measuring economic empowerment

The survey includes a set of questions designed to measure an individual's level of economic empowerment. We looked at four specific questions that measure different dimensions of empowerment. Together these questions capture most of the variation that exists in the responses to the questions in this section. Each reflects an important way in which people feel economically empowered:

  • Agency: About how involved or uninvolved are you typically in deciding how to spend your household’s income?
  • Influence: If you were to speak your mind on a decision regarding how to spend your household’s income, about how much influence do you think you would have on the final decision?
  • Voice: If you happened to disagree with a decision about how your household’s income is spent, how likely would you be to voice disagreement?
  • Control: To what extent do you agree or disagree with the following statements regarding the money you personally earn or receive? You make the final decision on how your money is spent or saved.

The response to each question is mapped to a scale that ranges from 1 (low economic empowerment) to 5 (high economic empowerment).

Economic empowerment: Relationships and prevalence

Women's economic empowerment: Modeled effects

Modeling women’s economic empowerment

The previous plots illustrate the relationship between single factors, such as share of household income provided, and economic empowerment. These plots are useful tools to highlight trends in the data but should be interpreted carefully.

For example, if we look at the relationship between education and economic empowerment in Bangladesh, we see an intriguing trend -- more educated people are less economically empowered. A tempting conclusion is that more education results in less economic empowerment. The whole picture becomes clearer by noticing that higher levels of education are more common in young people. This additional piece of information suggests a less surprising conclusion -- that younger people are less economically empowered.

Mathematical models such as linear regression can weigh the impact of all factors at once, adjusting for confounding variables. In the next section, we limit the dataset to female respondents and apply linear regression to understand which factors are important to women's economic empowerment.

We consider the effect of a wide range of variables in addition to those already discussed. These include variables related to savings, internet and mobile phone proficiency, marriage status, the occurrence and strength of shocks (e.g., family illness, death, or natural disaster), and others.