The Female Empowerment Index (FEMI): spatial and temporal variation in women's empowerment in Nigeria

Erica M Rettig; Stephen E Fick; Robert J Hijmans

doi:10.1016/j.heliyon.2020.e03829

The Female Empowerment Index (FEMI): spatial and temporal variation in women's empowerment in Nigeria

Heliyon. 2020 May 13;6(5):e03829. doi: 10.1016/j.heliyon.2020.e03829. eCollection 2020 May.

Authors

Erica M Rettig¹, Stephen E Fick², Robert J Hijmans¹

Affiliations

¹ Department of Environmental Science and Policy, University of California, Davis, CA 95616, USA.
² Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USA.

Abstract

Improving female empowerment is an important human rights and development goal that needs better monitoring. A number of indices have been developed to track female empowerment at the national level, but these are incomplete and may obscure important sub-national variation. We developed the Female Empowerment Index (FEMI) to track multiple domains of women's empowerment at the sub-national level. The index is based on six categories of empowerment: violence against women, employment, education, reproductive healthcare, decision making, and access to contraceptives. The FEMI has a range of zero to one (low to high empowerment), and it is calculated as the mean proportion of positive outcomes in the six categories. To provide a proof of concept, we computed the FEMI for Nigeria and its 36 states from five Demographic and Health Surveys between the years of 1990 and 2013, using questions asked to 98,542 women between 15 and 49 years old. At the national level, the FEMI increased from 0.34 to 0.48. However, there was substantial sub-national variation, with state-level values ranging from 0.16-0.60 in 1990 to 0.19-0.73 in 2013. Our findings thus illustrate the importance of considering sub-national variation in female empowerment. The FEMI can be readily computed for other countries, and its ability to track spatial and temporal variation in woman's empowerment across a broad set of categories may make it more useful than existing approaches.

Keywords: Africa; Big data; Data analytics; Data mining; Data visualization; Demographic and health surveys; Gender inequality; Geography; Human geography; Human rights; Information science; Machine learning; Nigeria; Social inequality; Sociology; Women's empowerment.