A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women's Empowerment in Nutrition Index (WENI). (June 2022)

• Record Type: Journal Article
• Authors: Saha, Shree; Narayanan, Sudha
• Is Part Of: World development. Volume 154(2022)
• Publication Date: 2022-06
• Subjects: Empowerment -- Nutrition -- Machine learning -- LASSO -- Gender -- India -- South Asia; Economic history -- 1990- -- Periodicals; Economic assistance -- Developing countries -- Periodicals; 330.9
• DOI: 10.1016/j.worlddev.2022.105860
• Languages: English
• ISSNs: 0305-750X

Abstract

Highlights: We develop simplified indicator of nutritional empowerment the Abridged Women's Empowerment in Nutrition Index (A-WENI). We use machine learning techniques (LASSO) to reduce the number of indicators in the index from 33 to 20. A-WENI is developed for a five-state survey in India and validated with new data from the Indian state of Maharashtra. A-WENI reproduces well the empowerment status based on WENI and like WENI is a good predictor of nutritional outcomes. A-WENI can be more easily incorporated into general purpose rural surveys to capture women's empowerment and nutrition. Abstract: Measuring empowerment is both complicated and time consuming. A number of recent efforts have focused on how to better measure this complex multidimensional concept such that it is easy to implement. In this paper, we use machine learning techniques, specifically LASSO, using survey data from five Indian states to abbreviate a recently developed measure of nutritional empowerment, the Women's Empowerment in Nutrition Index (WENI) that has 33 distinct indicators. Our preferred Abridged Women's Empowerment in Nutrition Index (A-WENI) consists of 20 indicators. We validate the A-WENI via a field survey from a new context, the western Indian state of Maharashtra. We find that the 20-indicator A-WENI is both capable of reproducing well the empowerment scores and status generated by the 33-indicator WENI and predicting nutritional outcomes such as BMI and dietary diversity. Using this index, we find that in our Maharashtra sample, on average, only 35.9% of mothers of children under the age of 5 years are nutritionally empowered, whereas 77.2% of their spouses are nutritionally empowered. We also find that only 14.6% of the elderly women are nutritionally empowered. These estimates are broadly consistent with those based on the 33-indicator WENI. The A-WENI will reduce the time burden on respondents and can be incorporated in any general purpose survey conducted in rural contexts. Many of the indicators in A-WENI are often collected routinely in contemporary household surveys. Hence, capturing nutritional empowerment does not entail significant additional burden. Developing A-WENI can thus aid in an expansion of efforts to measure nutritional empowerment; this is key to understanding better the barriers and challenges women face and help identify ways in which women can improve their nutritional well-being in meaningful ways.