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 5years 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.
Highlights
The United Nations has identified the achievement of gender equality and empowerment of all women and girls as one of the Sustainable Development Goals
We find that the 20-indicator A-Women’s Empowerment in Nutrition Index (WENI) is both capable of reproducing well the empowerment status generated by the 33-indicator WENI and predicting nutritional outcomes such as BMI and dietary diversity
Our findings suggest that the WENI can be abridged to 20 indicators, while remaining faithful to the concept of nutritional empowerment and still being able to reproduce well the nutritional empowerment status based on the 33-indicator WENI
Summary
Drawing on data used to create the 33-indicator WENI, we apply LASSO (least absolute shrinkage and selection operator), a popular machine learning technique to identify the candidate constituent indicators of A-WENI We validate it afresh out of sample, implementing a survey in rural Maharashtra, India, for this purpose. We detail the process by which machine learning techniques can contribute to developing sophisticated yet simple measures of empowerment, based on more complex preexisting measures and present our findings from the validation of A-WENI in rural Maharashtra. This process entails “acquiring knowledge about and say over nutritional and health practices; gaining access to and control over intake of adequate and nutritious food; and being able to draw support from both family and other institutions to secure and maintain an adequate diet and health”(Narayanan et al, 2019, p. 2)
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