A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI)
| dc.creator | Saha, Shree | |
| dc.creator | Narayanan, Sudha | |
| dc.date | 2022-06 | |
| dc.date | 2024-04-12T13:37:50Z | |
| dc.date | 2024-04-12T13:37:50Z | |
| dc.date.accessioned | 2026-06-27T15:46:21Z | |
| dc.description | 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. | |
| dc.identifier | https://hdl.handle.net/10568/141398 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/114172 | |
| dc.language | en | |
| dc.publisher | Elsevier | |
| dc.relation | https://doi.org/10.1080/00220388.2021.1961746 | |
| dc.relation | https://doi.org/10.1007/s12571-021-01155-x | |
| dc.rights | Open Access | |
| dc.source | Saha, Shree; and Narayanan, Sudha. 2022. A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI). World Development 154(June 2022): 105860. https://doi.org/10.1016/j.worlddev.2022.105860 | |
| dc.subject | models | |
| dc.subject | women's empowerment | |
| dc.subject | gender | |
| dc.subject | machine learning | |
| dc.subject | empowerment | |
| dc.subject | nutrition | |
| dc.subject | rural areas | |
| dc.subject | women | |
| dc.title | A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI) | |
| dc.type | Journal Article |