Women’s Nutritional Empowerment and Their Well-Being Identifying Key Drivers in India and Bangladesh

Abstract

This paper uses six nationally representative household surveys from India and Bangladesh to examine the link between women's empowerment and their own nutritional status. Using a recently developed concept of nutritional empowerment, we first assess the degree to which these surveys capture its constituent elements. After identifying the relevant variables in these surveys that best represent the various aspects of nutritional empowerment, we use these surveys to estimate the relative contribution of different factors of nutritional empowerment to women's nutritional outcomes, specifically BMI (in India and Bangladesh) and anemia (in India). While there are a number of approaches to decomposing the contribution of various factors driving nutrition, we present a novel application of the Shapley-Owen decomposition method, hitherto not applied in the context of determinants of nutritional status. This decomposition method reflects not just the independent, standalone contribution of a specific factor, but a factor's contribution including possible interaction with other factors of nutritional empowerment. Consistent across the surveys, we find that resources, particularly those of health and food drive BMI, while resources relating to health and fertility overwhelmingly determine haemoglobin levels (anemia) in India. We also find that the contribution of knowledge and agency correlate positively with resources, suggesting that these dimensions are complementary. Our findings suggest that policies aimed at empowering women must therefore not focus merely on providing knowledge or seek to strengthen women's decision-making roles in the family. Rather, they should prioritize providing health resources to women in constrained settings.

Empowerment and nutrition in Niger: insights from the Women’s Empowerment in Nutrition grid

“…The difference in R-squared between models with and without the covariate of interest is averaged over the exhaustive set of regressions. This average is the contribution of the covariate of interest to R-squared (see Narayanan et al 2019, for a detailed discussion on the Shapley-Owen approach). In other words, the difference in R-squared from the complete set of models that include and exclude the covariate of interest is averaged to estimate the contribution of the covariate of interest to total R-squared.…”

“…The Shapley-Owen approach, by estimating a series of models and taking their average, is able to account for this (Narayanan et al 2019) The base models we run are a series of linear regressions of nutritional achievement, Y, on the various subsets of WEN grid covariates, grouped by domain-dimensions, and controls. Equation 1shows the complete model, including all covariates.…”