Quantile Regression to model undernutrition among under ve children in Uttar Pradesh

Abstract

According to the World Bank, prevalence of undernourished children in India
is one of the highest in the world. At least half of the deaths worldwide
are attributed to malnutrition. According to the National Family Health Survey,
2015-16 (NFHS 4), Uttar Pradesh has an under-five mortality rate of 78 per
1000 live births with around fifty percent of the children under five years of age
being stunted (low weight-for-height), forty percent children underweight (low
weight-for-age) and eighteen percent being wasted (low weight-for-height). This
paper attempts to model factors affecting undernutrition among children of Uttar
Pradesh with an aim to find out if the factors exert a differential effect on the
conditional distributional of the outcome variable. The unit level information on
5181 children aged 0-59 months taken from the National Family Health Survey
(NFHS-3) 2005-06 has been used in the analysis. Z-scores have been computed
on the basis of appropriate anthropometric indicators (weight & height) relative
to the WHO International reference population for the particular age.Additive
Quantile Regression Model was used to examine factors and their effect on nutritional
status of children. The results were also compared with the logistic
regression analysis and the findings indicated that not all covariates emerged
as significant in the logistic model, however most of them were significant in the
quantile regression model. Also it could be seen that maximum number of covariates
were found significant for severe undernutrition, indicating that differential
effect of predictors on the conditional distribution of the outcome variables.