Quantile Regression Analysis of Predictors of Calorie Demand in India: An Implication for Sustainable Development Goals

This study examined predictors of households' calorie demand using consumer expenditure survey data during the time frame of millennium development goals. It draws suggestions for achieving sustainable development goals to eliminate calorie-poverty. We used the log of per-capita calorie intake as the calorie demand. Endogeneity corrected quantile regression was applied to examine the distributional effect of predictors. Findings revealed calorie-monthly per-capita consumption expenditure (MPCE) elasticities were positively statistically significant across quantiles in rural-and urban-areas, but, contrary to traditional wisdom, elasticities are lower for calorie-poor than calorie-rich households. Dietary diversification of food items, relative food price, and share of medical-and education-expenditure were the main adverse drivers of calorie demand. Our results are robust to the under-reporting and measurement error. The policy implications are: (a) only focusing on pro-poor income enhancing strategies will not able to reduce calorie deprivation, it should be backed by imparting awareness about food choice and nutritional value of low price food items, (b) to implement necessary policy to maintain stable food inflation and effectively targeted food subsidy for calorie poor, (c) to adopt forward-looking medical-and education-policy such as free health and education facilities to all by enhancing public spending to revive the quality of public hospitals and educational institutions.