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Gender Differences in Double Burden of Malnutrition in India: Quantile Regression Estimates

Author

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  • Archana Agnihotri

    ((Corresponding author), Research, Chennai, India)

  • Brinda Viswanathan

    (Professor, Madras School of Economics, Chennai, India)

Abstract

India has witnessed growing prevalence of double burden of malnutrition among both men and women. Based on BMI quantile regression estimates using NFHS-4 data, a comparative assessment on the role of dietary patterns, lifestyle, education, health and hygiene, household’s demographic composition and region of residence on double burden of malnutrition, is provided separately for men and women in India. NFHS-4 data differs in sample size and nature of questions for men and women. In order to provide robustness checks gendered comparisons are also discussed by contrasting the results from full sample with the sub-samples for couples, and women only from the households of male sample. Within each BMI quintile BMI increases with education except for women in top quintile where the magnitude reduces for 10 or more years and even more for 12 or more years of education, after controlling for other factors. This perhaps is a reflection of an expectation for women to be lean than it may be for men as they are more likely to be younger and exposed to media particularly social media. Vegan diets worsen BMI for the lowest quintile while the same diet is beneficial to those at the top quintile. For men sedentary occupations are associated with overweight and for women household drudgery is associated with increased underweight. There is a broad geographic segregation of malnutrition with low BMI more prevalent in Central and Eastern India and high BMI in Southern and Northern India while double burden is more prevalent among men in Western India. Overall, the conditional quantile estimates are discerning of the covariates associated with double burden of malnutrition in India compared to the conditional mean (OLS) estimates.

Suggested Citation

  • Archana Agnihotri & Brinda Viswanathan, 2021. "Gender Differences in Double Burden of Malnutrition in India: Quantile Regression Estimates," Working Papers 2021-208, Madras School of Economics,Chennai,India.
  • Handle: RePEc:mad:wpaper:2021-208
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    References listed on IDEAS

    as
    1. Perianayagam, Arokiasamy & Goli, Srinivas, 2013. "Health insurance and health care in India: a supply-demand perspective," MPRA Paper 51103, University Library of Munich, Germany, revised 31 Oct 2013.
    2. Dang, Archana & Maitra, Pushkar & Menon, Nidhiya, 2019. "Labor market engagement and the body mass index of working adults: Evidence from India," Economics & Human Biology, Elsevier, vol. 33(C), pages 58-77.
    3. Md Zakaria Siddiqui & Ronald Donato & Jaya Jumrani, 2019. "Looking Past the Indian Calorie Debate: What is Happening to Nutrition Transition in India," Journal of Development Studies, Taylor & Francis Journals, vol. 55(11), pages 2440-2459, November.
    4. Siddiqui, Zakaria & Donato, Ronald, 2020. "The dramatic rise in the prevalence of overweight and obesity in India: Obesity transition and the looming health care crisis," World Development, Elsevier, vol. 134(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    BMI; Quantile Regression; Gendered Difference; Diet; Lifestyle; Sample Size;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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