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Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling

Author

Listed:
  • Saurav Guha

    (ICAR-Indian Agricultural Statistics Research Institute)

  • Hukum Chandra

    (ICAR-Indian Agricultural Statistics Research Institute)

Abstract

Although India has progressed significantly on several health outcomes but the state of food and nutrition security in the country still requires sustained efforts to accelerate achievement. Existing data based on socio-economic surveys conducted by National Sample Survey Office (NSSO) produce precise measures of food and nutrition security status at state and national level. However, these surveys cannot be used directly to produce reliable district or further smaller domain level estimates because of small sample sizes which lead to high level of sampling variability. Decentralized administrative planning system in India demands the availability of disaggregate (e.g. district) level statistics for target oriented effective policy planning and monitoring, as food and nutrition security is often unevenly distributed among the subsets of relatively small areas. But, due to lack of district level estimates, the mapping and analyse related to food and nutrition security measures are restricted to state and national level. As a result, disaggregate level dissimilarity and variability existing in food and nutrition security are often masked. This article delineates multivariate small area estimation (SAE) technique to obtain reliable and representative estimates of food consumption and nutrition status at district level for the rural areas of state of Uttar Pradesh in India by combining latest round of available Household Consumer Expenditure Survey 2011–2012 data of NSSO and the Indian Population Census 2011. The empirical evidence indicate that the estimates generated by SAE approach are reliable and representative. Spatial maps showing district level inequality in distribution of food and nutrition security in Uttar Pradesh is also produced. The disaggregate level estimates and spatial maps of food and nutrition security are directly relevant to sustainable development goal indicator 2.1.2—severity of food insecurity. The estimates and maps of food insecurity indictors are anticipated to offer irreplaceable information to administrative decision-makers and policy experts for identifying the regions requiring more attention. Government of India has recently launched number of schemes for the benefit of rural population in the country and these estimates will be useful for fund allocation as well as in the monitoring of these schemes.

Suggested Citation

  • Saurav Guha & Hukum Chandra, 2021. "Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 623-646, April.
    • Handle: RePEc:spr:soinre:v:154:y:2021:i:2:d:10.1007_s11205-020-02573-8
    DOI: 10.1007/s11205-020-02573-8
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    References listed on IDEAS

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    1. Priyanka Anjoy & Hukum Chandra & Pradip Basak, 2019. "Estimation of Disaggregate-Level Poverty Incidence in Odisha Under Area-Level Hierarchical Bayes Small Area Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 251-273, July.
    2. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    3. Hukum Chandra & Nicola Salvati & U. C. Sud, 2011. "Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2413-2432, January.
    4. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    5. Hukum Chandra, 2013. "Exploring spatial dependence in area-level random effect model for disaggregate-level crop yield estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(4), pages 823-842.
    6. Gauri Datta & Tatsuya Kubokawa & Isabel Molina & J. Rao, 2011. "Estimation of mean squared error of model-based small area estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 367-388, August.
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    Cited by:

    1. Angelo Moretti, 2023. "Regional Public Opinions on LGBTI People Equal Opportunities in Employment: Evidence from the Eurobarometer Programme using Small Area Estimation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(2), pages 413-438, April.
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    3. Saurav Guha & Hukum Chandra, 2022. "Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 643-663, July.

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