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Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India

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  • Saurav Guha

    (ICAR-Indian Agricultural Statistics Research Institute)

  • Hukum Chandra

    (ICAR-Indian Agricultural Statistics Research Institute)

Abstract

In recent times, India has achieved significant advancement on several health indicators while the state of food security in the country still needs sustained efforts to accelerate attainment. Existing data based on socio-economic surveys conducted by National Sample Survey Office (NSSO) produce precise measures of food security status at state and national level. However, these NSSO 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. As food security is often unevenly distributed among the subsets of relatively small areas, the availability of disaggregate (e.g. district) level statistics for target oriented effective policy planning and monitoring is the need of the hour for decentralized administrative planning system in India. But, due to lack of district level estimates, the mapping and analysis 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 model-based estimates of food insecurity indicators at district level for the rural areas of state of Uttar Pradesh in India by combining latest round of available Household Consumer Expenditure Survey 2011–12 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 insecurity in Uttar Pradesh is also produced. The disaggregate level estimates and spatial maps of food insecurity 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 disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(3), pages 597-615, June.
    • Handle: RePEc:spr:ssefpa:v:13:y:2021:i:3:d:10.1007_s12571-021-01143-1
    DOI: 10.1007/s12571-021-01143-1
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    References listed on IDEAS

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    2. 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.
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    5. 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.
    6. 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.
    7. Md Jamal Hossain & Sumonkanti Das & Hukum Chandra & Mohammad Amirul Islam, 2020. "Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-16, April.
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    Cited by:

    1. Guha Saurav & Chandra Hukum, 2022. "Measuring and Mapping Micro Level Earning Inequality towards Addressing the Sustainable Development Goals – A Multivariate Small Area Modelling Approach," Journal of Official Statistics, Sciendo, vol. 38(3), pages 823-845, September.
    2. Ashkra & Akram Ahmad Khan, 2024. "Assessment of food security status in Aligarh, Uttar Pradesh, India: using fao’s food insecurity experience scale," SN Business & Economics, Springer, vol. 4(11), pages 1-17, November.
    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.
    4. Abhishek Singh & Ashish Kumar Upadhyay & Kaushalendra Kumar & Ashish Singh & Fiifi Amoako Johnson & Sabu S. Padmadas, 2022. "Spatial heterogeneity in son preference across India’s 640 districts: An application of small-area estimation," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(26), pages 793-842.
    5. Ravi Bhavnani & Nina Schlager & Karsten Donnay & Mirko Reul & Laura Schenker & Maxime Stauffer & Tirtha Patel, 2023. "Household behavior and vulnerability to acute malnutrition in Kenya," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    6. Serge Savary & Stephen Waddington & Sonia Akter & Conny J. M. Almekinders & Jody Harris & Lise Korsten & Reimund P. Rötter & Goedele den Broeck, 2022. "Revisiting food security in 2021: an overview of the past year," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(1), pages 1-7, February.

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