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District-Level Estimates of Poverty Incidence for the State of West Bengal in India: Application of Small Area Estimation Technique Combining NSSO Survey and Census Data

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  • Hukum Chandra

    (ICAR-Indian Agricultural Statistics Research Institute)

Abstract

Despite having long term efforts, poverty is an important and persistent social issue in India. Existing data based on socio-economic surveys produce state and nationally representative poverty estimates but cannot be used directly to generate reliable disaggregate or local level estimates. The state and national level estimates often mask the variations at the local level which in turn restricts the effective implementation of policies related to poverty alleviation locally within and between administrative units. This paper uses the Household Consumer Expenditure Survey data of NSSO and link with the Population Census data to produce the reliable district-level estimates of poverty incidence in the rural areas of West Bengal in India. In particular, small area estimation (SAE) method is explored to generate reliable district-level poverty estimates. The results clearly indicate that the district-level estimates generated by model-based SAE method are precise and representative. A map showing how poverty incidence varies by district across the State of West Bengal is also produced. The estimates generated from this research are useful for meeting the data requirements for policy research and strategic planning by different international organizations and by Departments and Ministries in the Government of India.

Suggested Citation

  • Hukum Chandra, 2021. "District-Level Estimates of Poverty Incidence for the State of West Bengal in India: Application of Small Area Estimation Technique Combining NSSO Survey and Census Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(2), pages 375-391, June.
    • Handle: RePEc:spr:jqecon:v:19:y:2021:i:2:d:10.1007_s40953-020-00226-8
    DOI: 10.1007/s40953-020-00226-8
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    References listed on IDEAS

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    1. Chamber of Commerce, 2016. "West Bengal: Economic Review," Working Papers id:10629, eSocialSciences.
    2. Gonzalez-Manteiga, W. & Lombardia, M.J. & Molina, I. & Morales, D. & Santamaria, L., 2007. "Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2720-2733, February.
    3. Dipankor Coondoo & Amita Majumder & Somnath Chattopadhyay, 2011. "District-level poverty estimation: a proposed method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2327-2343.
    4. 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.
    5. Rajesh K. Chauhan & Sanjay K. Mohanty & S V Subramanian & Jajati K Parida & Balakrushna Padhi, 2016. "Regional Estimates of Poverty and Inequality in India, 1993–2012," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 1249-1296, July.
    6. Siddhartha Mitra, 2016. "Poverty in West Bengal: A Review of Recent Performance and Programmes," India Studies in Business and Economics, in: Swapnendu Banerjee & Vivekananda Mukherjee & Sushil Kumar Haldar (ed.), Understanding Development, edition 1, chapter 13, pages 191-205, Springer.
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    Cited by:

    1. Anoop Jain & Sunil Rajpal & Md Juel Rana & Rockli Kim & S. V. Subramanian, 2023. "Small area variations in four measures of poverty among Indian households: Econometric analysis of National Family Health Survey 2019–2021," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.

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