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Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique

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  • Hukum Chandra
  • Nicola Salvati
  • U. C. Sud

Abstract

The National Sample Survey Organisation (NSSO) surveys are the main source of official statistics in India, and generate a range of invaluable data at the macro level (e.g. state and national levels). However, the NSSO data cannot be used directly to produce reliable estimates at the micro level (e.g. district or further disaggregate level) due to small sample sizes. There is a rapidly growing demand of such micro-level statistics in India, as the country is moving from centralized to more decentralized planning system. In this article, we employ small-area estimation (SAE) techniques to derive model-based estimates of the proportion of indebted households at district or at other small-area levels in the state of Uttar Pradesh in India by linking data from the Debt--Investment Survey 2002--2003 of NSSO and the Population Census 2001 and the Agriculture Census 2003. Our results show that the model-based estimates are precise and representative. For many small areas, it is even not possible to produce estimates using sample data alone. The model-based estimates generated using SAE are still reliable for such areas. The estimates are expected to provide invaluable information to policy analysts and decision-makers.

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  • 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.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2413-2432
    DOI: 10.1080/02664763.2011.559202
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    References listed on IDEAS

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    1. Manteiga, Wenceslao Gonzalez & Vieu, Philippe, 2007. "Statistics for Functional Data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4788-4792, June.
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    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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ranjbar, Setareh & Salvati, Nicola & Pacini, Barbara, 2023. "Estimating heterogeneous causal effects in observational studies using small area predictors," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    8. 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.
    9. Boubeta, Miguel & Lombardía, María José & Morales, Domingo, 2017. "Poisson mixed models for studying the poverty in small areas," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 32-47.
    10. 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.
    11. Chandra, H, 2018. "Localized estimates of the incidence of indebtedness among rural households in Uttar Pradesh: an application of small area estimation technique," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 31(1).
    12. Chandra, Hukum & Salvati, Nicola & Chambers, Ray, 2018. "Small area estimation under a spatially non-linear model," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 19-38.

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