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.
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References
Battese, G.E., R.M. Harter, and W.A. Fuller. 1988. An error component model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association 83: 28–36.
Breslow, N.E., and D.G. Clayton. 1993. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88: 9–25.
Brown, G., Chambers, R., Heady, P., and D. Heasman. 2001. Evaluation of small area estimation methods - an application to unemployment estimates from the UK LFS. In: Proceedings of statistics canada symposium 2001. Achieving data quality in a statistical agency: a methodological perspective.
Chandra, H., N. Salvati, and 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 Applied Statistics 38 (11): 2413–2432.
Chaudhuri, S., and N. Gupta. 2009. Levels of living and poverty patterns: a district-wise analysis for India. Economic and Political Weekly XLIV (9): 94–110.
Chauhan, R.K., S.K. Mohanty, S.V. Subramanian, J.K. Parida, and B. Padhi. 2016. Regional estimates of poverty and inequality in India, 1993–2012. Social Indicators Research 127: 1249–1296.
Coondoo, D., A. Majumder, and S. Chattopadhyay. 2011. District-level poverty estimation: a proposed method. Journal of Applied Statistics 38 (10): 2327–2343.
Fay, R.E., and R.A. Herriot. 1979. Estimation of income from small places: an application of james-stein procedures to census data. Journal of the American Statistical Association 74: 269–277.
Johnson, F.A., H. Chandra, J. Brown, and S. Padmadas. 2010. Estimating district-level births attended by skilled attendants in ghana using demographic health survey and census data: an application of small area estimation technique. Journal Official Statistics 26 (2): 341–359.
Manteiga, G.W., M.J. Lombardìa, I. Molina, D. Morales, and L. Santamarìa. 2007. Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model. Computation Statistics and Data Analysis 51: 2720–2733.
Mitra, S. 2016. Poverty in West Bengal: a review of recent performance and programmes. In Understanding development-an Indian perspective on legal and economic policy, ed. S. Banerjee, V. Mukherjee, and S.K. Haldar. New Delhi: Springer.
Mohanty, S.K., D. Govil, R.K. Chauhan, R. Kim, and S.V. Subramanian. 2016. Estimates of poverty and inequality in the districts of India, 2011–2012. Journal of Development Policy and Practice 1 (2): 142–202.
Pfeffermann, D. 2002. Small area estimation: new developments and directions. International Statistical Review 70: 125–143.
Rao, J.N.K. 2003. Small area estimation. New York: John Wiley and Sons.
Rao, J.N.K., and I. Molina. 2015. Small area estimation, 2nd ed. New York: John Wiley and Sons.
Saei, A., and R. Chambers. 2003. Small area estimation under linear and generalized linear mixed models with time and area effects. Working Paper M03/15. Southampton Statistical Sciences Research Institute, University of Southampton, U.K.
Särndal, C.-E., B. Swensson, and J. Wretman. 1992. Model assisted survey sampling. New York: Springer.
Ybarra, L.M.R., and S. Lohr. 2008. Small area estimation when auxiliary information is measured with error. Biometrika 95 (4): 919–931.
Acknowledgements
The author would like to acknowledge the valuable comments and suggestions of the Editor and the referee. These led to a considerable improvement in the paper. The work was carried out under the ICAR-National Fellow Project at ICAR-IASRI, New Delhi, India.
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Chandra, H. 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. J. Quant. Econ. 19, 375–391 (2021). https://doi.org/10.1007/s40953-020-00226-8
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DOI: https://doi.org/10.1007/s40953-020-00226-8