Abstract
Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011–12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable.
Highlights
Bihar is third-most populous state in India
We use survey data from the Household Consumer Expenditure Survey 2011–12 of National Sample Survey Office (NSSO) and the Population Census 2011, and assume a binomial specification for the observed district level sample counts. Model specification for this application was discussed in previous Section, and resulted in the identification of three Principal Component Analysis (PCA)-based covariates, labelled X11,X21 and X31, there
We plot direct survey estimates on the y-axis and corresponding model-based small area estimates on x-axis and we look for divergence of the fitted least squares regression line from the y = x and test for intercept = 0 and slope = 1
Summary
Bihar is third-most populous state in India. According to the 2011 Population Census, the population of state is 103 million, which is about 8.58 percent of the total population of the country. Poverty is a very complex issue in Bihar and there is an exigent need to devise a focused strategy for poverty eradication. Qualitative and timely disaggregate level data is essential for effective planning, implementation and monitoring of various Government schemes in Bihar. Disaggregated level data is inevitable for identifying the areas more in need and for developing focused and target oriented intervention programs. The geographic distribution of poverty and wealth is used to make decisions about resource allocation and provides a foundation for the study of inequality and the determinants of economic growth
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