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Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India—An application of small area estimation techniques

机译:印度比哈尔邦贫困发生率的局部估计和空间绘图—小面积估计技术的应用

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摘要

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.
机译:贫穷影响着许多人,但后果和影响却影响着社会的各个方面。因此,有关贫困发生率的信息是人口进行政策分析和决策的重要参数。为了在解决贫困问题时提供针对性的具体解决方案,需要进行小范围统计。通常设计和计划进行调查,以便对主要在较高地理区域(例如国家和州一级)的感兴趣的人口特征进行可靠的估计。样本大小通常不足以为分类分析提供可靠的估计。在许多情况下,需要对未提供数据调查的人口区域进行估算。然后,对于样本量小的区域,仅基于特定区域的可用数据进行人口特征的直接调查估计往往是不可靠的。本文通过将NSSO 2011-12年度家庭消费者支出调查和人口普查数据相结合,描述了小面积估算(SAE)方法的应用,以提高印度比哈尔邦地区贫困发生率估算的准确性。 2011年。结果表明,通过SAE方法生成的区级估计更加准确和具有代表性。相反,仅基于调查数据的直接调查估计就不稳定。

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