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

机译:印度北方邦的债务分类分解估算:小面积估算技术的应用

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摘要

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.
机译:国家抽样调查组织(NSSO)的调查是印度官方统计的主要来源,并且在宏观层面(例如州和国家层面)生成了一系列宝贵的数据。但是,由于样本量较小,NSSO数据无法直接用于微观层面(例如地区或进一步细分的层面)的可靠估算。随着印度从集中计划系统向更加分散的计划系统转变,印度对此类微观统计的需求迅速增长。在本文中,我们通过链接来自Debt-债务的数据,采用小面积估算(SAE)技术来得出基于模型的印度北方邦州或其他小面积水平上负债家庭比例的估算值。 NSSO 2002-2003年的投资调查以及2001年的人口普查和2003年的农业普查。我们的结果表明,基于模型的估计值是准确且具有代表性的。对于许多小区域,甚至不可能仅使用样本数据来产生估计值。对于这些领域,使用SAE生成的基于模型的估计仍然可靠。预计这些估计将为政策分析师和决策者提供宝贵的信息。

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