数据算数:估计尼日利亚2009年至2019年的贫困趋势

Jonathan Lain, Marta Schoch, Tara Vishwanath
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摘要

监测减贫需要经常提供家庭福利的微观数据,以便进行长期比较。由于统计能力有限、对数据收集的冲击以及调查方法的定期改进,许多国家无法获得此类数据。本文展示了如何联合部署回溯法和调查对调查的推算法可以帮助克服这一问题,在这种情况下,鉴于减贫挑战的规模,迫切需要估计贫困趋势,但调查对调查的推算法更有可能成功,并且可以直接进行测试。在尼日利亚,可用于构建归算模型的最新官方调查是通过与目标调查相同的方法在同一年收集的。这种数据格局可能出现在其他环境中,在这些环境中,小型的间隙性调查的方法比大型的官方消费调查更新得更快。天真地比较尼日利亚最近两次官方消费调查就会发现,2009年至2019年间,贫困率下降了17个百分点。然而,本文提出的两种方法都表明,贫困率的下降幅度要小得多,在3到7个百分点之间,这与尼日利亚同期在非货币福利指标上的表现相呼应。因此,该文件提供了指导,说明回溯和调查对调查的归因技术何时以及如何对监测减贫最有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Making Data Count: Estimating a Poverty Trend for Nigeria between 2009 and 2019
Abstract Monitoring poverty reduction requires frequent microdata on household welfare that can be compared over time. Such data are unavailable in many countries, given limited statistical capacity, shocks that prevent data collection, and regular improvements to survey methodology. This paper demonstrates how jointly deploying backcasting and survey-to-survey imputations can help to overcome this in a setting where estimating a poverty trend is badly needed, given the scale of the poverty-reduction challenge, but where survey-to-survey imputations are more likely to succeed and can be directly tested. In Nigeria, the most recent official survey that can be used to construct an imputation model was collected through the same methodology and in the same year as the target survey. This data landscape could arise in other settings where the methodology for smaller, interstitial surveys is updated more quickly than for larger, official consumption surveys. Naively comparing Nigeria's last two official consumption surveys would suggest that the poverty rate fell by 17 percentage points between 2009 and 2019. Yet the methods presented in this paper both suggest a much smaller reduction in poverty of between 3 and 7 percentage points, echoing Nigeria's performance on nonmonetary welfare indicators over the same period. The paper therefore provides guidance on when and how backcasting and survey-to-survey imputation techniques can be most valuable for monitoring poverty reduction.
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