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.

中文翻译：

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让数据发挥作用：估算 2009 年至 2019 年尼日利亚的贫困趋势

监测减贫工作需要频繁的家庭福利微观数据，并且可以随着时间的推移进行比较。由于统计能力有限、阻碍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。本文展示了在考虑到减贫挑战的规模、迫切需要估计贫困趋势、但调查间插补又无法实现的情况下，联合部署回推和调查间插补如何有助于克服这一问题。成功的可能性更大，并且可以直接进行测试。在尼日利亚，可用于构建估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。这种数据格局可能会出现在其他环境中，在这些环境中，小型间隙调查的方法比大型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会发现，2009 年至 2019 年贫困率下降了 17 个百分点。然而，本文提出的方法均表明贫困率下降幅度要小得多，在 3 至 7 个百分点之间，这与尼日利亚的表现相呼应同期非货币福利指标。因此，本文就何时以及如何使用回推和调查间插补技术对监测减贫最有价值提供了指导。