Making Data Count
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 back casting 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 back casting and survey-to-survey imputation techniques can be most valuable for monitoring poverty reduction.
| Main Authors: | , , |
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| Format: | Journal Article biblioteca |
| Language: | English |
| Published: |
Published by Oxford University Press on behalf of the World Bank
2023-11-01
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| Subjects: | POVERTY MEASUREMENT, SURVEY-TO-SURVEY IMPUTATIONS, NIGERIA, NO POVERTY, SDG 1, |
| Online Access: | http://documents.worldbank.org/curated/en/099052508012414683/IDU1d6e0da8f115b8147721bf7f1645f870f3a17 https://hdl.handle.net/10986/42014 |
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