Correcting Sampling and Nonresponse Bias in Phone Survey Poverty Estimation Using Reweighting and Poverty Projection Models
To monitor the evolution of household living conditions during the COVID-19 pandemic, the World Bank conducted COVID-19 High-Frequency Phone Surveys in around 80 countries. Phone surveys are cheap and easy to implement, but they have some major limitations, such as the absence of poverty data, sampling bias due to incomplete telephone coverage in many developing countries, and frequent nonresponses to phone interviews. To overcome these limitations, the World Bank conducted pilots in 20 countries where the Survey of Wellbeing via Instant and Frequent Tracking, a rapid poverty monitoring tool, was adopted to estimate poverty rates based on 10 to 15 simple questions collected via phone interviews, and where sampling weights were adjusted to correct the sampling and nonresponse bias. This paper examines whether reweighting procedures and the Survey of Wellbeing via Instant and Frequent Tracking methodology can eliminate the bias in poverty estimation based on the COVID-19 High-Frequency Phone Surveys. Experiments using artificial phone survey samples show that (i) reweighting procedures cannot fully eliminate bias in poverty estimates, as previous research has demonstrated, but (ii) when combined with Survey of Wellbeing via Instant and Frequent Tracking poverty projections, they effectively eliminate bias in poverty estimates and other statistics.
| Main Authors: | , , |
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| Format: | Working Paper biblioteca |
| Language: | English English |
| Published: |
World Bank, Washington, DC
2024-01-03
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| Subjects: | PHONE SURVEY DATA, WEIGHTING, POVERTY PROJECTIONS, POVERTY ESTIMATION, CORRECTION OF SAMPLING BIAS, NONRESPONSE BIAS, COVID-19 PANDEMIC PHONE SURVEY, SURVEY OF WELLBEING, |
| Online Access: | http://documents.worldbank.org/curated/en/099439112202312439/IDU1e2c666e61beea141bd192b812ed3b1ea5a99 https://openknowledge.worldbank.org/handle/10986/40829 |
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