Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis

Hai-Anh Dang (), Talip Kilic, Ksenia Abanokova and Gero Carletto

No 10867, Policy Research Working Paper Series from The World Bank

Abstract: Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results. Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time interval between surveys is associated with a lower probability of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R2) does not necessarily help. The results offer cost-saving inputs for future survey design.

Date: 2024-08-14
New Economics Papers: this item is included in nep-dev and nep-sea
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Related works:
Working Paper: Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis (2024)
Working Paper: Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis (2024)
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