Poverty imputation in contexts without consumption data: a revisit with further refinements

Dang, Hai-Anh H., Kilic, Talip, Abanokova, Kseniya and Carletto, Calogero (2024) Poverty imputation in contexts without consumption data: a revisit with further refinements. Review of Income and Wealth. ISSN 0034-6586

Text (Review of Income and Wealth - 2024 - Dang - Poverty Imputation in Contexts Without Consumption Data A Revisit With Further) - Published Version Available under License Creative Commons Attribution. Download (1MB)

• https://www.webofscience.com/api/gateway...
• Scopus publication

Abstract

Survey-to-survey imputation has been increasingly employed to address data gaps for poverty measurement in poorer countries. We refine existing imputation models, using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. We find that adding household utility expenditures to a basic imputation model with household-level demographic and employment variables provides accurate estimates, which even fall within one standard error of the true poverty rates in many cases. The proposed imputation method performs better than several commonly used multiple imputation and machine learning techniques. Further adding geospatial variables improves accuracy, as does including additional community-level predictors (available from data in Vietnam) related to educational achievement, poverty, and asset wealth. Yet, within-country spatial heterogeneity exists, with certain models performing well for either urban areas or rural areas only. These results offer cost-saving inputs into future survey design.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	LSE
Subjects:	H Social Sciences > HC Economic History and Conditions
Date Deposited:	17 Oct 2024 14:33
Last Modified:	24 Oct 2024 18:30
URI:	http://eprints.lse.ac.uk/id/eprint/125798

Actions (login required)

View Item