Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements

A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data f...

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Main Authors: Dang, Hai-Anh H., Kilic, Talip, Carletto, Calogero, Abanokova, Kseniya
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2021
Subjects:
Online Access:http://documents.worldbank.org/curated/undefined/914731636124765122/Poverty-Imputation-in-Contexts-without-Consumption-Data-A-Revisit-with-Further-Refinements
http://hdl.handle.net/10986/36550
id okr-10986-36550
recordtype oai_dc
spelling okr-10986-365502021-11-13T05:10:42Z Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements Dang, Hai-Anh H. Kilic, Talip Carletto, Calogero Abanokova, Kseniya POVERTY MEASUREMENT SURVEY-TO-SURVEY IMPUTATION HOUSEHOLD SURVEY EDUCATIONAL ACHIEVEMENT ASSET WEALTH DEMOGRAPHIC AND HEALTH SURVEY EMPLOYMENT A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures—as part of a basic imputation model with household-level demographic and employment variables—provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research. 2021-11-12T18:25:33Z 2021-11-12T18:25:33Z 2021-11 Working Paper http://documents.worldbank.org/curated/undefined/914731636124765122/Poverty-Imputation-in-Contexts-without-Consumption-Data-A-Revisit-with-Further-Refinements http://hdl.handle.net/10986/36550 English Policy Research Working Paper;No. 9838 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa Africa Eastern and Southern (AFE) Africa Western and Central (AFW) East Asia and Pacific Sub-Saharan Africa Ethiopia Malawi Nigeria Tanzania Vietnam
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
topic POVERTY MEASUREMENT
SURVEY-TO-SURVEY IMPUTATION
HOUSEHOLD SURVEY
EDUCATIONAL ACHIEVEMENT
ASSET WEALTH
DEMOGRAPHIC AND HEALTH SURVEY
EMPLOYMENT
spellingShingle POVERTY MEASUREMENT
SURVEY-TO-SURVEY IMPUTATION
HOUSEHOLD SURVEY
EDUCATIONAL ACHIEVEMENT
ASSET WEALTH
DEMOGRAPHIC AND HEALTH SURVEY
EMPLOYMENT
Dang, Hai-Anh H.
Kilic, Talip
Carletto, Calogero
Abanokova, Kseniya
Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
geographic_facet Africa
Africa Eastern and Southern (AFE)
Africa Western and Central (AFW)
East Asia and Pacific
Sub-Saharan Africa
Ethiopia
Malawi
Nigeria
Tanzania
Vietnam
relation Policy Research Working Paper;No. 9838
description A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures—as part of a basic imputation model with household-level demographic and employment variables—provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research.
format Working Paper
author Dang, Hai-Anh H.
Kilic, Talip
Carletto, Calogero
Abanokova, Kseniya
author_facet Dang, Hai-Anh H.
Kilic, Talip
Carletto, Calogero
Abanokova, Kseniya
author_sort Dang, Hai-Anh H.
title Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
title_short Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
title_full Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
title_fullStr Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
title_full_unstemmed Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements
title_sort poverty imputation in contexts without consumption data : a revisit with further refinements
publisher World Bank, Washington, DC
publishDate 2021
url http://documents.worldbank.org/curated/undefined/914731636124765122/Poverty-Imputation-in-Contexts-without-Consumption-Data-A-Revisit-with-Further-Refinements
http://hdl.handle.net/10986/36550
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