Arbeitspapier

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

Household consumption data are often unavailable, not fully collected, or incomparable over time in poorer countries. Survey-to-survey imputation has been increasingly employed to address these data gaps for poverty measurement, but its effective use requires standardized protocols. We refine existing poverty 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. 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.

Sprache
Englisch

Erschienen in
Series: GLO Discussion Paper ; No. 1226

Klassifikation
Wirtschaft
Statistical Simulation Methods: General
Measurement and Analysis of Poverty
Economic Development: Human Resources; Human Development; Income Distribution; Migration
Thema
consumption
poverty
survey-to-survey imputation
household surveys
Vietnam
Ethiopia
Malawi
Nigeria
Tanzania
Sub-Saharan Africa

Ereignis
Geistige Schöpfung
(wer)
Dang, Hai-Anh H.
Kilic, Talip
Abanokova, Kseniya
Carletto, Calogero
Ereignis
Veröffentlichung
(wer)
Global Labor Organization (GLO)
(wo)
Essen
(wann)
2023

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Dang, Hai-Anh H.
  • Kilic, Talip
  • Abanokova, Kseniya
  • Carletto, Calogero
  • Global Labor Organization (GLO)

Entstanden

  • 2023

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