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...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
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| 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 |
| Summary: | 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. |
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