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

A key challenge with poverty measurement is that household consumption data are oftenunavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldomfeasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is acost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choicesand ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existingpoverty 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 householdutility consumption expenditures—as part of a basic imputation model with household-level demographic andemployment variables—provides poverty estimates that are not statistically significantly different from the true povertyrates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatialvariables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additionalcommunity-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 modelperformance, with certain models performing well for either urban areas or rural areas only. The paper providesoperationally-relevant and cost-saving inputs into thedesign of future surveys implemented with a poverty imputation objective and suggests directions for future research.