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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.

This paper contributes to the debate on ways to improve the calculation of inequality measures in developing countries experiencing severe budget constraints. Linear regression-based survey-to-survey imputation techniques are most frequently discussed in the literature. These are effective at estimating predictions of poverty indicators but are much less accurate with inequality indicators. To demonstrate this limited accuracy, the first part of the paper discusses several simulations using Moroccan Household Budget Surveys and Labor Force Surveys. The paper proposes a method for overcoming these limitations based on an algorithm that minimizes the sum of the squared difference between a certain number of direct estimates of an index and its empirical version obtained from the predicted values. Indeed, when comparing the estimated results with those directly estimated from the original sample, the bias is negligible. Furthermore, the inequality indices for the years for which there are only model estimates, rather than direct information on expenditures, seem to be consistent with Moroccan economic trends.

Poverty in low-income countries is usually measured with large and infrequent household surveys. A challenge is to find methods to measure poverty more frequently. The objective of this study is to test a method for predicting poverty, based upon a statistical model utilizing consumption surveys and light annual surveys. A decade of poverty predictions and regular poverty estimates in Malawi provides us with a unique real-life experience to better understand the suitability of such approaches to monitor trends in poverty. The analysis from Malawi suggests that a modelling approach works per se, given that information on the household's demographic composition is included in the model. The main challenge when predicting onto other surveys seems to be related to comparability between the surveys. Differences in implementation, questionnaire design and survey sample size are aspects that may contribute to incomparability of data collected between the surveys.