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Year: 2014 Publisher: Washington, D.C., The World Bank,
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This paper studies the benefits, in terms of reliability and frequency of poverty statistics, of conducting a hybrid survey that collects non-consumption data from all surveyed households and consumption data from only a small subsample. Collecting detailed consumption or income data for the purpose of estimating poverty is costly and many low-income countries cannot afford to carry out such surveys on a regular basis. One option is to collect only non-consumption data and use consumption models developed from a previous round of household survey data to project poverty data. Although this approach is cost-effective because collection of non-consumption data is much cheaper than collection of consumption data, it is vulnerable to a structural change between the current and previous household surveys and might produce poverty estimates that are not comparable with the previous ones. Instead, the hybrid approach creates consumption models from a subsample of the current survey and applies them to the entire survey to project consumption data for all households in the sample. This paper examines the hybrid approach with data from the Bangladesh Household Income Expenditure Surveys of 2000 and 2005. Improvements in accuracy are achieved even with subsamples of just 320 or 640 households. Budget simulations confirm that the additional cost of collecting consumption data for such small subsamples is minimal.
Consumption --- E-Business --- Macroeconomics and Economic Growth --- Poverty Assessments --- Poverty Reduction --- Private Sector Development --- Rural Poverty Reduction --- Small Area Estimation Poverty Maps --- Subsample --- Survey to Survey Imputation
Book
Year: 2021 Publisher: Washington, D.C. : The World Bank,
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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.
Asset Wealth --- Demographic and Health Survey --- Educational Achievement --- Employment --- Household Survey --- Inequality --- Living Standards --- Poverty Lines --- Poverty Measurement --- Poverty Reduction --- Survey-To-Survey Imputation
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