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Small area estimates of poverty and inequality statistics, through survey-to-census imputation that lets consumption be estimated for each and every household in a census, are useful for at least three reasons. First, they can help improve the effectiveness of public spending, by targeting to prevent the leakage of benefits to the non-poor (and prevent the under-coverage of the poor). If poor people are concentrated in certain areas, spatial targeting by directing extra development projects and public services to those areas, may be more feasible than trying to individually target the poor. Geographic targeting is highly relevant in countries like Timor Leste, where mountainous topography contributes to high levels of heterogeneity. In similar environments, such as Papua New Guinea, the enclave nature of some modern economic development has created high levels of spatial inequality. The basic details are that household survey data are used to estimate a model of consumption, with explanatory variables restricted to those that have overlapping distributions from a census. The coefficients from this model are then combined with the variables from the census, and consumption is predicted for each household in the census. With these predictions available for all households, inequality and poverty statistics can be estimated for small geographic areas (Elbers et al, 2003).2 In the results below, the poverty statistics that are calculated by using the predicted consumption data for each census household are reported at the suco level (n=442). For the headcount poverty rate, the standard errors at the suco level (relative to the poverty index) average one-quarter and so this is a comparable degree of precision to what the survey offered at the municipality level (n=13) for a variable like the poverty severity index.

Demographic and Health Survey --- Poverty Line

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The World Bank is an international leader in the methodology and implementation of household surveys, working in close partnership with national statistics offices (NSOs) around the world. This guidebook is a consolidation of field-tested best practices to implement, improve, and modernize nationally representative multi-topic household surveys for monitoring welfare and poverty. Offered as a reference guide for task team leaders (TTLs) within the World Bank, the guidebook is intended as a powerful tool for any survey practitioners (such as NSOs, development partners, educators, researchers, and students) implementing household surveys in low- and middle-income countries. This guidebook starts with survey design, the first step in any survey undertaking, with careful attention given to minimizing non-sampling errors. Subsequent sections are sampling; questionnaire modules, which form the core of this guidebook; followed by geographic information systems (GIS); computer-assisted personal interviewing (CAPI); and finally, documentation and dissemination of the resulting data.

Data Collection --- Demographic And Health Survey --- Gender --- ICT Applications --- Inequality --- Information and Communication Technologies --- Living Standards --- Poverty Assessment --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

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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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Reducing child undernutrition is a key social policy objective of the Ethiopian government. espite substantial reduction over the past decade and a half, child undernutrition is still high. With 48 percent of children stunted, underweight, or wasted, undernutrition remains an important child health challenge. The existing literature highlights that the targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part because of the lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 emographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even controlling for household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished in each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number, and concentration of undernourished children point to vastly different locations. There is limited correlation between the shares of children underweight and stunted across woredas, indicating that different locations face different challenges.

Adolescent Health. --- Child Malnutrition. --- Demographic and Health Survey. --- Early Child and Children's Health. --- Health Monitoring and Evaluation. --- Health, Nutrition and Population. --- Population and Development. --- Small Area Estimation. --- Targeting.

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Reducing child undernutrition is a key social policy objective of the Ethiopian government. espite substantial reduction over the past decade and a half, child undernutrition is still high. With 48 percent of children stunted, underweight, or wasted, undernutrition remains an important child health challenge. The existing literature highlights that the targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part because of the lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 emographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even controlling for household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished in each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number, and concentration of undernourished children point to vastly different locations. There is limited correlation between the shares of children underweight and stunted across woredas, indicating that different locations face different challenges.

Adolescent Health. --- Child Malnutrition. --- Demographic and Health Survey. --- Early Child and Children's Health. --- Health Monitoring and Evaluation. --- Health, Nutrition and Population. --- Population and Development. --- Small Area Estimation. --- Targeting.