World Bank - Science and Technology Development

Obtaining consistent estimates on poverty over time as well as monitoring poverty trends on a timely basis is a priority concern for policy makers. However, these objectives are not readily achieved in practice when household consumption data are neither frequently collected, nor constructed using consistent and transparent criteria. This paper develops a formal framework for survey-to-survey poverty imputation in an attempt to overcome these obstacles, and to elevate the discussion of these methods beyond the largely ad-hoc efforts in the existing literature. The framework introduced here imposes few restrictive assumptions, works with simple variance formulas, provides guidance on the selection of control variables for model building, and can be generally applied to imputation either from one survey to another survey with the same design, or to another survey with a different design. Empirical results analyzing the Household Expenditure and Income Survey and the Unemployment and Employment Survey in Jordan are quite encouraging, with imputation-based poverty estimates closely tracking the direct estimates of poverty. 2014-09-23T04:00:00.000Z2014-09-23T04:00:00.000ZScience and Technology Development|Poverty Reduction|Private Sector DevelopmentRural Poverty Reduction|Statistical & Mathematical Sciences|Achieving Shared Growth|E-BusinessMiddle East and North AfricaDang, Hai-Anh H.|Lanjouw, Peter F.|Serajuddin, UmarUpdating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income countryEnglishJordanScience and Technology Development|Poverty Reduction|Private Sector DevelopmentRural Poverty Reduction|Statistical & Mathematical Sciences|Achieving Shared Growth|E-BusinessWPS7043JordanEnglishPolicy Research Working PaperMiddle East and North Africa

In much of the developing world, the demand for high frequency quality household data for poverty monitoring and program design far outstrips the capacity of the statistics bureau to provide such data. In these environments, all available data sources must be leveraged. Most surveys, however, do not collect the detailed consumption data necessary to construct aggregates and poverty lines to measure poverty directly. This paper benefits from a shared listing exercise for two large-scale national household surveys conducted in Liberia in 2007 to explore alternative methodologies to estimate poverty indirectly. The first is an asset-based model that is commonly used in Demographic and Health Surveys. The second is a survey-to-survey imputation that makes use of small area estimation techniques. In addition to a standard base model, separate models are estimated for urban and rural areas and an expanded model that includes climatic variables. Special attention is paid to the inclusion of cell phones, with implications for other assets whose cost and availability may be changing rapidly. The results demonstrate substantial limitations with asset-based indexes, but also leave questions as to the accuracy and stability of imputation models.2014-09-05T04:00:00.000Z2014-09-05T04:00:00.000ZScience and Technology Development|Poverty Reduction|Macroeconomics and Economic Growth|Private Sector DevelopmentRural Poverty Reduction|Regional Economic Development|E-Business|Statistical & Mathematical SciencesAfricaDabalen, Andrew|Graham, Errol|Himelein, Kristen|Mungai, RoseEstimating poverty in the absence of consumption data : the case of LiberiaEnglishLiberiaScience and Technology Development|Poverty Reduction|Macroeconomics and Economic Growth|Private Sector DevelopmentRural Poverty Reduction|Regional Economic Development|E-Business|Statistical & Mathematical SciencesWPS7024LiberiaEnglishPolicy Research Working PaperAfrica

This note adapts results by Huang and Hidiroglou (2003) on Generalized Least Squares estimation and Empirical Bayes prediction for linear mixed models with sampling weights. The objective is to incorporate these results into the poverty mapping approach put forward by Elbers et al. (2003). The estimators presented here have been implemented in version 2.5 of POVMAP, the custom-made poverty mapping software developed by the World Bank.2014-09-11T04:00:00.000Z2014-09-11T04:00:00.000ZScience and Technology Development|Poverty Reduction|Agriculture|EducationStatistical & Mathematical Sciences|Crops and Crop Management Systems|Poverty Monitoring & Analysis|Science Education|Scientific Research & Science ParksThe World Regionvan der Weide, RoyGLS estimation and empirical bayes prediction for linear mixed models with Heteroskedasticity and sampling weights : a background study for the POVMAP projectEnglishWorldScience and Technology Development|Poverty Reduction|Agriculture|EducationStatistical & Mathematical Sciences|Crops and Crop Management Systems|Poverty Monitoring & Analysis|Science Education|Scientific Research & Science ParksWPS7028WorldEnglishPolicy Research Working PaperThe World Region