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Estimating Poverty in the Absence of Consumption Data : The Case of Liberia

ALLOCATION OF RESOURCES CALCULATION CELL PHONE CELL PHONES CELLPHONE CELLPHONES CHANGES IN POVERTY CHILD MORTALITY COMMUNITY HEALTH CONFLICT CONSUMPTION AGGREGATE CONSUMPTION DATA CONSUMPTION EXPENDITURE CONSUMPTION EXPENDITURES CONSUMPTION QUINTILES CORRELATES OF POVERTY COUNTERFACTUAL CROP DIVERSITY CROP PRODUCTION DEMOGRAPHIC INFORMATION DIMENSIONS OF POVERTY DISCRIMINANT ANALYSIS DROP IN POVERTY ECONOMIC GROWTH ELECTRICITY ENUMERATION ESTIMATES OF POVERTY EXTREME POVERTY FACTOR ANALYSIS FAMINE FIREWOOD FREE SOFTWARE GLOBAL PARTNERSHIP HOUSEHOLD CONSUMPTION HOUSEHOLD DEMOGRAPHICS HOUSEHOLD HEAD HOUSEHOLD HEADS HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEYS HOUSING HUMAN CAPITAL HUMAN DEVELOPMENT HUMAN DEVELOPMENT INDEX IMPUTATION IMPUTATION METHOD IMPUTATION METHODS IMPUTATION PROCESS IMPUTATIONS INEQUALITY INFORMATION SERVICES LAND OWNERSHIP LAND SIZE LANDHOLDINGS LIVING STANDARDS MATERNAL MORTALITY MEANS TESTS MISSING DATA MISSING VALUES MULTIPLE IMPUTATION MULTIPLE IMPUTATIONS NATIONAL POVERTY NATIONAL POVERTY LINE NUTRITION OPEN ACCESS PER CAPITA CONSUMPTION POOR POOR HOUSEHOLDS POVERTY ANALYSIS POVERTY ESTIMATES POVERTY LEVELS POVERTY LINES POVERTY MAPPING POVERTY MEASUREMENT POVERTY MEASURES POVERTY QUINTILES POVERTY RANKINGS POVERTY RATES POVERTY REDUCTION POVERTY STATUS PRECISION PREDICTION PREDICTIONS PRINCIPAL COMPONENTS ANALYSIS RADIO RESULT RESULTS RURAL RURAL AREAS RURAL ECONOMY RURAL INEQUALITY SAMPLE DESIGN SAMPLE SIZE SATELLITE SOCIAL PROGRAMS STANDARD ERRORS STATA STATISTICAL ANALYSIS STATISTICAL ANALYSIS SOFTWARE STATISTICAL METHODS STATISTICIANS TARGETING TARGETS TECHNICAL UNIVERSITY TELEVISION TIME PERIOD TIME SERIES USES VERIFICATION WEB WELFARE INDICATOR
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World Bank Group, Washington, DC
Africa | Liberia
2014-10-02T19:55:24Z | 2014-10-02T19:55:24Z | 2014-09

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.

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