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

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 sour...

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Main Authors: Dabalen, Andrew, Graham, Errol, Himelein, Kristen, Mungai, Rose
Format: Policy Research Working Paper
Language:English
en_US
Published: World Bank Group, Washington, DC 2014
Subjects:
WEB
Online Access:http://documents.worldbank.org/curated/en/2014/09/20177049/estimating-poverty-absence-consumption-data-case-liberia
http://hdl.handle.net/10986/20336
id okr-10986-20336
recordtype oai_dc
spelling okr-10986-203362021-04-23T14:03:55Z Estimating Poverty in the Absence of Consumption Data : The Case of Liberia Dabalen, Andrew Graham, Errol Himelein, Kristen Mungai, Rose 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 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. 2014-10-02T19:55:24Z 2014-10-02T19:55:24Z 2014-09 http://documents.worldbank.org/curated/en/2014/09/20177049/estimating-poverty-absence-consumption-data-case-liberia http://hdl.handle.net/10986/20336 English en_US Policy Research Working Paper;No. 7024 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank Group, Washington, DC Publications & Research :: Policy Research Working Paper Publications & Research Africa Liberia
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
en_US
topic 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
spellingShingle 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
Dabalen, Andrew
Graham, Errol
Himelein, Kristen
Mungai, Rose
Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
geographic_facet Africa
Liberia
relation Policy Research Working Paper;No. 7024
description 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.
format Publications & Research :: Policy Research Working Paper
author Dabalen, Andrew
Graham, Errol
Himelein, Kristen
Mungai, Rose
author_facet Dabalen, Andrew
Graham, Errol
Himelein, Kristen
Mungai, Rose
author_sort Dabalen, Andrew
title Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
title_short Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
title_full Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
title_fullStr Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
title_full_unstemmed Estimating Poverty in the Absence of Consumption Data : The Case of Liberia
title_sort estimating poverty in the absence of consumption data : the case of liberia
publisher World Bank Group, Washington, DC
publishDate 2014
url http://documents.worldbank.org/curated/en/2014/09/20177049/estimating-poverty-absence-consumption-data-case-liberia
http://hdl.handle.net/10986/20336
_version_ 1764445064917942272