How Good a Map? Putting Small Area Estimation to the Test
The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.
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dig-okr-1098670402024-08-08T17:00:09Z How Good a Map? Putting Small Area Estimation to the Test Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures. 2012-06-04T19:29:50Z 2012-06-04T19:29:50Z 2007-03 http://documents.worldbank.org/curated/en/2007/03/7488024/good-map-putting-small-area-estimation-test https://hdl.handle.net/10986/7040 English Policy Research Working Paper; No. 4155 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank application/pdf text/plain World Bank, Washington, DC |
| institution |
Banco Mundial |
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DSpace |
| country |
Estados Unidos |
| countrycode |
US |
| component |
Bibliográfico |
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En linea |
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dig-okr |
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biblioteca |
| region |
America del Norte |
| libraryname |
Biblioteca del Banco Mundial |
| language |
English |
| topic |
APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS |
| spellingShingle |
APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter How Good a Map? Putting Small Area Estimation to the Test |
| description |
The authors examine the performance of
small area welfare estimation. The method combines census
and survey data to produce spatially disaggregated poverty
and inequality estimates. To test the method, they compare
predicted welfare indicators for a set of target populations
with their true values. They construct target populations
using actual data from a census of households in a set of
rural Mexican communities. They examine estimates along
three criteria: accuracy of confidence intervals, bias, and
correlation with true values. The authors find that while
point estimates are very stable, the precision of the
estimates varies with alternative simulation methods. While
the original approach of numerical gradient estimation
yields standard errors that seem appropriate, some
computationally less-intensive simulation procedures yield
confidence intervals that are slightly too narrow. The
precision of estimates is shown to diminish markedly if
unobserved location effects at the village level are not
well captured in underlying consumption models. With well
specified models there is only slight evidence of bias, but
the authors show that bias increases if underlying models
fail to capture latent location effects. Correlations
between estimated and true welfare at the local level are
highest for mean expenditure and poverty measures and lower
for inequality measures. |
| topic_facet |
APPROACH CAPITA CONSUMPTION CAPITA EXPENDITURE CASE STUDY CENSUSES COMMUNITY LEVEL CONSUMPTION LEVEL CONSUMPTION MODEL CONSUMPTION SMOOTHING CORRELATIONS DEGREES OF FREEDOM DELTA METHOD DEVELOPING COUNTRIES DISTRICT-LEVEL DISTURBANCE TERM ECONOMIC GROWTH EDUCATION ESTIMATES OF POVERTY ESTIMATION OF POVERTY ESTIMATION PROCEDURE EXPERIMENTS EXPLANATORY VARIABLES FOLLOW UP SURVEYS GEOGRAPHIC PROFILE OF POVERTY HEADCOUNT RATE HEALTH HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEY DATA HOUSEHOLD WELFARE HOUSEHOLDS IDIOSYNCRATIC COMPONENT IDIOSYNCRATIC ERROR INCOME INCOME DATA INEQUALITY LEVEL ESTIMATION OF WELFARE LEVEL OF AGGREGATION NUMBER OF HOUSEHOLDS NUTRITION PARAMETER ESTIMATES PARAMETRIC PARAMETRIC APPROACH PARAMETRIC DISTRIBUTIONS POLICY RESEARCH POLICY RESEARCH WORKING PAPER POOR POOR COMMUNITIES POOR HOUSEHOLDS POPULATION CENSUS POPULATION SIZE POVERTY GAP POVERTY INDICES POVERTY LINE POVERTY MAPPING POVERTY MAPPING METHODOLOGY POVERTY MAPS POVERTY MEASURES POVERTY RATE PROGRESS REGRESSORS RELIABILITY RESEARCH WORKING PAPERS RESEARCHERS RURAL SIMULATION SIMULATION METHODS SIMULATION PROCEDURES SIMULATIONS SMALL AREA ESTIMATION SOCIAL SPENDING SPATIAL DIMENSIONS OF POVERTY STANDARD DEVIATION STANDARD ERROR STANDARD ERRORS TARGETING TECHNIQUES TIME SERIES ANALYSIS VARIANCE-COVARIANCE MATRIX WELFARE INDICATORS |
| author |
Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter |
| author_facet |
Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter |
| author_sort |
Demombynes, Gabriel |
| title |
How Good a Map? Putting Small Area Estimation to the Test |
| title_short |
How Good a Map? Putting Small Area Estimation to the Test |
| title_full |
How Good a Map? Putting Small Area Estimation to the Test |
| title_fullStr |
How Good a Map? Putting Small Area Estimation to the Test |
| title_full_unstemmed |
How Good a Map? Putting Small Area Estimation to the Test |
| title_sort |
how good a map? putting small area estimation to the test |
| publisher |
World Bank, Washington, DC |
| publishDate |
2007-03 |
| url |
http://documents.worldbank.org/curated/en/2007/03/7488024/good-map-putting-small-area-estimation-test https://hdl.handle.net/10986/7040 |
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AT demombynesgabriel howgoodamapputtingsmallareaestimationtothetest AT elberschris howgoodamapputtingsmallareaestimationtothetest AT lanjouwjeano howgoodamapputtingsmallareaestimationtothetest AT lanjouwpeter howgoodamapputtingsmallareaestimationtothetest |
| _version_ |
1807158151748780032 |