How Good Is a Map? Putting Small Area Estimation to the Test
This paper examines the performance small area of welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true values....
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| Format: | Journal Article |
| Language: | EN |
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2012
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| Online Access: | http://hdl.handle.net/10986/5641 |
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okr-10986-56412021-04-23T14:02:23Z How Good Is a Map? Putting Small Area Estimation to the Test Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter Single Equation Models Single Variables: Models with Panel Data Longitudinal Data Spatial Time Series C230 Model Construction and Estimation C510 Data Collection and Data Estimation Methodology Computer Programs: General C800 Economic Development: Human Resources Human Development Income Distribution Migration O150 Economic Development: Regional, Urban, and Rural Analyses Transportation O180 General Regional Economics: Econometric and Input-Output Models Other Models R150 Urban, Rural, and Regional Economics: Regional Migration Regional Labor Markets Population Neighborhood Characteristics R230 This paper examines the performance small area of welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true values. The target populations are constructed using actual data from a census of households in a set of rural Mexican communities. Estimates are examined along three criteria: accuracy of confidence intervals, bias and correlation with true values. We find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. 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 we show that bias increases if underlying models fail to capture latent location effects. 2012-03-30T07:33:49Z 2012-03-30T07:33:49Z 2008 Journal Article Rivista Internazionale di Scienze Sociali 0035676X http://hdl.handle.net/10986/5641 EN http://creativecommons.org/licenses/by-nc-nd/3.0/igo World Bank Journal Article Mexico |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
EN |
| topic |
Single Equation Models Single Variables: Models with Panel Data Longitudinal Data Spatial Time Series C230 Model Construction and Estimation C510 Data Collection and Data Estimation Methodology Computer Programs: General C800 Economic Development: Human Resources Human Development Income Distribution Migration O150 Economic Development: Regional, Urban, and Rural Analyses Transportation O180 General Regional Economics: Econometric and Input-Output Models Other Models R150 Urban, Rural, and Regional Economics: Regional Migration Regional Labor Markets Population Neighborhood Characteristics R230 |
| spellingShingle |
Single Equation Models Single Variables: Models with Panel Data Longitudinal Data Spatial Time Series C230 Model Construction and Estimation C510 Data Collection and Data Estimation Methodology Computer Programs: General C800 Economic Development: Human Resources Human Development Income Distribution Migration O150 Economic Development: Regional, Urban, and Rural Analyses Transportation O180 General Regional Economics: Econometric and Input-Output Models Other Models R150 Urban, Rural, and Regional Economics: Regional Migration Regional Labor Markets Population Neighborhood Characteristics R230 Demombynes, Gabriel Elbers, Chris Lanjouw, Jean O. Lanjouw, Peter How Good Is a Map? Putting Small Area Estimation to the Test |
| geographic_facet |
Mexico |
| relation |
http://creativecommons.org/licenses/by-nc-nd/3.0/igo |
| description |
This paper examines the performance small area of welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true values. The target populations are constructed using actual data from a census of households in a set of rural Mexican communities. Estimates are examined along three criteria: accuracy of confidence intervals, bias and correlation with true values. We find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. 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 we show that bias increases if underlying models fail to capture latent location effects. |
| format |
Journal Article |
| 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 Is a Map? Putting Small Area Estimation to the Test |
| title_short |
How Good Is a Map? Putting Small Area Estimation to the Test |
| title_full |
How Good Is a Map? Putting Small Area Estimation to the Test |
| title_fullStr |
How Good Is a Map? Putting Small Area Estimation to the Test |
| title_full_unstemmed |
How Good Is a Map? Putting Small Area Estimation to the Test |
| title_sort |
how good is a map? putting small area estimation to the test |
| publishDate |
2012 |
| url |
http://hdl.handle.net/10986/5641 |
| _version_ |
1764395784042708992 |