Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia
Asset indices have been used since the late 1990s to measure wealth in developing countries. We extend the standard methodology for estimating asset indices using principal component analysis in two ways: by introducing constraints that force the indices to have increasing value as the number of ass...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | Inglés |
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John Wiley & Sons
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/146059 |
| _version_ | 1855515214700609536 |
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| author | Merola, Giovanni Maria Baulch, Bob |
| author_browse | Baulch, Bob Merola, Giovanni Maria |
| author_facet | Merola, Giovanni Maria Baulch, Bob |
| author_sort | Merola, Giovanni Maria |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Asset indices have been used since the late 1990s to measure wealth in developing countries. We extend the standard methodology for estimating asset indices using principal component analysis in two ways: by introducing constraints that force the indices to have increasing value as the number of assets owned increases, and by estimating sparse indices with a few key assets. This is achieved by combining categorical and sparse principal component analysis. We also apply this methodology to the estimation of per capita level asset indices. Using household survey data from northwest Vietnam and northeast Laos, we show that the resulting asset indices improve the prediction and ranking of income both at household and per capita level. |
| format | Journal Article |
| id | CGSpace146059 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | John Wiley & Sons |
| publisherStr | John Wiley & Sons |
| record_format | dspace |
| spelling | CGSpace1460592024-10-25T07:53:25Z Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia Merola, Giovanni Maria Baulch, Bob regression analysis component analysis (statistics) assets rural areas statistical methods Asset indices have been used since the late 1990s to measure wealth in developing countries. We extend the standard methodology for estimating asset indices using principal component analysis in two ways: by introducing constraints that force the indices to have increasing value as the number of assets owned increases, and by estimating sparse indices with a few key assets. This is achieved by combining categorical and sparse principal component analysis. We also apply this methodology to the estimation of per capita level asset indices. Using household survey data from northwest Vietnam and northeast Laos, we show that the resulting asset indices improve the prediction and ranking of income both at household and per capita level. 2019-11-12 2024-06-21T09:05:43Z 2024-06-21T09:05:43Z Journal Article https://hdl.handle.net/10568/146059 en Limited Access John Wiley & Sons Merola, Giovanni Maria; and Baulch, Bob. 2019. Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia. Review of Development Economics 23(2): 640-662. https://doi.org/10.1111/rode.12568 |
| spellingShingle | regression analysis component analysis (statistics) assets rural areas statistical methods Merola, Giovanni Maria Baulch, Bob Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title | Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title_full | Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title_fullStr | Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title_full_unstemmed | Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title_short | Using sparse categorical principal components to estimate asset indices: new methods with an application to rural Southeast Asia |
| title_sort | using sparse categorical principal components to estimate asset indices new methods with an application to rural southeast asia |
| topic | regression analysis component analysis (statistics) assets rural areas statistical methods |
| url | https://hdl.handle.net/10568/146059 |
| work_keys_str_mv | AT merolagiovannimaria usingsparsecategoricalprincipalcomponentstoestimateassetindicesnewmethodswithanapplicationtoruralsoutheastasia AT baulchbob usingsparsecategoricalprincipalcomponentstoestimateassetindicesnewmethodswithanapplicationtoruralsoutheastasia |