Computational tools for poverty measurement and analysis
This paper introduces some relatively straightforward computational tools for estimating poverty measures from the sort of data that are typically available from published sources. All that is required for using these tools is an elementary regression package. The methodology also easily lends itsel...
| Autor principal: | |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
1998
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/161213 |
| _version_ | 1855529824340148224 |
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| author | Datt, Gaurav |
| author_browse | Datt, Gaurav |
| author_facet | Datt, Gaurav |
| author_sort | Datt, Gaurav |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper introduces some relatively straightforward computational tools for estimating poverty measures from the sort of data that are typically available from published sources. All that is required for using these tools is an elementary regression package. The methodology also easily lends itself to a number of poverty simulations, some of which are discussed. The paper addresses the central question: How do we construct poverty measures from grouped data on consumption and income? Two broad approaches can be identified: simple interpolation methods and methods based on parameterized Lorenz curves. The paper briefly describes the two approaches and discusses why the second may be considered preferable. |
| format | Artículo preliminar |
| id | CGSpace161213 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 1998 |
| publishDateRange | 1998 |
| publishDateSort | 1998 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1612132025-11-06T06:41:41Z Computational tools for poverty measurement and analysis Datt, Gaurav income consumption poverty research methodology This paper introduces some relatively straightforward computational tools for estimating poverty measures from the sort of data that are typically available from published sources. All that is required for using these tools is an elementary regression package. The methodology also easily lends itself to a number of poverty simulations, some of which are discussed. The paper addresses the central question: How do we construct poverty measures from grouped data on consumption and income? Two broad approaches can be identified: simple interpolation methods and methods based on parameterized Lorenz curves. The paper briefly describes the two approaches and discusses why the second may be considered preferable. 1998 2024-11-21T09:54:12Z 2024-11-21T09:54:12Z Working Paper https://hdl.handle.net/10568/161213 en Open Access application/pdf International Food Policy Research Institute Datt, Gaurav. 1998. Computational tools for poverty measurement and analysis. FCND Discussion Paper 50. https://hdl.handle.net/10568/161213 |
| spellingShingle | income consumption poverty research methodology Datt, Gaurav Computational tools for poverty measurement and analysis |
| title | Computational tools for poverty measurement and analysis |
| title_full | Computational tools for poverty measurement and analysis |
| title_fullStr | Computational tools for poverty measurement and analysis |
| title_full_unstemmed | Computational tools for poverty measurement and analysis |
| title_short | Computational tools for poverty measurement and analysis |
| title_sort | computational tools for poverty measurement and analysis |
| topic | income consumption poverty research methodology |
| url | https://hdl.handle.net/10568/161213 |
| work_keys_str_mv | AT dattgaurav computationaltoolsforpovertymeasurementandanalysis |