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Computational Tools For Poverty Measurement And Analysis

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  • Datt, Gaurav

Abstract

This paper introduces some relatively simple 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 that are discussed. The paper addresses the central question: How do we construct poverty measures from grouped data? Two broad approaches are examined: simple interpolation methods and methods based on parameterized Lorenz curves. The second method is examined in detail.

Suggested Citation

  • Datt, Gaurav, 1998. "Computational Tools For Poverty Measurement And Analysis," FCND Discussion Papers 94862, CGIAR, International Food Policy Research Institute (IFPRI).
    • Handle: RePEc:ags:fcnddp:94862
    DOI: 10.22004/ag.econ.94862
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    1. Kakwani, N., 1990. "Poverty And Economic Growth: With Application To Cote D'Ivoire," Papers 63, World Bank - Living Standards Measurement.
    2. Kakwani, N., 1990. "Poverty And Economic Growth; With Application To Cote D'Ivoire," Papers 90-2, New South Wales - School of Economics.
    3. Datt, Gaurav & Ravallion, Martin, 1990. "Regional disparities, targeting, and poverty in India," Policy Research Working Paper Series 375, The World Bank.
    4. Kakwani, Nanak, 1980. "On a Class of Poverty Measures," Econometrica, Econometric Society, vol. 48(2), pages 437-446, March.
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