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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.