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 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.

Interpolation methods essentially involve fitting a distribution function to the grouped data. To estimate the headcount index, the distribution function is typically fitted over the class interval containing the poverty line. There are two basic limitations in using interpolation methods. First, they tend to provide relatively inaccurate predictions of the distribution function at selected points. This is particularly true of linear interpolation. Second, the calculation of distributionally sensitive poverty measures using interpolation methods can be cumbersome and inexact.

An alternative methodology for estimating poverty measures is based on parameterized Lorenz curves. This methodology is preferred both for its relative accuracy and the ease with which it helps perform a number of poverty simulations. The building blocks of the methodology are the Lorenz curve and mean consumption. The Lorenz curve captures all the information on the pattern

This paper introduces some computational tools for estimating poverty measures from group data that can be implemented using an elementary regression package.

of relative inequalities in the population. It is independent of any considerations of absolute living standards. Mean consumption provides a measure of the average living standards. The Lorenz curve and mean consumption together contain all the information needed to fully track the distribution function of consumption (or any other measure of welfare used in the analysis).

The paper describes the steps to constructing poverty measures (head-count index, poverty gap index, and the squared poverty gap index). It also discusses the method for checking the validity of a Lorenz curve, and the rationale for choosing between Lorenz curve parameterizations. The paper concludes with illustrations of some policy simulations of poverty that the Lorenz curve-based methodology facilitates: simulating poverty measures for different poverty lines; simulating poverty under distributionally neutral growth; decomposing changes in poverty into growth and redistribution components; and simulating the contribution of regional or sectoral disparities in mean consumption to aggregate poverty.

An interactive software, POVCAL, that implements the algorithms presented in the paper has been developed. POVCAL is available as a freeware at the following website: www.worldbank.org/html/prdph/lsms/tools/povcal.

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