Abstract
Although a wide array of stochastic dominance tests exist for poverty measurement and identification, they assume the income distributions have independent poverty lines or a common absolute (fixed) poverty line. We propose a stochastic dominance test for comparing income distributions up to a common relative poverty line (i.e., some fraction of the pooled median). A Monte Carlo study demonstrates its superior performance over existing methods in terms of power. The test is then applied to some Canadian household survey data for illustration.
Highlights
The seminal works of Sen (1976), Foster et al (1984), Atkinson (1987) have propagated a growing body of literature surrounding poverty measurement
Since poverty measures depend on a poverty line, which is an income threshold dividing the poor and non-poor, distributional orderings are sensitive to the choice of poverty lines
With increasing usage of relative poverty measures by international organizations (e.g., OECD 2016), we develop the asymptotic framework for testing for stochastic dominance up to a common relative poverty line
Summary
The seminal works of Sen (1976), Foster et al (1984), Atkinson (1987) have propagated a growing body of literature surrounding poverty measurement. Several statistical tests of stochastic dominance have been put forth in the literature (e.g., McFadden 1989; Kaur et al 1994; Anderson 1996; Davidson and Duclos 2000; Barrett and Donald 2003; Barrett et al 2016; Thompson and Stengos 2012), but the ones geared towards poverty measurement assume either separate poverty lines for each distribution or a common absolute poverty line between distributions. With increasing usage of relative poverty measures by international organizations (e.g., OECD 2016), we develop the asymptotic framework for testing for stochastic dominance up to a common relative poverty line (i.e., some fraction of the pooled median income level of two distributions).
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