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Sequential Stochastic Dominance And The Robustness Of Poverty Orderings

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  • Jean‐Yves Duclos
  • Paul Makdissi

Abstract

When comparing poverty across distributions, an analyst must select a poverty line to identify the poor, an equivalence scale to compare individuals from households of different compositions and sizes, and a poverty index to aggregate individual deprivation into an index of total poverty. A different choice of poverty line, poverty index or equivalence scale can of course reverse an initial poverty ordering. This paper develops easily‐checked sequential stochastic dominance conditions that throw light on the robustness of poverty comparisons to these important measurement issues. These general conditions extend well‐known results to any order of dominance, to the choice of individual versus family based aggregation, and to the estimation of “critical sets” of measurement assumptions. Our theoretical results are briefly illustrated using data for four countries drawn from the Luxembourg Income Study databases.

Suggested Citation

  • Jean‐Yves Duclos & Paul Makdissi, 2005. "Sequential Stochastic Dominance And The Robustness Of Poverty Orderings," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 51(1), pages 63-87, March.
  • Handle: RePEc:bla:revinw:v:51:y:2005:i:1:p:63-87
    DOI: 10.1111/j.1475-4991.2005.00145.x
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    References listed on IDEAS

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    1. Russell Davidson & Jean-Yves Duclos, 2000. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Econometrica, Econometric Society, vol. 68(6), pages 1435-1464, November.
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    More about this item

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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