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Consistent Tests for Poverty Dominance Relations

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Abstract

This paper considers methods for comparing poverty in two income distributions. We first discuss the concept and usefulness of the Poverty Gap Pro le (PGP) for comparing poverty in two populations. Dominance of one PGP over another suggests poverty dom- inance for a wide class of indices which may be expressed as functionals of the PGP. We then discuss hypotheses that can be used to test poverty dominance in terms of the PGP and introduce and justify a test statistic based on empirical PGP's where we allow for the poverty line to be estimated. A method for obtaining critical values by simulation is pro- posed that takes account of estimation of the poverty line. The fi nite sample properties of the methods are examined in the context of a Monte Carlo simulation study and the methods are illustrated in an assessment of relative consumption poverty in Australian over the period 1988/89-2009/10. JEL Classification: C01, C12, C21

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  • Garry F. Barrett & Stephen G. Donald & Yu-Chin Hsu, 2015. "Consistent Tests for Poverty Dominance Relations," IEAS Working Paper : academic research 15-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:15-a002
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    2. Greg Kaplan & Gianni La Cava & Tahlee Stone, 2018. "Household Economic Inequality in Australia," The Economic Record, The Economic Society of Australia, vol. 94(305), pages 117-134, June.
    3. Naouel Chtioui & Mohamed Ayadi, 2018. "Rank-based poverty measures and poverty ordering with an application to Tunisia," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(2), pages 117-139, July.
    4. Edwin Fourrier-Nicolaï & Michel Lubrano, 2020. "Bayesian inference for TIP curves: an application to child poverty in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 91-111, March.
    5. Pittau, Maria Grazia & Conti, Pier Luigi & Zelli, Roberto, 2025. "Inference for deprivation profiles in a binary setting," Journal of Econometrics, Elsevier, vol. 249(PB).
    6. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2020. "Bayesian assessment of Lorenz and stochastic dominance," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 767-799, May.
    7. Tahsin Mehdi, 2020. "Testing for Stochastic Dominance up to a Common Relative Poverty Line," Econometrics, MDPI, vol. 8(1), pages 1-9, February.
    8. Mariateresa Ciommi & Chiara Gigliarano & Francesco M. Chelli, 2021. "Incidence, intensity and inequality of poverty in Italy," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(4), pages 31-41, October-D.
    9. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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