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Empirical Likelihood-Based Inference for Poverty Measures with Relative Poverty Lines

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  • Brennan S. Thompson

Abstract

In this article, we propose an empirical likelihood-based method of inference for decomposable poverty measures utilizing poverty lines which are some fraction of the median of the underlying income distribution. Specifically, we focus on making poverty comparisons between two subgroups of the population which share the same poverty line. Our proposed method is assessed using a Monte Carlo simulation and is applied to some Canadian household income data.

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  • Brennan S. Thompson, 2013. "Empirical Likelihood-Based Inference for Poverty Measures with Relative Poverty Lines," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 513-523, December.
    • Handle: RePEc:taf:emetrv:v:32:y:2013:i:4:p:513-523
    DOI: 10.1080/07474938.2012.690671
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    References listed on IDEAS

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    1. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    2. Davidson, Russell & Flachaire, Emmanuel, 2007. "Asymptotic and bootstrap inference for inequality and poverty measures," Journal of Econometrics, Elsevier, vol. 141(1), pages 141-166, November.
    3. Ian Preston, 1995. "Sampling Distributions of Relative Poverty Statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(1), pages 91-99, March.
    4. Zheng, Buhong, 2001. "Statistical inference for poverty measures with relative poverty lines," Journal of Econometrics, Elsevier, vol. 101(2), pages 337-356, April.
    5. Kakwani, Nanak, 1993. "Statistical Inference in the Measurement of Poverty," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 632-639, November.
    6. 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.
    7. Sen, Amartya K, 1976. "Poverty: An Ordinal Approach to Measurement," Econometrica, Econometric Society, vol. 44(2), pages 219-231, March.
    8. Bishop, John A & Formby, John P & Zheng, Buhong, 1997. "Statistical Inference and the Sen Index of Poverty," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(2), pages 381-387, May.
    9. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
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    Cited by:

    1. Mehdi, Tahsin & Stengos, Thanasis, 2014. "Empirical likelihood-based inference for the generalized entropy class of inequality measures," Economics Letters, Elsevier, vol. 123(1), pages 54-57.
    2. Tahsin Mehdi, 2020. "Testing for Stochastic Dominance up to a Common Relative Poverty Line," Econometrics, MDPI, vol. 8(1), pages 1-9, February.
    3. J. F. Muñoz & E. à lvarez-Verdejo & R. M. García-Fernández, 2018. "On Estimating the Poverty Gap and the Poverty Severity Indices With Auxiliary Information," Sociological Methods & Research, , vol. 47(3), pages 598-625, August.
    4. Pécastaing, Nicolas & Dávalos, Jorge & Inga, Andy, 2018. "The effect of Peru's CDM investments on households’ welfare: An econometric approach," Energy Policy, Elsevier, vol. 123(C), pages 198-207.

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