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Testing for poverty dominance: an application to Canada

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  • Wen‐Hao Chen
  • Jean‐Yves Duclos

Abstract

The paper proposes and applies statistical tests for poverty dominance that check for whether poverty comparisons can be made robustly over ranges of poverty lines and classes of poverty indices. This helps provide both normative and statistical confidence in establishing poverty rankings across distributions. The tests, which can take into account the complex sampling procedures that are typically used by statistical agencies to generate household‐level surveys, are implemented using the Canadian Survey of Labour and Income Dynamics (SLID) for 1996, 1999, and 2002. Although the yearly cumulative distribution functions cross at the lower tails of the distributions, the more recent years tend to dominate earlier years for a relatively wide range of poverty lines. Failing to take into account SLID’s sampling variability (as is sometimes done) can inflate significantly one’s confidence in ranking poverty. Taking into account SLID’s complex sampling design (as has not been done before) can also decrease substantially the range of poverty lines over which a poverty ranking can be inferred. Ce mémoire propose et applique des tests de dominance de pauvreté qui s’assurent que des comparaisons d’états de pauvreté peuvent être faits robustement pour des éventails de seuils de pauvreté et des classes d’indices de pauvreté. Cela qui aide à fournir une confiance normative et statistique dans l’établissement du classement des distributions. Les tests (qui peuvent prendre en compte les procédures d’échantillonnage complexes qui sont habituellement utilisées par les agences statistiques pour leurs enquêtes au niveau des ménages) sont appliqués aux résultats de l’Enquête sur la dynamique du travail et du revenu (EDTR) pour 1996, 1999, et 2002. Même si les fonctions cumulatives de distribution se croisent dans leur zone inférieure, les années les plus récentes tendent à dominer les années antérieures pour un éventail de seuils de pauvreté. En compte, la variabilité d’échantillonnage de EDTR (comme c’est parfois le cas) peut gonfler de manière significative la confiance qu’on a dans les classements des états de pauvreté. Prendre en compte la complexité de la structure de l’échantillonnage de l' EDTR (comme on ne l’a pas fait auparavant) peut aussi décroître substantiellement l’éventail de seuils de pauvreté pour lesquels un classement des états de pauvreté peut être inféré.

Suggested Citation

  • Wen‐Hao Chen & Jean‐Yves Duclos, 2011. "Testing for poverty dominance: an application to Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(3), pages 781-803, August.
  • Handle: RePEc:wly:canjec:v:44:y:2011:i:3:p:781-803
    DOI: 10.1111/j.1540-5982.2011.01654.x
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    Cited by:

    1. Nabil Annabi & Youssef Boudribila & Simon Harvey, 2013. "Labour supply and income distribution effects of the working income tax benefit: a general equilibrium microsimulation analysis," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-33, December.
    2. Dirk Van de gaer & Joost Vandenbossche & José Luis Figueroa, 2014. "Children's Health Opportunities and Project Evaluation: Mexico's Oportunidades Program," The World Bank Economic Review, World Bank, vol. 28(2), pages 282-310.
    3. Sırma Şeker & Stephen Jenkins, 2015. "Poverty trends in Turkey," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(3), pages 401-424, September.
    4. Rami V. Tabri & Mathew J. Elias, 2024. "Testing for Restricted Stochastic Dominance under Survey Nonresponse with Panel Data: Theory and an Evaluation of Poverty in Australia," Papers 2406.15702, arXiv.org.
    5. Judith Clarke & Nilanjana Roy, 2012. "On statistical inference for inequality measures calculated from complex survey data," Empirical Economics, Springer, vol. 43(2), pages 499-524, October.
    6. Jean‐Yves Duclos & Agnès Zabsonré, 2014. "Social evaluations when populations differ in size," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(2), pages 605-633, May.
    7. Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.
    8. Tahsin Mehdi, 2020. "Testing for Stochastic Dominance up to a Common Relative Poverty Line," Econometrics, MDPI, vol. 8(1), pages 1-9, February.
    9. Hsu, Justine & Majdzadeh, Reza & Mills, Anne & Hanson, Kara, 2021. "A dominance approach to analyze the incidence of catastrophic health expenditures in Iran," Social Science & Medicine, Elsevier, vol. 285(C).

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General

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