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Jeanyves Duclos - One of the best experts on this subject based on the ideXlab platform.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    Econometrica, 2000
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal Poverty lines (or income censoring thresholds) up to which we may confidently assert that Poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the Measurement of Poverty is also established. The statistical results are established for deterministic or stochastic Poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases.

  • Absolute and Relative Deprivation and the Measurement of Poverty
    SSRN Electronic Journal, 1999
    Co-Authors: Jeanyves Duclos, Philippe Grégoire
    Abstract:

    This paper develops the link between Poverty and inequality by focussing on a class of Poverty indices (some of them well-known) which aggregate normative concerns for absolute and relative deprivation. The indices are distinguished by a parameter that captures the ethical sensitivity of Poverty Measurement to "exclusion" or "relative-deprivation" aversion. We also show how the indices can be readily used to predict the impact of growth on Poverty. An illustration using LIS data finds that the United States show more relative deprivation than Denmark and Belgium whatever the percentiles considered, but that overall deprivation comparisons of the four countries considered will generally necessarily depend on the intensity of the ethical concern for relative deprivation. The impact of growth on Poverty is also seen to depend on the presence of and on the attention granted to concerns over relative deprivation.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    Cahiers de recherche, 1998
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal Poverty lines (or income censoring thresholds) up to which we may confidently assert that Poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the Measurement of Poverty is also established. The statistical results are established for deterministic or stochastic Poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les proprietes asymptotiques de plusieurs estimateurs frequemment utilises pour ordonner les repartitions de revenus en termes de pauvrete, bien-etre social, et inegalite. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvrete couramment en usage ainsi que les estimateurs des courbes utiles pour l'inference de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent de determiner si la pauvrete, l'inegalite ou le bien-etre social sont plus eleves dans une repartition que dans une autre pour des classes generales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvrete ou de censure des revenus jusqu'auxquels on peut affirmer sans ambiguite que la pauvrete ou le bien-etre social sont plus eleves dans une repartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvrete est aussi derivee. Les resultats statistiques s'appliquent a des seuils deterministes ou stochastiques et a des echantillons dependants ou independants. On illustre brievement nos resultats a l'aide de donnees sur 6 pays tirees des banques de donnees du Luxembourg Income Study.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    G.R.E.Q.A.M., 1998
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices.

Mozaffar Qizilbash - One of the best experts on this subject based on the ideXlab platform.

  • A note on the Measurement of Poverty and vulnerability in the South African context
    Journal of International Development, 2002
    Co-Authors: Mozaffar Qizilbash
    Abstract:

    It is sometimes argued that Poverty is multi-dimensional and that the borderline between the poor and the non-poor is vague. In this paper fuzzy set theoretic Poverty measures are used to examine vulnerability and 'definite Poverty' in various dimensions of the quality of life. The focus of the paper is on inter-provincial rankings in these dimensions at the time of the 1996 Census in South Africa. While various methodological difficulties are noted, the analysis suggests that the distinction between human and financial Poverty, and that between definite Poverty and extreme vulnerability, are important for policy, in particular as regards the inter-provincial distribution of funds. Copyright © 2002 John Wiley & Sons, Ltd.

Russell Davidson - One of the best experts on this subject based on the ideXlab platform.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    Econometrica, 2000
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal Poverty lines (or income censoring thresholds) up to which we may confidently assert that Poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the Measurement of Poverty is also established. The statistical results are established for deterministic or stochastic Poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    Cahiers de recherche, 1998
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal Poverty lines (or income censoring thresholds) up to which we may confidently assert that Poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the Measurement of Poverty is also established. The statistical results are established for deterministic or stochastic Poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les proprietes asymptotiques de plusieurs estimateurs frequemment utilises pour ordonner les repartitions de revenus en termes de pauvrete, bien-etre social, et inegalite. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvrete couramment en usage ainsi que les estimateurs des courbes utiles pour l'inference de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent de determiner si la pauvrete, l'inegalite ou le bien-etre social sont plus eleves dans une repartition que dans une autre pour des classes generales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvrete ou de censure des revenus jusqu'auxquels on peut affirmer sans ambiguite que la pauvrete ou le bien-etre social sont plus eleves dans une repartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvrete est aussi derivee. Les resultats statistiques s'appliquent a des seuils deterministes ou stochastiques et a des echantillons dependants ou independants. On illustre brievement nos resultats a l'aide de donnees sur 6 pays tirees des banques de donnees du Luxembourg Income Study.

  • statistical inference for stochastic dominance and for the Measurement of Poverty and inequality
    G.R.E.Q.A.M., 1998
    Co-Authors: Russell Davidson, Jeanyves Duclos
    Abstract:

    We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of Poverty, welfare and inequality. This includes estimators of most of the Poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether Poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices.

Nanak Kakwani - One of the best experts on this subject based on the ideXlab platform.

  • Statistical inference in the Measurement of Poverty
    The Review of Economics and Statistics, 1993
    Co-Authors: Nanak Kakwani
    Abstract:

    Several Poverty indices have been suggested to measure the intensity of Poverty suffered by those below the Poverty line. Because the indices are estimated on the basis of sample observations, we need to test whether the observed differences in their values are statistically significant. This paper provides distribution-free asymptotic confidence intervals and statistical inference for additive Poverty indices. The methodology developed in the paper is applied to analyze Poverty in Cote d'Ivoire from the data of the Living Standards Survey, 1985. Copyright 1993 by MIT Press.

Sara Cantillon - One of the best experts on this subject based on the ideXlab platform.

  • Implications of Income Pooling and Household Decision-making for the Measurement of Poverty and Deprivation: An Analysis of the SILC 2010 Special Module for Ireland
    2013
    Co-Authors: Dorothy Watson, Bertrand Maitre, Sara Cantillon
    Abstract:

    A core assumption in conventional Poverty Measurement is that income is shared within households to the benefit of all household members. This technical paper draws on the 2010 Irish SILC module to examine aspects of the household’s financial regime, including which household members receive income, the extent to which income is contributed for the benefit of other household members and responsibility for decision-making. The paper finds only small differences in income pooling by gender, but large differences by the person’s position in the household. In terms of decision-making, we find that most couples share responsibility for decisions. Among the findings regarding the consequences of household financial regime (with income and other characteristics controlled) were the beneficial impact of having income from work and of shared responsibility for decisions. Contrary to expectations, variations in the proportion of income contributed for the benefit of other household members did not have the anticipated impact on household and individual deprivation. The paper concludes by pointing to some implications for the Measurement of Poverty.

  • Technical Paper on implications of income pooling & household decision making for the Measurement of Poverty and deprivation - an analysis of the SILC 2010 Special Module for Ireland
    2013
    Co-Authors: Dorothy Watson, Bertrand Maitre, Sara Cantillon
    Abstract:

    A core assumption in conventional Poverty Measurement is that income is shared within households to the benefit of all household members. This technical paper draws on the 2010 Irish SILC module to examine aspects of the household’s financial regime, including which household members receive income, the extent to which income is contributed for the benefit of other household members and responsibility for decision-making. The paper finds only small differences in income pooling by gender, but large differences by the person’s position in the household. In terms of decision-making, we find that most couples share responsibility for decisions. Among the findings regarding the consequences of household financial regime (with income and other characteristics controlled) were the beneficial impact of having income from work and of shared responsibility for decisions. Contrary to expectations, variations in the proportion of income contributed for the benefit of other household members did not have the anticipated impact on household and individual deprivation. The paper concludes by pointing to some implications for the Measurement of Poverty.