Tails of Lorenz curves

Schluter, Christian; Trede, Mark

doi:10.1016/s0304-4076(01)00145-2

Cited by 29 publications

(31 citation statements)

References 15 publications

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“…In the presence of data contamination in high incomes, we suggest computing inequality measures based on a semiparametric estimation of the income distribution: using the EDF for all but the righthand tail and a parametric estimation for the upper tail. Many parametric income distributions are heavy-tailed: this is so for the Pareto, Singh-Maddala, Dagum and Generalised Beta distributions, see Schluter and Trede (2002). This means that the upper tail decays as a power function:…”

Section: Semiparametric Inequality Measuresmentioning

confidence: 99%

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Income distribution and inequality measurement: The problem of extreme values

Cowell

Flachaire

2007

Journal of Econometrics

182

188

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International audienceWe examine the statistical performance of inequality indices in the presence of extreme values in the data and show that these indices are very sensitive to the properties of the income distribution. Estimation and inference can be dramatically affected, especially when the tail of the income distribution is heavy, even when standard bootstrap methods are employed. However, use of appropriate semiparametric methods for modelling the upper tail can greatly improve the performance of even those inequality indices that are normally considered particularly sensitive to extreme values

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Section: Semiparametric Inequality Measuresmentioning

confidence: 99%

“…For instance, Schluter and Trede (2002) show that the Singh-Maddala distribution is of Pareto type for large y, with the index of stability equals to y ¼ bc. In our simulations, we have bc ¼ 4:76, see (1).…”

Section: Article In Pressmentioning

confidence: 99%

Income distribution and inequality measurement: The problem of extreme values

Cowell

Flachaire

2007

Journal of Econometrics

182

188

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“…All three distributions are skewed to the right, but di¤er in other ways, such as their tail behavior. Schluter and Trede (2002), for instance, show that the right tail of the generalized beta distribution can be written as 1 F (x; a; b; c;…”

Section: Ge Indices and Income Distributionsmentioning

confidence: 99%

Edgeworth expansions and normalizing transforms for inequality measures

Schluter

Garderen

2009

Journal of Econometrics

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Finite sample distributions of studentized inequality measures di¤er substantially from their asymptotic normal distribution in terms of location and skewness. We study these aspects formally by deriving the second order expansion of the …rst and third cumulant of the studentized inequality measure. We state distribution-free expressions for the bias and skewness coe¢ cients. In the second part we improve over …rst-order theory by deriving Edgeworth expansions and normalizing transforms. These normalizing transforms are designed to eliminate the second order term in the distributional expansion of the studentized transform and converge to the Gaussian limit at rate O(n 1 ). This leads to improved con…dence intervals and applying a subsequent bootstrap leads to a further improvement to order O(n 3=2 ). We illustrate our procedure with an application to regional inequality measurement in Côte d'Ivoire.

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“…For an alternative approach focusing on crossings in the tails of Lorenz curves see Schluter and Trede (2002) and for a Bayesian approach see Hasegawa and Kozumi (2003). On the extension to absolute dominance and deprivation dominance see Bishop et al (1988), Xu and Osberg (1998) and on poverty dominance see also Chen andDuclos (2008), Thuysbaert (2008).…”

Section: Dominance: An Intuitive Applicationmentioning

confidence: 99%