Statistical Inference for Generalized Lorenz Dominance Based on Grouped Data: a Reconsideration
نویسندگان
چکیده
One income distribution is preferable to another under any increasing and Schur-concave (S-concave) social welfare function if and only if the generalized Lorenz (GL) curve of the first distribution lies above that of the second (GL dominance). This paper (i) derives the asymptotic distribution of a vector of sample GL curve ordinates, interpreting it as a method-of-moments estimator, and (ii) proposes a simple simulation-based test for GL dominance (and for multiple inequality restrictions in general) that is consistent and asymptotically has the correct size. The paper also provides detailed Monte Carlo experiments and an application to income distributions in Japan.
منابع مشابه
DISCUSSION PAPERS IN STATISTICS AND ECONOMETRICS SEMINAR OF ECONOMIC AND SOCIAL STATISTICS UNIVERSITY OF COLOGNE No. 3/00 Statistical Inference for Tail Behaviour of Lorenz Curves
The appeal of the Lorenz dominance criterion is undermined by the fact that many sample Lorenz curves intersect in the tails. Tests for Lorenz dominance which ignore tails (such as those considering only deciles) are therefore invalidated. Moreover, the usual inferential methods, based on central limit theorem arguments, do not apply to the tails of the Lorenz curve since the tails contain too ...
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