Estimating Inequality Measures from Quantile Data - HAL-SHS - Sciences de l'Homme et de la Société Access content directly
Preprints, Working Papers, ... Year : 2019

Estimating Inequality Measures from Quantile Data

Enora Belz
  • Function : Author
  • PersonId : 174792
  • IdHAL : enora-belz

Abstract

This article focuses on the problem of dealing with aggregate data. It proposes an innovative method for modelling Lorenz curves and estimating inequality indices on small populations, when (only) quantiles are available. When dealing with small population areas and due to privacy restrictions, individual or income share data are often not available and only quantiles are reported. The method is based on conditional expectation in order to find the different income shares and thus model a Lorenz curve with the functional forms already proposed in the literature. From this Lorenz curve, inequality indices (Gini, Pietra, Theil indices) can be derived. A simulation study is performed to evaluate this method and compare it with the other methods used. An example based on real Parisian data is presented to illustrate the method. A R package was written with all functions used in this article.
Fichier principal
Vignette du fichier
Belz_Estimating-Inequality-Measures-from-Quantile-Data_2019.pdf (2.65 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

halshs-02320110 , version 1 (18-10-2019)

Identifiers

  • HAL Id : halshs-02320110 , version 1

Cite

Enora Belz. Estimating Inequality Measures from Quantile Data. 2019. ⟨halshs-02320110⟩
162 View
1162 Download

Share

Gmail Facebook X LinkedIn More