Directional Tests and Confidence Bounds on Economic Inequality

Jean-Marie Dufour, Emmanuel Flachaire, Lynda Khalaf and Abdallah Zalghout
Additional contact information
Jean-Marie Dufour: McGill University = Université McGill [Montréal, Canada]
Emmanuel Flachaire: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Lynda Khalaf: University of Ottawa [Ottawa]
Abdallah Zalghout: MacEwan University

Post-Print from HAL

Abstract: For standard inequality measures, distribution-free inference methods are valid under conventional assumptions that fail to hold in applications. Resulting Bahadur-Savage type failures are documented, and correction methods are provided. Proposed solutions leverage on the positive support prior that can be defended with economic data such as income, in which case directional non-parametric tests can be salvaged. Simulation analysis with generalized entropy measures allowing for heavy tails and contamination reveals that proposed lower confidence bounds provide concrete size and power improvements, particularly through bootstraps. Empirical analysis on within-country wage inequality and on world income inequality illustrates the usefulness of the proposed lower bound, as opposed to the erratic behavior of traditional upper bounds.

Date: 2025-01
References: Add references at CitEc
Citations:

Published in Econometrics and Statistics , 2025, 33, pp.230-245. ⟨10.1016/j.ecosta.2022.02.003⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05069139

DOI: 10.1016/j.ecosta.2022.02.003

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().