A Joint Top Income and Wealth Distribution

Konferenzbeitrag

Top distributions of income and wealth are still incompletely measured in many national statistics, particularly when using survey data. This paper develops the technique of incorporating the joint distributional relationship to enhance the estimation of these two top distributions. We leverage the bivariate parametric/non-parametric copula to extrapolate both income and wealth distributions from German PHF (Panel on Household Finance) data. The copula modelling potentially reduces the ad hocery in choosing the estimation domain as well as in the parametric specification (eg Pareto family) imposed by almost all the marginal approaches. One distinct feature of our paper is to complement the model fit with external validation. The copula estimate can help us to perform out-of-sample prediction on the very top of the tail distribution from one margin conditional on the characteristics of the other. The validation exercises show that our copula-based approach can approximate much closer to the top tax data and wealth "rich list" than those unconditional marginal extrapolations. The properness of copula and conditioning criterion seems to convince the asymmetric joint association between (labor) income and wealth (capital income) distributions as recently evidenced by other countries.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2020: Gender Economics

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Evaluation, Validation, and Selection ; Specific Distributions; Specific Statistics ; Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access ; Personal Income, Wealth, and Their Distributions
• Thema: income and wealth joint distribution ; copula ; heavy-tailed distributions ; external consistency

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Zhu, Junyi ; Steiner, Viktor
• Ereignis: Veröffentlichung
• (wer): ZBW - Leibniz Information Centre for Economics
• (wo): Kiel, Hamburg
• (wann): 2020

Weitere Informationen

• Handle: http://hdl.handle.net/10419/224651
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ