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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Modeling Household Income with Contaminated Unimodal Distributions

verfasst von : Angelo Mazza, Antonio Punzo

Erschienen in: New Statistical Developments in Data Science

Verlag: Springer International Publishing

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Abstract

In many countries, income inequality has reached its highest level over the past half century. In the labor market, the technological progress has widened the earnings gap between high- and low-skilled workers. Changes in the structure of households, with a growing percentage of single-headed households, and in family formation, with an increased earnings correlation among partners in couples, is contributing in increasing inequality. A key step in measuring income inequality is the estimation of the income distribution, due to the sensitivity of usual inequality measures to extreme values. To deal with this issue, we propose the use of contaminated lognormal and gamma models and we derive the formulations for computing the Gini index based on the model parameters. An application to 101 empirical income distributions that include countries at different development stages is presented.

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Vorheriges Kapitel What Do Employers Look for When Hiring New Graduates? Answers from the Electus Survey
Nächstes Kapitel Endowments and Rewards in the Labour Market: Their Role in Changing Wage Inequality in Europe
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Metadaten
Titel
Modeling Household Income with Contaminated Unimodal Distributions
verfasst von
Angelo Mazza
Antonio Punzo
Copyright-Jahr
2019
Buch
New Statistical Developments in Data Science

Print ISBN: 978-3-030-21157-8

Electronic ISBN: 978-3-030-21158-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-21158-5_28