Full Citation

Title: Mixing it up the Case for Finite Mixture Models to Study the Distribution of Income

Citation Type: Journal Article

Publication Year: 2020

ISBN:

ISSN:

DOI: 10.1140/epjst/e2020-900130-4

NSFID:

PMCID:

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Abstract: We offer a brief review of the use of distributional mixture models with a finite number of components for the study of the distribution of income. In general, finite mixture models find a number of applications across fields, but they usually arise from theoretical considerations. Applications to the distribution of income present a joint inference about the number and types of components to include in a mixture, corresponding to how different income generating mechanisms’ statistical signatures are represented in the observed data. Many of the contributions in this area rest on an implicit (and sometimes explicit) information theoretic approach to this inference problem. Our review concludes with new illustrative findings from the US based on restricted-access Census data.

Url: https://link.springer.com/article/10.1140/epjst/e2020-900130-4

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Authors: Schneider, Markus P.A.; Scharfenaker, Ellis

Periodical (Full): The European Physical Journal Special Topics

Issue:

Volume: 229

Pages: 1685-1704

Data Collections: IPUMS CPS

Topics: Poverty and Welfare

Countries: United States