Earnings distributions of scalable vs. non-scalable occupations

Adriano Maia, Raul Matsushita and Sergio Da Silva

Physica A: Statistical Mechanics and its Applications, 2020, vol. 560, issue C

Abstract: It has been suggested that occupations where one is paid by the hour are not scalable, while scalable occupations allow one to make more money without an equivalent increase in labor and time. Non-scalable occupations are expected to have low income variance, whereas scalable ones show large income inequalities. This study examines the evidence for this suggested categorizing using personal earnings microdata for twelve candidate occupations of both types, scalable and not. We find the upper tails of all distributions decay as power laws. Moreover, we cannot reject the suggested categorizing for earnings above medians.

Keywords: Earnings distribution; Income distribution; Scalable occupations; Pareto distribution; Power laws; Generalized Pareto curve (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:560:y:2020:i:c:s037843712030621x

DOI: 10.1016/j.physa.2020.125192

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