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Growth volatility and inequality in the U.S.: A wavelet analysis

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journal contribution
posted on 28.03.2019 by Shinhye Chang, Rangan Gupta, Stephen M. Miller, Mark Wohar
This study applies wavelet coherency analysis to explore the relationship between the U.S. economic growth volatility, and income and wealth inequality measures over the period 1917 to 2015 and 1962 to 2014. We consider the relationship between output volatility during positive and negative growth scenarios. Wavelet analysis simultaneously examines the correlation and causality between two series in both the time and frequency domains. Our findings provide evidence of positive correlation between the volatility and inequality across high (short-run)- and low-frequencies (long-run). The direction of causality varies across frequencies and time. Strong evidence exists that volatilities lead inequality at low-frequencies across income inequality measures from 1917 to 1997. After 1997, however, the direction of causality changes. In the time-domain, the time-varying nature of long-run causalities implies structural changes in the two series. These findings provide a more thorough picture of the relationship between the U.S. growth volatility and inequality measures over time and frequency domains, suggesting important implications for policy makers.

History

School

  • Business and Economics

Department

  • Business

Published in

Physica A: Statistical Mechanics and its Applications

Volume

521

Pages

48 - 73

Citation

CHANG, S. ... et al, 2019. Growth volatility and inequality in the U.S.: A wavelet analysis. Physica A: Statistical Mechanics and its Applications, 521, pp.48-73.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal Physica A: Statistical Mechanics and its Applications and the definitive published version is available at https://doi.org/10.1016/j.physa.2019.01.024.

Publication date

2019-01-18

ISSN

0378-4371

Language

en

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