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Causality between Per Capita Real GDP and Income Inequality in the U.S.: Evidence from a Wavelet Analysis

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  • Shinhye Chang

    (University of Pretoria)

  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Nevada, Las Vegas and University of Connecticut)

Abstract

This study applies wavelet coherency analysis to examine the relationship between the U.S. per capita real GDP and six income inequality measures over the period 1917 to 2012. Wavelet analysis allows the simultaneous examination of correlation and causality between the two series in both the time and frequency domains. Our findings provide robust evidence of positive correlation between the growth and inequality across frequencies. Yet, directions of causality vary across frequencies and evolve with time. 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. per capita real GDP and inequality measures over time and frequency, suggesting important implications for policy makers.

Suggested Citation

  • Shinhye Chang & Rangan Gupta & Stephen M. Miller, 2016. "Causality between Per Capita Real GDP and Income Inequality in the U.S.: Evidence from a Wavelet Analysis," Working papers 2016-14, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2016-14
    Note: Stephen Miller is the corresponding author
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    More about this item

    Keywords

    Income; Inequality; Wavelet analysis; U.S.;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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