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Top Incomes, Heavy Tails, and Rank-Size Regressions

Abstract : In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated), which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK.
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https://hal-amu.archives-ouvertes.fr/hal-01978497
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Submitted on : Friday, January 11, 2019 - 3:24:56 PM
Last modification on : Saturday, February 19, 2022 - 6:06:53 PM

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Christian Schluter. Top Incomes, Heavy Tails, and Rank-Size Regressions. Econometrics, MDPI, 2018, 6 (1), pp.10. ⟨10.3390/econometrics6010010⟩. ⟨hal-01978497⟩

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