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Econometrics 2018, 6(2), 30; https://doi.org/10.3390/econometrics6020030

Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data

1
Department of Economics, Ewha Womans University, Seoul 03760, Korea
2
The World Bank, Washington, DC 20433, USA
*
Author to whom correspondence should be addressed.
Received: 1 January 2018 / Revised: 23 May 2018 / Accepted: 23 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Abstract

It is sometimes observed and frequently assumed that top incomes in household surveys worldwide are poorly measured and that this problem biases the measurement of income inequality. This paper tests this assumption and compares the performance of reweighting and replacing methods designed to correct inequality measures for top-income biases generated by data issues such as unit or item non-response. Results for the European Union’s Statistics on Income and Living Conditions survey indicate that survey response probabilities are negatively associated with income and bias the measurement of inequality downward. Correcting for this bias with reweighting, the Gini coefficient for Europe is revised upwards by 3.7 percentage points. Similar results are reached with replacing of top incomes using values from the Pareto distribution when the cut point for the analysis is below the 95th percentile. For higher cut points, results with replacing are inconsistent suggesting that popular parametric distributions do not mimic real data well at the very top of the income distribution. View Full-Text
Keywords: top incomes; inequality measures; survey non-response; Pareto distribution; parametric estimation; EU SILC top incomes; inequality measures; survey non-response; Pareto distribution; parametric estimation; EU SILC
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Hlasny, V.; Verme, P. Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data. Econometrics 2018, 6, 30.

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