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 nonresponse. 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.
Highlights
Thanks to the wide public attention that top incomes have received in the aftermath of the global financial crisis, it is acknowledged that top incomes have grown disproportionally faster than other incomes in industrialized countries over the past several decades
Right truncation affects the measurement systematically. This should not be surprising, because right-truncation in this exercise affects the estimation of the Pareto distribution, and the extent of replacing observed top incomes with values drawn from the national parametric distributions
When 5–8 percent of observed top incomes are replaced, the Gini rises by 0.39 to 4.36 percentage points, suggesting that in this group the observed incomes typically underrepresent the incomes in the population due to unit non-response and other biases
Summary
Thanks to the wide public attention that top incomes have received in the aftermath of the global financial crisis, it is acknowledged that top incomes have grown disproportionally faster than other incomes in industrialized countries over the past several decades. The fact that these top incomes are difficult to capture in household surveys potentially leads to biases in the estimation of income inequality related to the representation and precision of reported top incomes, even though the direction of the bias is not a priori clear Income measurement issues including surveying, interview methods and post-survey treatment explain differences in inequality measurements across data sources (Frick and Krell 2010)
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