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...

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density...

By using nonparametric methods, this paper estimates the distribution of both household and size-adjusted real income in Italy between 1987–1998. Because of data sparseness in the distribution, an adaptive bandwidth is used, while to account for sample design a weighting variable is...

Using a newly available comprehensive micro-data set we examine changes in the shape of the Brazilian income distribution during the "lost decade" of the 1980s. We adopt alternative parametric and non-parametric approaches to modelling the distribution. We show that inequality changed...

Rising poverty and inequality increases the risk of social instability in countries all around the world. For measuring poverty and inequality there exists a variety of statistical indicators. Estimating these indicators is trivial as long as the income variable is measured on a metric scale....

Using kernel density estimation we find that over their 1990s business cycles the entire distribution of after-tax (disposable) income moved to the right in the United States and Great Britain while inequality declined. In contrast, Germany and Japan experienced less growth, a rise in inequality...