In the Former Soviet Union, the early 1990s were characterized by large falls in GDP and small changes to already low unemployment. The slow adjustment to unemployment was a result of employers using various means to maintain employment levels, including; extended periods of unpaid leave,...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...

Inequality measures are often used to summarise information about empirical income distributions. However , the resulting picture of the distribution and of the changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general...

This article provides a brief overview of the key issues in inequality measurement and has been prepared for inclusion in the second edition of The New Palgrave.

Building on previous studies on perceptions of inequality, welfare and risk we investigate the structure of individuals' rankings of uncertain prospects in terms of risk and their relationship to individual preferences. We examine three interlinked propositions that are fundamental to the...

Theil’s information-theoretic approach to the measurement of inequality (Theil 1967) is set in the context of subsequent developments over recent decades. It is shown that Theil’s initial insight leads naturally to a very general class of inequality measures. It is thus closely related to a...

Lorenz curves and associated tools for ranking income distributions are commonly estimated on the assumption that full, unbiased samples are available. However, it is common to find income and wealth distributions that are routinely censored or trimmed. We derive the sampling distribution for a...

Inequality measures are powerful tools of applied welfare analysis. However, to use the tools effectively one has to take into account the characteristics of the data with which one usually has to work. These raise a number of common statistical problems which are addressed here for both...

The techniques of simple random sampling are seldom appropriate in the empirical analysis of income distributions. Various types of weighting schemes are usually required either from the point of view of welfare-economic considerations (the mapping of household/family distributions into...

Statistical problems in modelling personal income distributions include estimation procedures, testing and model choice. Typically, the parameters of a given model are estimated by classical procedures such as maximum likelihood and least squares estimators. Unfortunately, the classical methods...