This paper delivers new insights into the development of income inequality and regional stratification in Germany after unification using a new method for detecting social stratification by a decomposition of the GINI index which yields the obligatory between- and within-group components as well...

This paper is concerned with the problem of ranking Lorenz curves in situations where the Lorenz curves intersect and no unambiguous ranking can be attained without introducing weaker ranking criteria than first-degree Lorenz dominance. To deal with such situations two alternative sequences of...

We offer an explanation for the inconclusive results of empirical studies into the relationship between the magnitude of the Gini coefficient of income distribution at origin and the intensity of migration. Bearing in mind the substantial literature that identifies relative deprivation as an...

Household survey data provide a rich information set on income, household context and demographic variables, but tend to under report incomes at the very top of the distribution. Administrative data like tax records offer more precise information on top incomes, but at the expense of household...

Probit and logit models typically require a normalization on the error variance for model identification. This paper shows that in the context of sample mean probability decompositions, error variance normalizations preclude estimation of the effects of group differences in the latent variable...

This paper proposes a comprehensive, path-independent decomposition formula of changes into ceteris paribus effects and interaction effects. The formula implies a reassessment of sequential decomposition methods that are widely used in the literature and that are restrictive in how they treat...

This paper proposes a decomposition of the composition effect, i.e. the part of the observed between-group difference in the distribution of some economic outcome that can be explained by differences in the distribution of covariates. Our decomposition contains three types of components: (i) the...

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naïve regression that includes a group indicator variable. Our analysis yields two principal findings....