This paper introduces a concept of inequality comparisons with ordinal bivariate categorical data. In our model, one population is more unequal than another when they have common arithmetic median outcomes and the first can be obtained from the second by correlation-increasing switches and/or...

We explore a novel first order dominance (FOD) approach to poverty mapping and compare its properties to small area estimation. The FOD approach uses census data directly; is straightforward to implement; is multidimensional allowing for a broad conception of welfare; and accounts rigorously for...

This paper proposes a concept of inequality comparisons with ordinal multidimensional categorical data. In our model, one population is more unequal than another when they have common arithmetic median outcomes and the first can be obtained from the second by correlation-increasing switches...

This paper develops an ordinal method of comparison of multidimensional inequality. In our model, population distribution g is more unequal than f when the distributions have common median and can be obtained from f by one or more shifts in population density that increase inequality. For our...

This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution f first order stochastic dominates distribution g if and only if f can be obtained from g by iteratively shifting density from one outcome to another that is...

We explore a novel first order dominance (FOD) approach to poverty mapping and compare its properties to small area estimation. The FOD approach uses census data directly; is straightforward to implement; is multidimensional allowing for a broad conception of welfare; and accounts rigorously for...