LAUSR

Because of their applicability for ordering distributions within general classes of utility and social welfare functions, sampling theory tests for stochastic and Lorenz dominance have attracted considerable attention in the literature. We contribute to this literature by proposing a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo (MCMC), we compute posterior probabilities for (i) X dominates Y, (ii) Y dominates X, and (iii) neither Y nor X is dominant by counting the proportions of MCMC draws that satisfy the constraints implied by each of the alternatives. We apply the proposed approach to samples of Indonesian income distributions for 1999, 2002, 2005 and 2008. To ensure flexible modelling of the distributions, mixtures of gamma densities are fitted for each of the years. We introduce probability curves that depict the probability of dominance at each population proportion and which convey valuable information about dominance probabilities for restricted population proportions relevant when studying poverty orderings. The dominance probabilities are compared with p-values from some sampling theory tests; the probability curves are used to gain insights into seemingly contradictory outcomes