Almost Dominance: Inference and Application

Abstract

This paper proposes a general framework for inference on three types of almost dominances: almost Lorenz dominance, almost inverse stochastic dominance, and almost stochastic dominance. We first generalize almost Lorenz dominance to almost upward and downward Lorenz dominances. We then provide a bootstrap inference procedure for the Lorenz dominance coefficients, which measure the degrees of almost Lorenz dominance. Furthermore, we propose almost upward and downward inverse stochastic dominances and provide inference on the inverse stochastic dominance coefficients. We also show that our results can easily be extended to almost stochastic dominance. Simulation studies demonstrate the finite sample properties of the proposed estimators and the bootstrap confidence intervals. This framework can be applied to economic analysis, particularly in the areas of social welfare, inequality, and decision making under uncertainty. As an empirical example, we apply the methods to the inequality growth in the United Kingdom and find evidence for almost upward inverse stochastic dominance.