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 better. For the bivariate case, we develop the theoretical basis for an algorithmic dominance test that is easy to implement