Inference about productivity change over time based on data envelopment (DEA) has focused primarily on the Malmquist index and is based on asymptotic properties of the index. In this paper we propose a novel set of significance tests for DEA based productivity change measures based on permutations and accounting for the inherent correlations when panel data are observed. The tests are easily implementable and give exact significance probabilities as they are not based on asymptotic properties. Tests are formulated both for the geometric means of the Malmquist index, and also of its components, i.e. the frontier shift index and the eciency change index, which together enable analysis of not only the presence of differences, but also gives an indication of whether the productivity change is due to shifts in the frontiers and/or changes in the efficiency distributions. Simulation results show the power of, and suggest how to interpret the results of, the proposed tests. Finally, the tests are illustrated using a data set from the literature.