Among the goals of statistical matching, a very important one is the estimation of the joint distribution of variables not jointly observed in a sample survey but separately available from independent sample surveys. The absence of joint information on the variables of interest leads to uncertainty about the data generating model. The present article reviews the concept of uncertainty in statistical matching and how to measure it by providing a unified framework for the parametric and nonparametric setting. Furthermore, the reduction of uncertainty due to the introduction of logical constraints is investigated and a simulation experiment is performed.