Machine learning classifiers have become a household tool for banks, companies, and government institutes for automated decision-making. In order to help explain why a person was classified a certain way, a solution was proposed that could generate these counterfactual explanations. Several generators have been introduced and tested but include several side effects. One of these side effects is making it easier to be classified incorrectly after sufficient recourse has been applied. Dynamics, a.k.a. shifts in both the domain and model, cause these side effects. We aimed to quantify these dynamics induced by two generators, Wachter et al. and REVISE, and compare them against each other. We performed three experiments with both generators and looked at the effect a different dataset, model, or hyper-parameter may have had on the dynamics. We found that REVISE induces a slight model shift while the domain shifts increase with each round of recourse.