The paper derives analytical transitions probabilities following an exogenous shock to the
deterministic component in the conditional logit model. The solution draws on the postestimation
distribution of the model’s stochastic component, identified on the basis of a direct
utility maximization interpretation of agents’ revealed choice. Computational experiments
confirm that analytical prediction of transitions probabilities might perform substantially better
than the established calibration method with few repetitions. However, results obtained in an
empirical application studying labor supply responses to social insurance reform in Germany
suggest that previous calibration-based results accurately indicate the direction of incentive
effects, while underpredicting small transitions frequencies.