Analytical Prediction of Transitions Probabilities in the Conditional Logit Model

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.