Replication Data for: Modeling Issue Competence Over Time: A Bayesian Framework for Estimating Dynamic Issue Ownership

Recent years have witnessed a tremendous interest in issue ownership and in the dynamics of issue ownership in particular. While new insights have been gained, our progress is stifled by two factors. One, research on issue ownership is typically subject to data sparsity, which is particularly dire in longitudinal analyses and which has often restricted analyses to few issues. Two, existing research has mostly studied issue ownership by means of simple percentages, which are prone to random sampling error, thus disregarding the inevitable uncertainty in estimating public attributions of issue ownership. To overcome both shortcomings, we propose a Bayesian multilevel categorical model. Analyzing multiple issues in a single model allows inferences on ownership structures with regard to minor issues. Moreover, the model can be flexibly specified to recover dynamic issue ownership or estimates using other grouping factors, such as subnational ascriptions of party competence. The model is applied in a case study with data from the German Longitudinal Election Study, which yields plausible estimates of German parties’ issue competences. Substantively, our model shows that parties’ issue competences display some level of malleability, but that changes unfold gradually over time.