In a political economy framework, we study how false narratives emerge in response to limitations in recipients' memory, proposing an approach based on partial identification. Coarse memory allows voters to recall policy and outcome frequencies but not their correlations. Politicians exploit this by crafting plausible narratives to inflate their policies’ effectiveness. We find that the less a policy is implemented, the more optimistic the narrative can be. In a competition model, opposing narratives become polarized, fostering political cycles where office tenure is independent of policy quality. Our mechanisms are consistent with an analysis of U.S. Congress members' rhetorical strategies.

We propose a supervised method to detect causal attribution in political texts, distinguishing between expressions of merit and blame. Analyzing four million tweets shared by U.S. Congress members from 2012 to 2023, we document a pronounced shift toward causal attribution following the 2016 presidential election. The shift reflects changes in rhetorical strategy rather than compositional variation in the actors or topics of the political debate. Within causal communication, a trade-off emerges between positive and negative tone, with power status as the key determinant: government emphasizes merit, while opposition casts blame. This pattern distinguishes causal from purely affective communication. Additionally, we find that blame is associated with lower trust in politicians, perceived government effectiveness, and spreads more virally than merit.