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.

This paper studies the supply and effects of causal rhetoric in U.S. politics. We define causal rhetoric as assigning responsibility for political outcomes, via claims of blame and merit. Training a supervised classifier, we detect causal rhetoric in over a decade of congressional tweets, finding that its supply has risen rapidly and pervasively, displacing affective messaging. We show that the production of causal rhetoric involves a trade-off between revenues and costs. First, quasi-random variation in Twitter adoption shows that blame increases small-donor revenues by expanding donor count, while merit raises average donation size. Second, fine-grained legislative data suggest that policy ownership determines relative costs: blame is cheaper for opponents, merit for proposers. Finally, causal rhetoric has downstream effects on societal outcomes, fostering protest activity and shaping polarization and institutional trust.