This paper exploits a randomized audit program to document how information about a corrupt politician causes electoral spillovers on his party. I focus on two types of spillovers: spillovers across types of elections (cross-electoral spillovers) and spillovers across jurisdiction borders (cross-border spillovers). Using detailed data on radio antenna location and coverage, I identify neighboring areas in the same media market. Moreover, via machine learning and text-analysis tools, I take a data-driven approach to create an index that ranks municipalities according to their corruption level. I uncover how information about corruption shapes voting decisions through the structure and geography of the media market. I show that voters hold the party of the incumbent politician accountable in four distinct ways. In municipalities where corruption occurs, voters punish parties in (1) local and (2) national elections. Most importantly, I show that news of a politician’s corruption affects his party in neighboring municipalities that share the same media market, and these spillovers affect both (3) local and (4) national elections. Ruling out other potential mechanisms, I show that these findings are consistent with electoral accountability. Finally, a back-of-the-envelope calculation suggests that an audit occurring at the local level causes the party’s vote support in the audited neighborhood (audited municipality and its neighbors) to decrease by 10 percentage points.