Though the impact of file-sharing of copyrighted content has been discussed for over a decade, only in the past few years have countries begun to adopt legislation to criminalize this behavior.These laws impose penalties ranging from warnings and monetary fines to disconnecting Internet service. While their supporters are quick to point out trends showing the efficacy of these laws at reducing use of file-sharing sites, their analyses rely on brief snapshots of activity that cannot reveal long- and short-term trends. In this paper, we introduce an approach to model user behavior based on a hidden Markov model and apply it to analyze a two- year-long user-level trace of download activity of over 38k users from around the world. This approach allows us to quantify the true impact of file-sharing laws on user behavior, identifying behavioral trends otherwise difficult to identify. For instance, despite an initial reduction in activity in New Zealand when a three-strikes law took effect, after two months activity had returned to the level observed prior to the law being enacted. Given that punishment seems to, at best, result in short-term compliance, we suggest that incentives-based approaches may be more effective at changing user behavior.