Using a novel dataset covering the complete history of individual-level web traffic and digital subscriptions from a major metropolitan newspaper in the United States from 2020 to 2023, we investigate consumers' willingness to pay for different categories of news content, with particular focus on the kinds of coverage believed to generate civic externalities. Our identification relies on the quasi-random arrival of paywall events which force consumers to subscribe if they wish to continue reading. Using this variation, we estimate a model of consumer demand and construct the optimal content portfolio for the paper under different counterfactual revenue models: a fully subscription-based model and a fully ad-supported model. Our results suggest that news consumers are willing to pay for politically-relevant content, and that measures of demand based only on time-use substantially underestimate the value of hard news. Ad- and subscription-supported models lead to very different optimal production choices by the news outlet.