What do news readers want?

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 between 2020 and 2024, 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 strategy 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 staff allocation for the paper under different counterfactual revenue models: a fully subscription-based model and a fully ad-supported model. Our results suggest that readers are willing to pay for local reporting, and that measures of demand based only on time-use substantially underestimate the value of “hard” news coverage on topics like local politics and public health. However, digital subscription revenues alone are insufficient to cover staff costs even at the highest revenue-generating sections of the paper. We use our model to estimate the subsidy required to expand the newspaper's production of investigative coverage.