Title: The Anatomy of Online Reviews: Evidence of Self-Selection from the Steam Store.

Abstract How good are reviews as signals of product quality for consumers? Using a data-set derived from the popular `Steam' gaming platform I investigate the self-selection of reviewers. A policy reform on Steam in 2019 both lowered the transaction cost of reviewing, with this randomly occurring within a game and reviewer's life cycle. I find that the new individuals elicited to review by the policy change are 4\% more likely to rate any game positively, leave 15\% shorter reviews and are less experienced both within and across games. This selection is heterogeneous across games, greatly affecting their rank order and while some sellers benefitted from the increase in selection, others did not. Overall, the policy reduced selection bias and improved the consumer's information set, but new reviewers were rated as less helpful by their peers, implying that more accurate review scores come at the cost of less helpful reviews.