Removing Sludge – Does Lowering Barriers To Rate Improve The Informativeness of Crowd Consensus?

Abstract

We investigate how lowering barriers to rate affects the informativeness of the rating consensus – a crowd-sourced performance measure. In 2020, Amazon.com introduced a new one-tap rating system for verified users, whereas before, a written text review was required to rate a product. We document that the policy change solicits substantially more ratings from platform users. The rating averages increase across all product categories.
Moreover, we find evidence consistent with authentic ratings crowding out purchased fake ratings and rendering rating manipulation conducted by sellers less effective than before. Overall, ratings on Amazon seem to become more informative, as evidenced by higher sensitivity of future sales to ratings, and by fewer customer complaints about misleading ratings. Taken together, this paper provides the first field evidence that the removal of “sludge,” which imposes cognitive costs on actors’ decision-making, can encourage their prosocial activities and improve the surplus of a crowd-based reputation system.

Keywords: Wisdom of crowds, sludge, non-financial information, informativeness, rating management, Amazon.com

JEL Classification: D20, D82, M10, M31, M41