Thanks! I agree: price increases are not necessarily welfare decreasing (especially for sellers). Moreover, it is hard to measure the reduction of guests’ search costs with more transparency… so any welfare analysis is very complex in this framework

Curious to learn more?☝️

Check out the full paper: papers.ssrn.com/sol3/papers....

We uncover a fascinating mechanism linking transparency to pricing strategies in digital markets. Feedback and thoughts are welcome!

Policy implications:

Price transparency isn’t universally good or bad. While it reduces search costs and obfuscation, it can also lead to price increases in some cases. Regulation needs to consider both demand-side and supply-side effects carefully.

The core insight: transparency changes how hosts (not just guests) look at prices. When rivals’ total prices are clearer, hosts adjust their strategies. This is especially impactful in peer-to-peer platforms where pricing frictions exist and some hosts and guests might be naive or have search costs.

Key results:

• Listings with high cleaning fees reduced them by 2-4% post-policy.⬇️

• But listings without cleaning fees raised their nightly prices by 5-6%.⬆️

Why? Greater transparency let some hosts realize their prices were too low, prompting increases.

Airbnb hosts set nightly prices and cleaning fees. Before 2019, EU guests only saw cleaning fees at checkout. After a regulatory push, Airbnb made these fees visible upfront. Using a difference-in-differences approach, we studied the impact on prices and fees.

Thanks a lot, Brett!

Here is the link: pubsonline.informs.org/doi/10.1287/... (15/15)

📚 Overall, our work sheds new light on how consumer heterogeneity shapes online ratings and offers practical solutions to improve rating systems. We’re excited to see it published in Management Science! (14/15)
#Ecommerce #Ratings #ConsumerBehavior #ManagementScience #DataScience #IMDb #MovieLens

📊 Our findings have important implications for platform design. By understanding these biases and applying corrections, platforms can deliver more reliable ratings, benefiting consumers and (high-quality) producers alike. (13/15)

💡 Conversely, simply overweighting the ratings of experienced users, a common practice on several platforms, can actually backfire, further penalizing high-quality movies. (12/15)

Yes! Once debiasing the ratings, this movie’s rating goes up! In particular, this movie is one of the biggest “winners” of our debiasing procedure. (11/15)

🎞️ A notable case of this bias? The French movie "The Unknown Girl", selected for the Palme d’Or Cannes in 2016, is rated 6.5 on IMDb. That’s relatively low… but is it due to the fact that most of the raters were experienced, stringent users? (10/15)

After applying it, the corrected ratings better align with external measures of quality, such as the Oscars and Metacritic scores. It also helped fix those ranking reversals! (9/15)

🔄 However pervasive, this bias can be undone. We developed an algorithm to de-bias ratings by adjusting for user stringency. Our approach doesn’t require us to take a stance on users’ expertise. Rather, we let ratings and individual stringencies iterate until they converge. (8/15)