University of Maryland
Department of Economics
3114 Tydings Hall
College Park, MD 20742
NBER Working Papers and Publications
|May 2014||Banning Foreign Pharmacies from Sponsored Search: The Online Consumer Response|
with Matthew Chesnes, Ginger Zhe Jin: w20088
Increased competition from the Internet has raised concerns for the quality of online prescription drugs. Given the illegality of importing unapproved prescription drugs into the U.S. and the pressure from the Department of Justice, Google agreed to ban pharmacies non-certified by the National Association of Boards of Pharmacy (NABP) from sponsored search listings. We study how the ban on non-NABP-certified pharmacies from sponsored search affects consumer search on the Internet. Using click-through data from comScore, we find that non-NABP-certified pharmacies receive fewer clicks after the ban, and this effect is heterogeneous. In particular, pharmacies not certified by the NABP, but certified by other sources (other-certified sites), experience a reduction in total clicks, and some of t...
Published: Matthew Chesnes & Weijia (Daisy) Dai & Ginger Zhe Jin, 2017. "Banning Foreign Pharmacies from Sponsored Search: The Online Consumer Response," Marketing Science, vol 36(6), pages 879-907.
|November 2012||Optimal Aggregation of Consumer Ratings: An Application to Yelp.com|
with Ginger Z. Jin, Jungmin Lee, Michael Luca: w18567
Consumer review websites leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1,000 reviews). Because of this, the way in which information is aggregated is a central decision faced by consumer review websites. Given a set of reviews, what is the optimal way to construct an average rating? We offer a structural approach to answering this question, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time.
Applying this approach to restaurant reviews from Yelp.com, we construct optimal ratings for all restaurants and compare them to the arithmetic averages displayed by Yelp. Depending on how we interpret the downward trend of reviews within a r...