Since eBay sold its first item, a broken laser pointer, in 1995, the online marketplace has hosted sellers of all stripes, and that’s not always been good for business. Not only do buyers who have a bad experience tend to leave eBay for good, according to Chicago Booth’s Chris Nosko and Berkeley’s Steven Tadelis, they also fail to report the problem. The result is that second-rate sellers’ reputations go unharmed, and more unsuspecting consumers are disappointed.

Nosko and Tadelis’s research suggests that if eBay better screened for high-quality sellers and used search-result rankings to give those sellers’ products top billing, the platform could improve its services for buyers and sellers alike.

It’s in eBay’s interest, as a commission-collecting marketplace, to make sure its good sellers get business and its bad sellers get the boot. But its seller profiles, which potential buyers can use to vet a seller, don’t reflect the negative experiences that go unreported. It takes more effort for buyers to leave negative feedback than to leave nothing at all, and at least one buyer’s negative feedback has resulted in a lawsuit from the seller—factors that could discourage unhappy buyers from posting. It is the resulting silence that Nosko and Tadelis try to account for with a new measure of seller reputation that they call the “EPP.”

A seller’s profile on eBay displays a “percent positive” measure for that seller, computed as the ratio of the number of transactions with positive feedback from buyers to the number of transactions with any feedback at all. To more accurately reflect a seller’s true reputation, Nosko and Tadelis constructed the “effective percent positive,” or EPP, which is the ratio of the number of positive feedback transactions to total transactions. This measure takes into account the share of transactions for a seller where no feedback exists.

One can imagine two sellers, A and B, both receiving one negative comment and 99 positive comments. Both have a percent positive measure of 99 percent. But Seller A has 120 transactions and Seller B has 150 transactions, with Seller A having 20 “silent” transactions where buyers provide no feedback at all and Seller B having 50 silent transactions. Seller A resultantly has an EPP of about 83 percent and Seller B has an EPP of 66 percent.

Nosko and Tadelis ran an experiment in which they manipulated eBay’s search-ranking algorithm such that one set of participants was exposed to high-EPP sellers while a control group of buyers was not, meaning that buyers in the treatment condition were more likely to see search results from high-quality sellers.

The experiment reveals that buyers in the treatment condition were significantly more likely to purchase on eBay in the future than those in the control group. Based on their results, the researchers suggest platforms such as eBay should consider ranking search results so that products from high-quality sellers are prominently displayed.

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