Melons as Lemons: Asymmetric Information, Consumer Learning and Quality Provision

There is often a lack of reliable quality provision in many markets in developing countries. I designed an experiment to understand this phenomenon in a setting that features typical market conditions in developing countries: the retail watermelon markets in a major Chinese city. I first demonstrate empirically that there is substantial asymmetric information between sellers and buyers on sweetness, the key indicator of quality, yet sellers do not di↵erentiate and price watermelons by quality. I then randomly introduce one of two branding technologies into 40 out of 60 markets—one sticker label that is widely used and counterfeited and one novel lasercut label. I track sellers’ quality, pricing and sales over an entire season and collect household panel purchasing data to examine the demand side’s response. I find that laser-branding induced sellers to provide higher quality and led to higher sales profits. However, after the intervention was withdrawn, all markets reverted back to baseline. To rationalize the experimental findings, I build an empirical model of consumer learning and seller quality provision. The results suggest that consumers are hesitant to upgrade their perception under stickers, which makes quality provision a low-return investment. While the new technology enhances learning, the resulting increase in profits is not su cient to cover the fixed cost of the technology for small individual sellers. Counterfactual analysis shows that information frictions and fragmented markets lead to significant under-provision of quality. Third-party interventions that subsidizing the initial demand and learning process or entry of large firms could improve welfare. JEL Classifications: D22, D61, D82, D83, L11, L14, L15, O12, O14, O31 Total Word Count: About 12,500 ⇤The paper was previously circulated under the title “Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation. ” I am deeply grateful to Benjamin Olken, Abhijit Banerjee, Robert Townsend and Nikhil Agarwal for their continuous encouragement and guidance over the course of this project. I thank David Atkin, Esther Duflo, Marcel Fafchamps, Hanming Fang, Glenn Ellison, Robert Gibbons, German Sergio Gieczewski, Seema Jayachandran, Yan Ji, Siyuan Liu, Rocco Macchiavello, Yuhei Miyauchi, Xiaosheng Mu, Ariel Pakes, Frank Schilbach, Peng Shi, Maheshwor Shrestha, Paulo Somaini, Tavneet Suri, Jean Tirole, Juuso Toikka, Richard Zeckhauser, Juanjuan Zhang, Hongkai Zhang, and seminar participants at NEUDC, AFE, Berkeley, Chicago (Economics/Booth), CICER, Columbia, Cornell, GWU, Harvard Economics, Harvard Kennedy School, Microsoft Research, MIT Sloan Applied Economics, MIT Sloan Marketing, Northwestern, NYU Stern, PKU, PSU, SITE, Stanford GSB, UMich, UPenn, Yale SOM Marketing, and the development and IO lunches at MIT for helpful discussions and feedback. Funding for this project was generously provided by the Weiss Family Fund, the Abdul Latif Jameel Poverty Action Lab, and the George and Obie Shultz Fund. All errors are my own. †Address: 79 JFK Street, Cambridge, MA 02142. Phone: 617-6157771. E-mail: jie bai@hks.harvard. edu.