Essays on reputation in online marketplaces

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dc.contributor.advisor Fabra, Natalia
dc.contributor.advisor Machado, Matilde P.
dc.contributor.author Rossi, Michelangelo
dc.date.accessioned 2020-10-09T12:15:06Z
dc.date.available 2020-10-09T12:15:06Z
dc.date.issued 2020-07
dc.date.submitted 2020-09-17
dc.identifier.uri http://hdl.handle.net/10016/31176
dc.description.abstract Would you ever rent a house you have never seen; whose owner you have never met; in a city you have never visited? And again: would you ever pay upfront to a person you do not know, and you will never meet, who promise to deliver an object you have never seen? A few decades ago the answers to these questions would have been negative for most customers. Conversely, nowadays millions of users rely on digital platforms, such as Airbnb and eBay, to get services with the same characteristics as those described by the previous two questions. How was that possible? How did users around the world start to trust each other after millennia of skepticism and malevolence? An answer to such questions relies on the innovative way digital marketplaces use to reduce the asymmetry of information between parties: review systems. In almost all digital platforms, users can review the services they have experienced providing new pieces of information to prospective users. Accordingly, reviews reduce the uncertainty about sellers' quality since each feedback increases the precision of buyers' estimates. Besides, reviews also discipline sellers' ongoing behavior with the potential punishment of negative feedback. Still, signaling quality and monitoring sellers' behavior are two separate tasks. From a microeconomic perspective, reviews reduce adverse selection effects by signaling sellers' quality, whereas monitoring behavior affects moral hazard issues. In this dissertation I study the power, and the limits, of review systems to reduce these two types of asymmetry of information: adverse selection and moral hazard. In the two chapters of this thesis, I examine both signaling and monitoring tasks.1 In the first chapter, How Does Competition Affect Reputation Concerns? Theory and Evidence from Airbnb, I show how changes in the number of close competitors affect the power of reputation to induce sellers to exert effort. The impact of competition on sellers' incentives is theoretically ambiguous. More competition disciplines sellers, but, at the same time, it erodes reputational premia. This paper identifies empirically whether one effect dominates the other using data from Airbnb. To guide the empirical analysis, I develop a model of reputation with frictional matching between the two sides of the market. Here the relative number of hosts and guests affects the value of building a reputation through effort. In this specific framework, more competition depresses hosts' profits and leads hosts to reduce effort. I test the model's prediction exploiting a change in regulation for Airbnb listings effective in San Francisco in 2017. I identify a negative causal effect of competition on ratings about hosts' effort. These findings suggest that more competition may erode incentives for high-quality services in frictional marketplaces where sellers' performances depend on reputation. In the second chapter, Quality Disclosures and Disappointment: Evidence from the Academy Awards, I study the impact of quality disclosures on buyers' rating behavior using data from an online recommender system. Disclosures may alter expectations on sellers' quality and a ect buyers' rating behavior. In particular, if buyers' utility depends on a reference point induced by their expectations, a positive disclosure of quality such as an award may lead to buyers' disappointment and it negatively in uences their ratings. I identify the disappointment effect in moviegoers' ratings originated from the rise in expectations due to movies' nominations for the Academy of Motion Picture Arts and Sciences awards. I control for the selection of moviegoers who watch and rate movies before or after nominations with a non-parametric matching technique. After nominations, ratings for nominated movies significantly drop relative to ratings for movies that were not nominated. This short-term disappointment effect reduces the rating premium of nominated movies by more than five percent. 1. reviewed the economic literature regarding the role of review systems in digital platforms in Asymmetric Information and Review Systems: The Challenge of Digital Platforms published in 2018 as a chapter of the book Economic Analysis of the Digital Revolution (edited by Prof. Juan Jos e Ganuza and Prof. Gerard Llobet).
dc.language.iso eng
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Essays on reputation in online marketplaces
dc.type doctoralThesis
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
dc.description.degree Programa de Doctorado en Economía por la Universidad Carlos III de Madrid
dc.description.responsability Presidente: Juan José Ganuza.- Secretario: Alan Richard Crawford.- Vocal: Chiara Farronato
dc.contributor.departamento Universidad Carlos III de Madrid. Departamento de Economía
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