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dc.contributor.advisorChairperson, Graduate Committee: Mark Andersonen
dc.contributor.authorSears, James Matthewen
dc.coverage.spatialDrugs.en
dc.date.accessioned2017-10-09T22:21:11Z
dc.date.available2017-10-09T22:21:11Z
dc.date.issued2016en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/13794en
dc.description.abstractDespite complete user anonymity, asymmetrical information, and incomplete enforcement mechanisms, the deep web market Silk Road facilitated approximately $200 million in illegal drug sales in 34 months. This study tests how the site's reputation system facilitated successful transactions and how user feedback functioned as the primary signal of seller quality in the absence of formal contract enforcement. Using novel data from the site on marijuana, amphetamine, and meth transactions, listings, and vendors, I find strong evidence that consumers engaged with the site's reputation system and relied on both item and seller-level information. Hedonic regressions provide evidence of a 'bad news' learning environment, estimating a 3 to 11% price discount for negative item reviews. Seller ratings are found to act as an effective proxy for permanent seller characteristics, and named trains act as a primary source of quality (and price) differentiation for marijuana. I find no evidence of price penalties or signal heterogeneity for new sellers. This study is the first to shed light on the value of reputation on the deep web's largest marketplace, yielding new insight into the mechanisms modern markets use to overcome social distance and prevent market failure.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.subject.lcshReputationen
dc.subject.lcshInterneten
dc.subject.lcshTrusten
dc.subject.lcshUncertaintyen
dc.titleA reputation for the good stuff: user feedback signaling and the deep web market silk roaden
dc.typeThesisen
dc.rights.holderCopyright 2016 by James Matthew Searsen
thesis.degree.committeemembersMembers, Graduate Committee: Isaac Swensen; Daniel Rees.en
thesis.degree.departmentAgricultural Economics & Economics.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage102en
mus.relation.departmentAgricultural Economics & Economics.en_US
mus.data.thumbpage88en


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