Internet Exchanges for Used Books: An Empirical Analysis of Welfare Implications

Information technology-enabled exchanges have enhanced the viability of a variety of secondary markets, notably markets for used books. Electronic used book exchanges, in particular, offer a wider selection, lower search costs, and significantly lower prices than physical used bookstores do. The increased viability of these used book markets has caused concern among groups such as the Book Publishers Association and AuthorâÃÂÃÂs Guild who believe that used book markets will significantly cannibalize new book sales. This proposition, while theoretically possible, is based on speculation as opposed to empirical evidence. In this research, we use a unique dataset collected from Amazon.comâÃÂÃÂs new and used marketplaces to estimate the impact of IT-enabled used book markets on new book sales. We use these data to calculate the impact of these secondary market exchanges on consumer and publisher welfare by calculating the cross-price elasticity of new books sales with respect to used book prices. Our analysis suggests that IT-enabled secondary market exchanges increase consumer surplus by approximately $70 million annually. Further, we find that only 15cannibalize new book purchases. The remaining 85lose only$32 million in gross profit annually (about 0.2% of total gross profit) due to the presence of AmazonâÃÂÃÂs used book markets. Further, the additional used book readership gain from these electronic markets may mitigate author losses through increased revenue from secondary sources such as speaking and licensing fees. These surplus changes, combined with the estimated $64 million the used book market added to AmazonâÃÂÃÂs gross profits, show that IT-enabled used markets for books have a strong positive first-order impact on total welfare.Information Systems Working Papers Serie

The Economics of Peer-to-Peer Networks

Peer-to-Peer (P2P) networks have emerged as a significant social phenomenon for the distribution of information goods and may become an important alternative to traditional client-server network architectures for knowledge sharing within enterprises. This paper reviews and synthesizes the relevant computer science and economics literatures as they relate to P2P networks, and raises important questions for researchers interested in studying the behavior of these networks from the perspective of the economics of information technology. With regard to the economic characteristics of these networks, we show that while the characteristics of services provided over P2P networks are similar to public goods and club goods, they have many important differences and hence there is a need for new theoretical models as well as empirical and experimental analysis to understand P2P user behavior. We then identify several important areas for study with regard to the economics of P2P networks and review recent academic papers in each area

Internet Exchanges for Used Books: An Empirical Analysis of Welfare Implications

Information technology-enabled exchanges have enhanced the viability of a variety of secondary markets, notably markets for used books. Electronic used book exchanges, in particular, offer a wider selection, lower search costs, and significantly lower prices than physical used bookstores do. The increased viability of these used book markets has caused concern among groups such as the Book Publishers Association and AuthorâÃÂÃÂs Guild who believe that used book markets will significantly cannibalize new book sales. This proposition, while theoretically possible, is based on speculation as opposed to empirical evidence. In this research, we use a unique dataset collected from Amazon.comâÃÂÃÂs new and used marketplaces to estimate the impact of IT-enabled used book markets on new book sales. We use these data to calculate the impact of these secondary market exchanges on consumer and publisher welfare by calculating the cross-price elasticity of new books sales with respect to used book prices. Our analysis suggests that IT-enabled secondary market exchanges increase consumer surplus by approximately $70 million annually. Further, we find that only 15cannibalize new book purchases. The remaining 85lose only$32 million in gross profit annually (about 0.2% of total gross profit) due to the presence of AmazonâÃÂÃÂs used book markets. Further, the additional used book readership gain from these electronic markets may mitigate author losses through increased revenue from secondary sources such as speaking and licensing fees. These surplus changes, combined with the estimated $64 million the used book market added to AmazonâÃÂÃÂs gross profits, show that IT-enabled used markets for books have a strong positive first-order impact on total welfare.Information Systems Working Papers Serie

Culling the herd: using real-world randomized experiments to measure social bias with known costly goods

Peer ratings have become increasingly important sources of product information, particularly in markets for information goods. However, in spite of the increasing prevalence of this information, there are relatively few academic studies that analyze the impact of peer ratings on consumers transacting in real-world marketplaces. In this paper, we partner with a major telecommunications company to analyze the impact of peer ratings in a real-world video-on-demand market where consumer participation is organic and where movies are costly and well known to consumers. After experimentally changing the initial conditions of product information displayed to consumers, we find that, consistent with the prior literature, peer ratings influence consumer behavior independently from underlying product quality. However, we also find that, in contrast to the prior literature, there is little evidence of long-term bias as a result of herding effects, at least in our setting. Specifically, when movies are artificially promoted or demoted in peer rating lists, subsequent reviews cause them to return to their true quality position relatively quickly. One explanation for this difference is that consumers in our empirical setting likely had more outside information about the true quality of the products they were evaluating than did consumers in the studies reported in prior literature. Although tentative, this explanation suggests that in real-world marketplaces where consumers have sufficient access to outside information about true product quality, peer ratings may be more robust to herding effects and thus provide more reliable signals of true product quality than previously thought.info:eu-repo/semantics/acceptedVersio

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers