This paper presents experimental evidence on the effects of minimum bids in first-price, sealed-bid auctions. The auction experiments manipulated the minimum bids in a preexisting market on the Internet for collectible trading cards from the game Magic: the Gathering. They yielded data on a number of economic outcomes, including the number of participating bidders, the probability of sale, the levels of individual bids, and the auctioneer's revenues. The benchmark theoretical model tested here is the classic auction model described by Riley and Samuelson (1981), with symmetric, risk-neutral bidders with independent private values. The data verify a number of the predictions of the theory. A particularly interesting result shows that many bidders behave strategically, anticipating the effects of the reserve price on others' bids.
We estimate that American firms and consumers experience costs of almost $20 billion annually due to spam. Our figure is more conservative than the $50 billion figure often cited by other authors, and we also note that the figure would be much higher if it were not for private investment in anti-spam technology by firms, which we detail further on. Based on the work of crafty computer scientists who have infiltrated and monitored spammers' activity, we estimate that spammers and spam-advertised merchants collect gross worldwide revenues on the order of $200 million per year. Thus, the “externality ratio” of external costs to internal benefits for spam is around 100:1. In this paper, we start by describing the history of the market for spam, highlighting the strategic cat-and-mouse game between spammers and email providers. We discuss how the market structure for spamming has evolved from a diffuse network of independent spammers running their own online stores to a highly specialized industry featuring a well-organized network of merchants, spam distributors (botnets), and spammers (or “advertisers”). We then put the spam market's externality ratio of 100 into context by comparing it to other activities with negative externalities. Lastly, we evaluate various policy proposals designed to solve the spam problem, cautioning that these proposals may err in assuming away the spammers' ability to adapt.
In empirical analysis of economic games, researchers frequently wish to estimate quantities describing group outcomes, such as the expected revenue in an auction or the mean allocative efficiency in a market experiment. For such applications, we propose an improved statistical estimation technique called "recombinant estimation." The technique takes observations of the complete strategy of each player and recombines them to compute all the possible group outcomes which could have resulted from different matches of players. We calculate the improvement in efficiency of the recombinant estimator relative to the standard estimator, and show how to estimate standard errors for the recombinant estimator for use in hypothesis testing. We present an application to a two-player sealed-bid auction and a two-player ultimatum bargaining game. In these applications, the improved efficiency of our estimator is equivalent to an increase of between 40% and 200% in the sample size. We discuss how to design game experiments in order to be able to take full advantage of recombinant estimation. Finally, we discuss practical computational issues, showing how one can avoid combinatorial explosions of computing time while still yielding significantly improved efficiency of estimation.
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