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We examine the bidding behavior of firms competing in the newly created spot market for electricity in Texas, where electricity generating firms submit hourly supply schedules to sell power. We characterize an equilibrium model of bidding into this market and use detailed firm-level data on bids and marginal costs of production to compare actual bidding behavior to theoretical benchmarks derived from our model. We find that firms with large stakes in the market performed close to the theoretical benchmark of static profit-maximization. However, several smaller firms utilized excessively steep bid schedules that significantly deviated from this benchmark. Our results suggest that payoff scale has an important effect on firms ’ willingness and ability to participate in complex, strategic market environments. We find that the bidding behavior of the smaller firms contributed significantly to productive inefficiency in this new market, although the smaller firms moved closer to the theoretical bidding benchmarks over time.

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.

This paper proposes a semiparametric estimation procedure of the first-price auction model with risk averse bidders within the independent private values paradigm. We show that the model is nonidentified in general from observed bids. Moreover, any distribution of bids can be rationalized by an auction model with either constant relative risk aversion or constant absolute risk aversion. Thus identification of the model must be achieved through additional restrictions. We then establish semiparametric identification under a common but unknown support condition and parameterization of the bidders' utility function. Next we propose a semiparametric method for estimating the corresponding auction model using local polynomial estimators. This method involves several steps and allows to recover the parameters of the utility function as well as the bidders' private values and their distribution. An attractive computational advantage of our method is that it does not require solving the differential equation characterizing the equilibrium strategy. An illustration of the method on U.S. Forest Service timber sales is proposed. In particular, a test of bidders' risk neutrality is performed.

The quantal response equilibrium (QRE) notion of Richard D. McKelvey and Thomas R. Palfrey (1995) has recently attracted considerable attention, due in part to its widely documented ability to rationalize observed behavior in games played by experimental subjects. However, even with strong a priori restrictions on unobservables, QRE imposes no falsifiable restrictions: it can rationalize any distribution of behavior in any normal form game. After demonstrating this, we discuss several approaches to testing QRE under additional maintained assumptions.

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This paper studies experimentally how information about rivals’ types affects bidding behavior in first- and second-price auctions. The comparative static hypotheses associated with information about rivals enables us to test the relevance of such information as well as of the auction theory in general, by providing an effective means to control for risk aversion and other behavioral motives that were difficult to control for in previous experiments. Our experimental evidence provides strong support for the theory, and sheds light on the roles of risk aversion and the spite motive in first- and second-price auctions, respectively.

∗We would like to thank John Ham and José-Vı́ctor Rı́os-Rull for many helpful comments. Financial support from the Social Research and Demonstration Corporation is gratefully acknowledged. This paper introduces an innovative test of search and matching models using the exogenous variation introduced in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context.

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and a Ph.D. in Economics. He previously taught for eight years in the Graduate School of Business at the University of Chicago. He has won numerous awards for teaching excellence at the Carlson School, and has been recognized as an outstanding faculty member in BusinessWeek’s Guide to the Best Business Schools. He teaches MBA and executive courses in areas such as pricing strategy, marketing strategy, marketing management, and supply-chain management. His research focuses on pricing and channels of distribution, where he has studied issues such as “pricing as a strategic capability, ” price wars, price pass-through, branded variants, dual distribution, gray markets, co-op advertising, and quick response. Bergen’s research has been published in top scholarly journals. Recent articles include “How to Fight a Price War ” pub-lished in Harvard Business Review and “Pricing as a Strategic Capability ” and “Com-peting with Gray Markets, ” published in Sloan Management Review. In addition, he has extensive pricing consulting experience with companies in the medical, services, food, retail, and industrial markets. ROBERT J. KAUFFMAN is the Director of the MIS Research Center, and Professor and