Computer simulations, as well as traditional and recent theory, suggest hypotheses on the dynamics of dispersed prices that we test on existing laboratory data. As predicted in some variations of the Edgeworth hypothesis, the posted price data exhibit a significant cycle. Relative to the unique stationary distribution, the empirical distribution has excess mass in a price interval that moves downward over time until it approaches the lower boundary of the stationary distribution. Then the excess mass jumps upward and the downward cycle resumes. The amplitude of the cycle seems fairly constant over the longer experimental sessions. Of the simulations we consider, the one closest to Edgeworth’s 1925 account, a hybrid of gradient dynamics and logit dynamics, seems to best reproduce the observed dynamics. Acknowledgements: Financial support for the laboratory data collection was provided by the NSF under grants SBR-9617917 and SBR-9709874. We are also grateful to Ed Hopkins and Jörgen Weibull for helpful discussions. The second author thanks CeNDEF for sponsoring the

In a recent paper Hong and Shum (2006) present a structural methodology to estimate search cost distributions. We extend their approach to the case of oligopoly and present a new method to estimate search costs by maximum likelihood. We apply our method to a data set of online prices for different computer memory chips. The estimates suggest that on-line consumers have either quite high or quite low search costs so they either search exhaustively in the market or very little, for at most three prices. Search frictions confer a significant amount of market power to the firms: despite that more than 20 firms operate in each of the markets we study, price-cost margins are around 25%. Kolmogorov-Smirnov goodness-of-fit tests suggest we cannot reject the null hypothesis that the observed prices are generated by the model. The paper also illustrates how the structural methodology can be employed to simulate the effects of policy interventions.

At the firm level, revenue and costs are well measured but prices and quantities are not. This paper shows that because of these data limitations estimates of returns to scale at the firm level are for the revenue function, not production function. Given this observation, the paper argues that, under weak assumptions, micro-level estimates of returns to scale are often inconsistent with profit maximization or imply implausibly large profits. The puzzle arises because popular estimators ignore heterogeneity and endogeneity in factor/product prices, assume perfect elasticity of factor supply curves or neglect the restrictions imposed by profit maximization (cost minimization) so that estimators are inconsistent or poorly identified. The paper argues that simple structural estimators can address these problems. Specifically, the paper proposes a full-information estimator that models the cost and the revenue functions simultaneously and accounts for unobserved heterogeneity in productivity and factor prices symmetrically. The strength of the proposed

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In our multistage search model, the seller’s reservation price is affected by the offer price distribution, while the optimal asking price is chosen so as to maximize the return from search. We show that a greater dispersion in offer prices leads to a higher reservation price and a higher optimal asking price, which in turn results in a higher expected transaction price. Under the assumption that offer prices are normally distributed, a higher dispersion of offer prices also reduces time on the market for overpriced properties.

This paper presents a study of mergers in a consumer search market where firms sell homogeneous products. Our first result is that mergers have redistributive effects with consumers searching little getting better off at the expense of consumers who search a lot. Our second result is that the magnitude of search costs is crucial in determining the incentives of firms to merge and the welfare implications of mergers. When search costs are relatively small, mergers turn out not to be profitable for the merging firms, which shows that a “merger paradox ” can also arise under price competition. If search costs are relatively high instead, a merger causes a fall in average price and this triggers search. As a result, non-shoppers who didn’t find it worthwhile to search in the premerger situation, start searching post-merger. We show that this change in the search composition of demand may make mergers incentive-compatible for the firms and, in some cases, socially desirable.

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A brief survey of the economics of price dispersion, written for the New Palgrave

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At the firm level, revenue and costs are well measured but prices and quantities are not. This paper shows that because of these data limitations estimates of returns to scale at the firm level are for the revenue function, not production function. Given this observation, the paper argues that, under weak assumptions, micro-level estimates of returns to scale are often inconsistent with profit maximization or imply implausibly large profits. The puzzle arises because popular estimators ignore heterogeneity and endogeneity in factor/product prices, assume perfect elasticity of factor supply curves or neglect the restrictions imposed by profit maximization (cost minimization) so that estimators are inconsistent or poorly identified. The paper argues that simple structural estimators can address these problems. Specifically, the paper proposes a fullinformation estimator that models the cost and the revenue functions simultaneously and accounts for unobserved heterogeneity in productivity and factor prices symmetrically. The strength of the proposed estimator is illustrated by Monte Carlo simulations and an empirical application. Finally, the paper discusses a number of implications of estimating revenue functions rather than production functions and demonstrates that the profit share in revenue is a robust non-parametric economic diagnostic for estimates of returns to scale.