The ready-to-eat cereal industry is characterized by high concentration, high price-cost margins, large advertising-to-sales ratios, and numerous introductions of new products. Previous researchers have concluded that the ready-to-eat cereal industry is a classic example of an industry with nearly collusive pricing behavior and intense nonprice competition. This paper empirically examines this conclusion. In particular, I estimate price-cost margins, but more importantly I am able empirically to separate these margins into three sources: (i) that which is due to product differentiation; (ii) that which is due to multi-product firm pricing; and (iii) that due to potential price collusion. The results suggest that given the demand for different brands of cereal, the first two effects explain most of the observed price-cost margins. I conclude that prices in the industry are consistent with noncollusive pricing behavior, despite the high price-cost margins. Leading firms are able to maintain a portfolio of differentiated products and influence the perceived product quality. It is these two factors that lead to high price-cost margins.

This paper examines the construction of a price index based on an estimated-demand system. In principle the method examined can produce a price index that takes account of the introduction of new products and quality changes in existing products. However, I isolate two key assumptions that have to be made in order to interpret the demand estimates into welfare measures. Using estimates of a brand-level demand system for ready-to-eat cereal, I demonstrate the empirical importance of the assumptions. For the data I use, depending on the interpretation of the demand estimates, a price index can range between a 35% increase over the five years examined to a 2.4% decrease.

Temporary price reductions (sales) are common for many goods and naturally result in large increases in the quantity sold. Demand estimation based on temporary price reductions may mismeasure the long run responsiveness to prices. In this paper we quantify the extent of the problem and assess its economic implications. We structurally estimate a dynamic model of consumer choice using two years of scanner data on the purchasing behavior of a panel of households. The results suggest that static demand estimates, which neglect dynamics: (i) overestimate own price elasticities by 30 percent; (ii) underestimate cross-price elasticities to other products by up to a factor of 5; and

We study intertemporal price discrimination when consumers can store for future consumption needs. We offer a simple model of demand dynamics, which we estimate using market-level data. Optimal pricing involves temporary price reductions that enable sellers to discriminate between price sensitive consumers, who stockpile for future consumption, and less price-sensitive consumers, who do not stockpile. We empirically quantify the impact of intertemporal price discrimination on profits and welfare. We find that sales (i) capture 25–30 percent of the gap between non-discriminatory profits and (unattainable) third-degree price discrimination profits, (ii) increase total welfare, and (iii) have a modest impact on consumer welfare.

Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead assume (i) the correlation between the instrument and the error term has the same sign as the correlation between the endogenous regressor and the error term, and (ii) that the instrument is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate the method in two applications.

We compare house sales on a For-Sale-By-Owner (FSBO) platform to Multiple Listing Service (MLS) sales and find that FSBO precommission prices are no lower, but that FSBO is less effective in terms of time to sell and probability of a sale. We do not find direct evidence of the importance of network size as a reason for the lower effectiveness of FSBO. We do find evidence of endogenous platform differentiation: patient sellers use FSBO while patient buyers transact more often on the MLS (where they avoid patient sellers). We discuss the implications for platform competition, two-sided markets, and welfare.

We estimate demand for residential broadband using high-frequency data from subscribers facing a three-part tariff. The three-part tariff makes data usage during the billing cycle a dynamic problem, thus generating variation in the (shadow) price of usage. We provide evidence that subscribers respond to this variation, and we use their dynamic decisions to estimate a flexible distribution of willingness to pay for different plan characteristics. Using the estimates, we simulate demand under alternative pricing and find that usage-based pricing eliminates low-value traffic. Furthermore, we show that the costs associated with investment in fiber-optic networks are likely recoverable in some markets, but that there is a large gap between social and private incentives to invest.

We estimate a bargaining model of competition between hospitals and managed care organizations (MCOs) and use the estimates to evaluate the effects of hospital mergers. We find that MCO bargaining restrains hospital prices significantly. The model demonstrates the potential impact of coinsurance rates, which allow MCOs to partly steer patients toward cheaper hospitals. We show that increasing patient coinsurance tenfold would reduce prices by 16 percent. We find that a proposed hospital acquisition in Northern Virginia that was challenged by the Federal Trade Commission would have significantly raised hospital prices. Remedies based on separate bargaining do not alleviate the price increases.

The prices of for-profit academic journals have increased rapidly over the past decade (Barbara Albee and Brenda Dingley, 2001). There remains substantial debate as to the explanation for these increases. Among those put forward are the increased concentration of the journal industry (see e.g., McCabe, 2002) and the relatively recent effort by major publishers to bundle print and electronic journals (Aaron S. Edlin and Rubinfeld, 2004). While both explanations are undoubtedly important, what is missing is the significant role of the primary customers of journal publishers—the academic libraries. As agents of college and university faculties, libraries serve the interests of their principals while having only limited information about faculty journal demands. Facing little or no hard budget constraint, faculty are unlikely or unwilling to make difficult allocative choices. As a result, libraries have been making hard choices for years (between journals and books, and among journals), in a world of increasing budgetary pressure. Given that electronic transmission of knowledge is becoming increasingly important, an understanding of the reasons for the increases in journal prices is a vital element in the ongoing discussion of best mechanisms by which scholarly communications can be disseminated. In this paper, we formulate a model of library journal demand and suggest how it can be used to analyze the optimal pricing of journals by publishers. This represents part of a larger project whose long-range goal is to explain the pattern of journal pricing over time, and to

Competition authorities have expressed concern that firms selling broadband internet and TV subscriptions may employ usage-based pricing (UBP) of internet to steer consumers toward TV over streaming video. We study this issue with household-level panel data from an internet service provider's UBP experiment, capturing the prices' effects on internet and TV subscriptions, internet usage, and firm revenue. We find that this specific UBP policy largely served to meter internet usage by high-demand households rather than steer them toward TV. Households' payments increased due to usage-related overage charges and internet subscription upgrades to avoid overages. Some households avoided internet-related payments by reducing usage rather than adding TV subscriptions.