Aviv Nevo

Measuring Market Power in the Ready-to-Eat Cereal Industry

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 non-price 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: (1) that which is due to product differentiation; (2) that which is due to multi-product firm pricing; and (3) 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 markups. I conclude that prices in the industry are consistent with non-collusive 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.

A Practitioner's Guide to Estimation of Random‐Coefficients Logit Models of Demand

Estimation of demand is at the heart of many recent studies that examine questions of market power, mergers, innovation, and valuation of new brands in differentiated‐products markets. This paper focuses on one of the main methods for estimating demand for differentiated products: random‐coefficients logit models. The paper carefully discusses the latest innovations in these methods with the hope of increasing the understanding, and therefore the trust among researchers who have never used them, and reducing the difficulty of their use, thereby aiding in realizing their full potential.

Mergers with Differentiated Products: The Case of the Ready-to-Eat Cereal Industry

Traditional merger analysis is difficult to implement when evaluating mergers in industries with differentiated products. I discuss an alternative, which consists of demand estimation and the use of a model of postmerger conduct to simulate the competitive effects of a merger. I estimate a brand-level demand system for ready-to-eat cereal using supermarket scanner data and use the estimates to (1) recover marginal costs, (2) simulate postmerger price equilibria, and (3) compute welfare effects, under a variety of assumptions. The methodology is applied to five mergers, two of which occurred and for which I compare predicted to actual outcomes.

Sales and consumer inventory

Temporary price reductions (sales) are quite common for many goods and usually result in an increase in the quantity sold. We explore whether the data support the hypothesis that these increases are, at least partly, due to dynamic consumer behavior: at low prices consumers stockpile for future consumption. This effect, if present, has broad implications for interpretation of demand estimates. We construct a dynamic model of consumer choice and use it to derive testable predictions. We test the implications of the model using two years of store-level scanner data and data on the purchases of a panel of households over the same time. The results support the existence of household stockpiling behavior. (This abstract was borrowed from another version of this item.)

Identification With Imperfect Instruments

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 allow the instrumental variable to be correlated with the error term, but we assume the correlation between the instrumental variable and the error term has the same sign as the correlation between the endogenous regressor and the error term and that the instrumental variable is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate that the method can generate useful (set) estimates by using it to estimate demand for differentiated products.

Sales and Consumer Inventory

Temporary price reductions (sales) are quite common for many goods and usually result in an increase in the quantity sold. We explore whether the data support the hypothesis that these increases are, at least partly, due to dynamic consumer behavior: at low prices consumers stockpile for future consumption. This effect, if present, has broad implications for interpretation of demand estimates. We construct a dynamic model of consumer choice and use it to derive testable predictions. We test the implications of the model using two years of store-level scanner data and data on the purchases of a panel of households over the same time. The results support the existence of household stockpiling behavior.

Intertemporal Substitution and Storable Products

Storable products allow consumers to time their purchases to exploit price fluctuations. It has been documented that during promotions consumers buy more. The additional purchases are potentially intended not only for current use, but to be stockpiled for future consumption. This paper discusses the predictions of a consumer inventory model and reviews the available evidence. We then discuss the implications for demand estimation and present estimates of the economic magnitude of the dynamic effect of storability. (JEL: L0, L4, D1, D4) Copyright (c) 2004 The European Economic Association.

Academic Journal Pricing and the Demand of Libraries

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 evaluate two broad policy questions: (i) To what extent have mergers and/or bundling practices been responsible for the increase in journal prices? (ii) To what extent are journal prices likely to change in response to changing library acquisition strategies (reallocation of budgets between serials and books, journal sharing among libraries, setting hard budgets, etc.)?

Empirical models of imperfect competition: A discussion

The field of Industrial Organization (IO) studies the behavior of firms and the interaction among them. In the last 25 years, IO studies have increasingly focused on single industries, using a combination of economic theory and statistics to analyze strategic interaction between firms. The focus on a particular industry allows the researcher to develop a model that takes into account the specific details of the industry. IO economists then use the model to derive comparative statics and test them in the data, and often estimate the structural parameters of the model using state-of-the-art econometric methods. IO studies a broad array of decisions made by firms, starting from long-run decisions, such as those of entry into particular markets or those that relate to product design and development, medium-run decisions, such as contractual relationships and production, and short-run decisions, such as pricing and bidding in auctions. As each of these decisions is somewhat distinct from others in various aspects (the nature of the decision, the relevant policy questions, the typical data sets available to the empirical researcher, and so on), the literature of recent years can be classified according to which decision it analyzes. Two issues of particular interest have been entry and exit decisions that determine market structure and price competition. These issues are the focus of the two excellent surveys by Athey and Haile, and Berry and Tamer. Athey and Haile survey the key principles guiding the studies of auction markets, while the paper by Berry and Tamer addresses recent work and identification in empirical models of strategic entry. While the papers and the topics are quite different, they share several themes. First, they exploit the assumption that agents are acting optimally (i.e. maximizing profits or utility) and that the data is generated by equilibrium behavior in order to infer unobserved quantities from observed variables. Second, by building complete economic

The Tragedy of the Last Mile: Congestion Externalities in Broadband Networks

We exibly estimate demand for residential broadband accounting for congestion externalities that arise among consumers due to limited network capacity, as well as dynamics arising from nonlinear pricing. Our high frequency data permits insight into temporal patterns in usage across the day that are impacted by network congestion, and how usage responds to efforts to mitigate congestion. To estimate demand, we build a dynamic model of consumer choice and rely on variation in the timing of network upgrades and nonlinear pricing to identify the model. Using the model estimates, we calculate the welfare changes associated with different economic and technological solutions for reducing congestion, including peak-use pricing, throttling connectivity speeds, and local-cache technologies.