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

By Aviv Nevo. 2001

2,264 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Marketing

Citations in the corpus

Counterfactual inference for consumer choice across many product categories (2019, methodology): “Analysts have applied the discrete choice framework to a variety of different types of data sets, including aggregate, market-level data (see, e.g., Berry et al. (1995), Nevo (2001), and Petrin (2002)) 1 , as well as data from individual choices for a cross-section of individuals.”
Discrete Choice Models with Multiple Unobserved Choice Characteristics (2007, positive): “Such settings have been the motivation for BLP and literature that follows them (e.g., Nevo, 2000, 2001; Petrin, 2003; Ackerberg and Rysman, 2002; Bajari and Benkard, 2003).”
Services and the Business Models of Product Firms: An Empirical Analysis of the Software Industry (2013, methodology): “For instance, Nevo (2001) uses the BLP approach to create IVs to explain profit margins of different types of cereals.”
Spatial competition in retail markets: movie theaters (2006, background): “See Nevo (2000) for example in the aggregate data mixed logit literature and also the discussion and references in Ruud (1996) in the individual level data mixed logit literature.”
Estimating market power in a two-sided market: The case of newspapers (2007, methodology): “We can then compare the estimated margins with the empirical margins in order to assess which is the right model as in Nevo (2001).”
Monetary Transmission through Shadow Banks (2018, positive): “I introduce product differentiation for bank deposits and heterogeneous preferences for depositors following the industrial organization literature (Berry 1994; Berry, Levinsohn, and Pakes 1995; Nevo 2001).”
A Bayesian analysis of the multinomial probit model with fully identified parameters (2000, background): “For example, a multinomial model for choices between di!erent brands of similar products as in Nevo (1997) would feature a price coe$cient which is certainly negative and never much less than !20 or so.”
Investigating Category Pricing Behavior at a Retail Chain (2002, methodology): “Recent research in the economics literature (BLP; Nevo 2001) has applied such random coefficients versions of the logit discrete choice model that allow for a flexible pattern of cross-elasticities among brands with aggregate- as opposed to individuallevel data.”
Trucks without Bailouts: Equilibrium Product Characteristics for Commercial Vehicles (2018, background): “of the second stage profits with respect to prices [Nevo, 2001]. It also incorporates microdata to form “micromoments”, which aid in understanding the relationship between the buyer attributes and product characteristics [Petrin, 2002] (considered also in [Berry, Levinsohn, and Pakes, 2004]). The pr”
The Role of the Management Sciences in Research on Personalization (2003, neutral): “Another alternative is to use instrumental variables for the endogenous variables as in Nevo (1997), who uses prices and advertising of ready-to-eat cereals in a similar geographical region as instruments to estimate the demand function in a given region.”