Peter C. Reiss

Entry And Competition In Concentrated Markets

This paper proposes an empirical framework for measuring the effects of entry in concentrated markets. Building on models of entry in atomistically competitive markets, we show how the number of producers in an oligopolistic market varies with changes in demand and market competition. These analytical results structure our empirical analysis of competition in five retail and professional industries. Using data on geographically isolated monopolies, duopolies, and oligopolies, we study the relationship between the number of firms in a market, market size, and competition. Our empirical results suggest that competitive conduct changes quickly as the number of incumbents increases. In markets with five or fewer incumbents, almost all variation in competitive conduct occurs with the entry of the second or third firm. Surprisingly, once the market has between three and five firms, the next entrant has little effect on competitive conduct.

Cost-Reducing and Demand-Creating R&D with Spillovers

This paper analyzes R&D policies when the returns to cost-reducing and demand-creating R&D are imperfectly appropriable and market structure is endogenous. Previous characterizations of appropriability are generalized to permit the possibility that own and rival R&D are imperfect substitutes. We also describe how. equilibrium expenditures on process and product R&D, as well as equilibrium market structure, depend on technological opportunities and spillovers. In contrast to previous work, diminished appropriability does not necessarily reduce R&D expenditures. For example, under some conditions, an increase in the extent of process (product) spillovers will lead to an increase in product (process) R&D. We estimate several variants of the model using manufacturing line of business data and data from a survey of R&D executives.

Do Entry Conditions Vary across Markets

THE NUMBER OF FIRMS in a market is a primary determinant of market concentration and performance. In the long run the number of firms is affected by the ease with which they can enter and exit. Many recent theoretical models of entry have emphasized that strategic behavior by incumbents may have an important bearing on the number of firms that enter the market. For instance, these models illustrate how the extent of postentry competition and opportunities for erecting strategic entry barriers might affect the likelihood that another firm will enter a market.1 In contrast to these strategic models, other models of the long-run number of firms emphasize that technological factors, such as economies of scale, determine entry. These theories minimize the importance of strategic behavior in the long run and instead emphasize that highly concentrated industries are simply ones for which few firms will fit given the degree of returns to scale.

Competition and Entry in Small Airline Markets

EMPIRICAL studies of deregulated airline markets usually address two policy questions. First, did airline deregulation make city-pair markets competitive? Second, should postderegulation mergers and the emergence of hub and spoke systems raise antitrust concerns? Several recent empirical analyses of airline ticket prices conclude that airline deregulation did not make city-pair markets perfectly contestable.1 Some of these studies also conclude that potential competition at origin and destination airports does not promote much price competition.2 These studies typically draw their conclusions from regressions that uncover a positive correlation between average market fares and measures of market or

Dealer and manufacturer margins

When retail dealers carry only one product line, the size of the dealer margin is crucial in the success of both the manufacturer and the dealer. This article proposes a successive monopoly model of patterns in exclusive dealer and manufacturer margins across a product line. The predictions of the model then are compared with the pricing practices of a major U.S. automobile manufacturer and its dealers. The data support a special case of our theory. Our analysis also indicates that we cannot reject the hypothesis that the retail demand curves for these models are (locally) linear. Finally, we use the margin data to provide updated evidence on the extent to which retail prices depart from list price.

Alternative Nonnested Specification Tests of Time Series Investment Models

This paper develops and compares nonnested hypothesis tests for linear regression models with first-order serially correlated errors. It extends the nonnested testing procedures of Pesaran, Fisher and McAleer, and Davidson and MacKinnon, and compares their performance on four conventional models of aggregate investment demand using quarterly U.S. investment data from 1951:1 to 1983:IV. The data and the nonnested hypothesis tests initially indicate that no model is correctly specified, and that the tests are occasionally intransitive in their assessments. Before rejecting these conventional models of investment demand, we go on to investigate the small sample properties of these different nonnested test procedures through a series of monte carlo studies. These investigations demonstrate that when there is significant serial correlation, there are systematic finite sample biases in the nominal size and power of these test statistics. The direction of the bias is toward rejection of the null model, although it varies considerably by the type of test and estimation technique. After revising our critical levels for this finite sample bias, we conclude that the accelerator model of equipment investment cannot be rejected by any of the other alternatives.

Chapter 64 Structural Econometric Modeling: Rationales and Examples from Industrial Organization

Abstract 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.

Structural Workshop Paper---Descriptive, Structural, and Experimental Empirical Methods in Marketing Research

What can be learned about marketing phenomena from descriptive, structural, and experimental empirical models? Is structure implicit in a descriptive empirical model? What is a “reduced-form model?” What is a natural experiment, and what can one infer from a study that uses experimental data? Having clear answers to these questions can improve empirical dialog. This paper defines descriptive, structural, and experimental empirical work, provides examples, discusses their similarities and differences, and comments on their strengths and weaknesses. An important theme is that the marketing question and the data available should determine the methods used, and not the other way around. Most of the examples discussed reference linear models that are widely employed in the marketing literature. Many of the points, however, extend to the development and interpretation of cutting-edge nonlinear, dynamic, or nonparametric models used in marketing.

Detecting multiple outliers with an application to R&D productivity

Abstract Multiple outlying observations are frequently encountered in empirical analyses of economic data. Several multiple outlier tests exist, but little evidence is available on their relative power against alternative causes of outliers and influence. This paper analytically and numerically compares the sensitivity of several outlier diagnostics to different forms of data contamination; it also proposes new statistics. Practical issues associated with these tests are addressed using data on R&D spending and total factor productivity from Griliches and Lichtenberg (1984). The tests confirm their hypothesis that previously low estimates of the return to R&D maybedue to outliers.

Just How Sensitive are Instrumental Variable Estimates

Researchers regularly use instrumental variables to resolve concerns about regressor endogeneity. The existing literature has correctly emphasized that the choice of instrumental variables matter for the resulting estimates. This paper shows that researchers should also be concerned that the functional form of the instrument matters as well for the resulting estimates. For example, simply changing an instrumental variable from the level to the logarithm can change estimates directly. This article documents the problem, suggests why the problem occurs and suggests different approaches to the problem.