A Poisson Probability Model of Entry and Market Structure with an Application to U. S. Industries during 1972-77

Abstract

The performance characteristics of an industry are closely linked to the nature of entry and exit in the industry. If entry barriers are low, the threat of potential entry can effectively constrain the ability of incumbent firms to raise price above the competitive level. On the other hand, as entry barriers rise and the probability of entry diminishes, the potential for monopolistic practices increases. Prior empirical studies of entry have focused mainly on its determinants, emphasizing industry characteristics as entry barriers. Examples include McGuckin [19], Orr [21], McDonald [17], and Duetsch [7], which analyze the number of new entrants, and Berry [4], McDonald [17; 18], and Masson and Shannon [15; 16] which focus on the market share of entering firms. Our study uses the model of Orr, and its later extension by Duetsch, as its initial reference point. Like the Orr-Duetsch studies we estimate a model for entry determinants across industries based on the number of new firms. Our contribution is two-fold. First, we analyze a new sample period, 1972-77. Second, we provide a methodological improvement over the logarithmic regression approaches of Orr and Duestch. Because the observations on entry are count data (non-negative integers), our model is developed from the premise that entry requires a statistical framework based on a discrete probability distribution. To meet this requirement, we specify and estimate an econometric model of entry based on the Poisson distribution. Our methodology is in the spirit of Hausman, Hall, and Griliches [11] who apply the Poisson distribution to count data on patent application across firms. Hausman, Hall and Griliches point out that the Poisson model offers an improved methodology for a wide range of economic applications that feature data in the form of repeated counts. This observation motivated our application of the Poisson distribution to the entry problem. The Poisson approach admits a richer analysis of the entry data than the logarithmic regression approach in two ways. First, the logarithmic specification, while computationally convenient, provides a rather incomplete description of the entry data. The log of entry is only well defined