A POISSON PROBABILITY MODEL OF ENTRY AND MARKET-STRUCTURE WITH AN APPLICATION TO UNITED-STATES-INDUSTRIES DURING 1972-77

The performance characteristics of an industry are closely linked to the nature of entry and exit in the industry. 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. First, we analyze a new sample period, 1972-77. Second, we provide a methodological improvement over the logarithmic regression approaches of Orr and Duetsch. Our methodology is in the spirit of Hausman, Hall, and Griliches who apply the Poisson distribution to count data on patent application across firms. 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 second limitation of the log specification is that maximum likelihood estimation is precluded. With the distribution of entry specified as Poisson we are also able to estimate the probabilities of entry across industries, and thus direct the study towards limit pricing in a new and interesting way. In sum, dynamic limit-pricing models predict that pricing decisions across different market structures vary with the probability of entry across markets. Consequently, estimates of these probabilities are of interest for limit pricing. -from Authors