Product Differentiation and Film-Programming Choice: Measuring Product Similarity in a Dynamic Panel Data Setting

This paper proposes a general empirical measure of the degree of product differentiation between two products. The robustness of this measure is tested using a rich new panel data set on filmprogramming choice in a major U.S. metropolitan motion-pictures exhibition market. The degree of similarity between two products is captured by the extent to which the two products share the same attributes. We propose a similarity index equal to one for cases in which the attributes of two products are measurably identical, equal to zero when the set of common attributes is empty, and equal to values ranging between zero and one as the degree of product similarity moves from completely dissimilar to perfectly identical. Further, we propose a model of strategic interaction between and among firms that leads to predictions of the factors that will influence the similarity index. We apply the model and predictions to the choice motion-pictures exhibitors make when deciding on weekly film offerings at their theatres, relative to the expected equilibrium choices of their competitors sharing the market. Our dataset covers a year’s worth of weekly movie offerings and screening times between 2000 and 2001 in the Boston metropolitan market. We find significant evidence of stability in the degree of product differentiation within specific theatre pairs over time. Discrepancies in relative market power, in percentage terms, lead to increased differentiation. The further from a holiday the particular week under study, the more differentiated the film offerings are for a given theatre pair. Finally, we explore the significance of ownership patterns in the context of strategic product differentiation and propose an alternative similarity index to capture differences in physical capacity and thus strategic market power. JEL Classifications: L11, C33, L82 We are grateful to the DeSantis Center for Motion Picture Industry Studies of Florida Atlantic University College of Business and Economics for providing a grant to fund this research and to Synergy Retail for compiling the dataset used in this paper. We thank Sanjiv Jaggia for invaluable discussions and suggestions and are grateful to Vicky Huang, Anna Kumysh, and Vidisha Vachharajani for their careful research assistance and to Sorin Codreanu for his data-management programming assistance. 2