In this thesis, I chose Wuhan, a typical big city in mainland China, as the location for the sample which included 194,397 real-time observations to build a prototype of a cinema-level demand system. This thesis is comprised of three related and independent parts, as follows.
Price rigidity for differentiated movies has been a puzzling issue, because the lack of variation in prices makes estimating demand for movies difficult. In first paper, I estimate a demand equation for movies by using data from a typical large urban market in China which features substantial and high-frequency variation in price and box-office sales across films, screening times, and auditoriums. Our two-level nested logit model captures the key details of the market, especially price variation, that determine the market share of a movie, and sheds light on the implications of price decisions.
A vast number of new movies are released to the market each year. Does this lead to excessive entry or expand the industry? A movie release may fail if a cinema ignores potential stiff competition from other cinemas. In second paper, I empirically measure the impact of new movie releases by using detailed entry data across cinemas from a typical large urban market in China. I identify and measure three primary effects of the release of new movies: market expansion of the entire industry, business stealing across different cinemas and cannibalisation among movies within a cinema. I find that differentiated product entry in the movie exhibition industry leads to a strong market expansion effect, weak business-stealing and modest cannibalisation effects, implying that the extensive releasing of new movies increases industry profits and expands consumer choice.
I depict the life cycle of a movie in the third paper by using extensive and detailed data from birth to death across movies, including their screening times and the auditorium sizes, from a large market within China. The life cycle of the movie's distribution shows a descending stair shape. A survival analysis reveals a pattern of dynamic interaction between cinemas’ expectations and the performance of movies, which determines the shape and length of a movie’s life cycle. Cinemas constantly adjust their expectations of a movie to match its performance. The survival model also captures important time-invariant variables that are correlated with the run length of the movie. The rate of occupation drops below 10% before the movie dies, and then a new movie entry will finally push the “ready-to-go” movie to exit from the market.