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The Pennsylvania State University The Graduate School ESSAYS ON THE ECONOMICS OF THE MOTION PICTURE INDUSTRY A Dissertation in Economics by Naibin Chen © 2020 Naibin Chen Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2020 The dissertation of Naibin Chen was reviewed and approved by the following: Peter Newberry Assistant Professor of Economics Dissertation Co-Advisor Co-Chair of Committee Mark J. Roberts Professor of Economics Dissertation Co-Advisor Co-Chair of Committee Paul L. E. Grieco Associate Professor of Economics Christopher Parker Assistant Professor of Information Technology & Analytics Charles Murry Assistant Professor of Economics Special Member Marc Henry Professor of Economics Graduate Program Director ii Abstract Chapter 1 inspects how critic reviews and audience reviews impact a movie’s box office performance differently. I use a Bayesian Learning model to model the process of consumers forming their beliefs about a movie’s quality, based on their prior, critic reviews, and audience reviews. Consumers then make a discrete choice from the set of movies that are available in a given week. Using a dataset of 665 movies released in 2011-2015, I estimate the model and show that critic reviews and audience reviews have significant and heterogeneous effects on movies produced by major studios or minor studios, movies reviewed by critics before release (regular) or not (cold-opened), and movies with different advertising expenditures. My counterfactual analyses show that forcing movies to be screened before release harms 71% of the cold-opened movies; consumers benefit from the availability of audience reviews by watching more good movies and fewer bad movies, with the quality measured from the perspective of consumers. Chapter 2 continues to discuss how the availability of reviews may change the way that consumers respond to advertising and, in turn, affect firms’ advertising strategies. Using data from the motion picture industry, I estimate the effect of advertising on consumer choices depending on whether or not consumers have access to expert reviews. Given the demand estimates, I evaluate a studio’s optimal advertising choice depending on the availability of expert reviews. My results show that advertising and expert reviews both have significant impacts on demand. For an average movie, when expert reviews are absent, advertising is about 2.25 times as effective at increasing the opening week revenue as it is when expert reviews are available. It would appear that expert reviews help consumers make better choices, as the percentage of consumers who choose a movie they would not watch if they were better informed drops by 0.74% on average. Additionally, with the presence of expert reviews, the median studio saves 76.60% of advertising expenditure and studios’ profits increase by $2.80 million per movie on average. Chapter 3 investigates the cultural effect of changing a product characteristic in the motion picture industry. In response to a growing Chinese market, Hollywood companies have been adding Chinese features in their movies. Such a change may improve a movie’s performance in the Chinese market, but may hurt the box office revenues in other markets. Using a dataset of 501 movies released in 2011-2014, I estimate the effect of adding Chinese features on the box office revenues in the domestic market, the Chinese market, and the rest of the world. I find that adding Chinese features significantly improves the revenue in China, but has a negative and insignificant effect in the domestic and the international markets. If Chinese features were added to all 168 movies imported to China, on average, movies would suffer a net loss of $4.58 million. But as the Chinese market size grows, there could be a net gain of $12.10 million if the market size doubles, with 63% of movies benefiting from adding Chinese features. iii Table of Contents List of Figures vii List of Tables viii Acknowledgments xi Chapter 1 Audience v Critics: the Effect of Reviews on Box Office Revenue 1 1.1 Introduction . 1 1.2 Data . 5 1.2.1 Data Description . 5 1.2.2 Summary Statistics . 7 1.2.3 Preliminary Results . 11 1.3 Model and Estimation . 13 1.3.1 Learning from Critic & Audience Reviews . 13 1.3.2 Estimation . 18 1.3.3 Identification . 19 1.4 Results . 20 1.4.1 Preference Parameters . 20 1.4.2 Learning Parameters . 21 1.4.3 Robustness . 23 1.5 Counterfactuals . 24 1.5.1 Effect of Cold Opening . 24 1.5.2 Benefit from Audience Reviews . 26 1.6 Conclusion . 28 Chapter 2 Advertising under Learning from Expert Reviews 29 2.1 Introduction . 29 2.2 Data . 32 2.2.1 Industry Background . 33 2.2.2 Descriptive Statistics . 34 2.2.2.1 Advertising Expenditures . 34 2.2.2.2 Critic and Audience Reviews . 35 2.2.2.3 Box Office Performance . 36 2.2.2.4 Movie Characteristics . 37 iv 2.2.3 Evidence of Consumer Learning . 38 2.3 Model . 40 2.3.1 Timing . 40 2.3.2 Demand . 41 2.3.3 Supply . 42 2.3.4 Consumer Learning . 43 2.3.4.1 Opening Week . 44 2.3.4.2 Post-release Period . 46 2.3.5 Studio Learning . 46 2.4 Estimation . 48 2.4.1 Parameterization . 48 2.4.1.1 Weight on Signals . 48 2.4.1.2 Rescaling Reviews . 48 2.4.2 Demand Estimation . 49 2.4.2.1 Identification . 49 2.4.2.2 Instrumental Variables . 50 2.4.2.3 Estimation Strategy . 51 2.4.3 Advertising Policy Function . 52 2.5 Results . 52 2.5.1 Preference Parameters . 53 2.5.2 Learning Parameters . 54 2.5.3 Advertising Policy Function . 56 2.5.4 Robustness . 57 2.6 Counterfactuals . 58 2.6.1 Effect of Reviews on Consumer Choice . 58 2.6.2 Effect of Reviews on Studio Choice . 60 2.6.2.1 Mandatory Screening By Critics . 60 2.6.2.2 Removing Review Aggregators . 62 2.7 Conclusion . 64 Chapter 3 Hollywood’s Response to the Growing Chinese Movie Market 66 3.1 Introduction . 66 3.2 Data . 69 3.2.1 Chinese Features . 69 3.2.2 Box Office Revenues . 70 3.2.3 Movie Characteristics . 72 3.3 Estimation . 74 3.3.1 Domestic Market . 74 3.3.2 Chinese Market . 75 3.3.3 International Market . 75 3.3.4 Identification . 76 3.4 Results . 76 3.4.1 Effect of Chinese Features . 76 3.4.2 Other Movie Characteristics . 78 3.4.3 Robustness Tests . 78 3.5 Counterfactuals . 79 v 3.5.1 Removing Chinese Features . 80 3.5.2 Adding Chinese Features . 81 3.5.3 Varying Market Size . 81 3.6 Conclusion . 82 Appendix A Data Collection 84 A.1 Dataset for Reviews and Advertising . 84 A.1.1 Box Office Performance . 84 A.1.2 Collecting Online Reviews . 86 A.1.3 Movie Characteristics . 89 A.1.4 Advertising Expenditures . 93 A.2 Dataset for Chinese Features . 94 A.2.1 Movie Selection . 94 A.2.2 Movie Characteristics . 94 A.2.3 Chinese Features . 96 A.2.4 Box Office Revenues . 98 Appendix B Proofs 101 B.1 Learning Process . 101 B.2 Combining Prior and Private Signal . 103 Appendix C Robustness Tests 105 C.1 Chapter 1 Robustness Tests . ..