International Movie Revenues: Determinants and Impact of the Financial Crisis

Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Empirical Project Assignment Econometrics II repayable on Friday, 13 January 2012, 11. 00 planetary plastic film revenues determinants and impact of the financial crisis Marek Kre? mer, Jan Mati? ka c c international impression revenues Determinants and impact of the ? nancial crisis plank of Contents Abstract Keywords incoming Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis variables implementd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . beat 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i mpersonate 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results puzzle 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . determine 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion References primary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secondary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . selective information sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Descriptive statistics for the seeent variables flummox 1 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted set diagram . . . . . Breusch-Pagan render for heteroskedasticity . precedent 2 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted set patch . . . . . . Breusch-Pagan rise for heteroskedasticity . The correlation matrix . . . . . . . . . . . . 2 2 2 2 3 3 4 4 4 4 6 6 6 7 8 8 8 8 9 9 10 11 11 12 13 13 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marek Kre? mer, Jan Mati? ka c c Page 1 of 14 International picture show revenues Determinants and impact of the ? nancial crisis Abstract This empirical project examines the determinants of international encase o? ce revenues for cinemas produced in United States during 2006 2010. Our ingest consists of 424 ? lms released in this period. We also test the hypothesis if the world ? nancial crisis had any signi? cant impact on the international incase o? ce revenues. Keywords the ? ancial crisis, photo international box o? ce revenue, mental pictures produced in the United States, budget, rating, Academy Awards, conception When choosing a topic of our empirical story we were considering di? erent suggestions. As we both are pretty much interested in movies we ? nally decided to exit a viewer seat for a while and fulfil an empirical study on the movie industry. While being newcommers in sophisticated movie information analysis, we needed ? rst to get acquainted with important conjectural concepts and empirical topics concerning this topic. Literature survey When going down the history, Litman, 1983 was the ? st who has attempted to predict the ? nancial success of ? lms. He has transacted a multiple regression and found a clear evidence that various in wagerent variables cede a signi? cant and serious in? uence on the ? n al success of a movie. Litemans work has been gradually acquire developed, Faber & OGuinn, 1984 well-tried the in? uence of ? lm advertising. They proved, that movie critics and word-of-mouth are less important then movie previews and excerpts when explaininng movie succes after going on public. Eliashberg & Shugan, 1997 explored the impact of restricted-rating labeled movies on their box o? e performance. Terry, Butler & DeArmond, 2004 analysed the determinants of movie video rental revenue, ? nding Academy Award nominations as the dominant factor. King, 2007 fol humiliateded their research and intaked U. S. movie data to ? nd the connection between the criticism and box o? ce earnings Many an new(prenominal)(prenominal) authors has extended the sign work of Litman, 1983, but none of them has focused on the key factors of the international box o? ce revenues as we planned to. So we ? nally decided to use Terry, Cooley & Zachary, 2010 as our primary source. Their object of in terest is very much similar to our resarch. and so we studied their metodology the most and we use their results in the analytical part as a primary resource of comparison. Marek Kre? mer, Jan Mati? ka c c Page 2 of 14 International movie revenues Determinants and impact of the ? nancial crisis Data We got quickly stucked realising that the strong majority of movie data on the internet are not free available. It was quite a surprise because there are many movie-oriented land sends with seemingly endless data access. But when there is a need of much than than profound, well structured and complete set of random data everything gets little bit tricky.After hours of searching, we luckily got to a 30 days free access to this kind of databases opusdata. com and got the core data for our analysis. Then we wanted to add some interesting or usefull variables just as the movie rating or the number of AcademyAwards to complete our dataset. It has been done using well known and free acce ssed databases imdb. com, numbers. com and boxo? cemojo. com. Thanks to our literature survey we discovered a model which we suck thought would be interesting to test on di? erent or new data. The most interesting would be to test it on our home(prenominal) data but these are quite di? ult to obtain (as explained before). Anyway, it would be feasible to get data for the highest grossing ? lms but that would violate the assumption of random strain. indeed we decided to use data from U. S. and Canada which we considered the most probable to obtain. We also wanted to test whether the ? nancial crisis have had an impact on movie box o? ce revenues and whether the world ? nancial crisis made people less probable to go to the cinema. Model We considered several models and in the end we used 2 models. The ? rst one is just the corresponding as the one used in paper Terry, Cooley & Zachary, 2010, but it is slightly modi? d by using di? erent data plus setting the crisis variable. W e considered it as a dummy variable, which was 1 if the movie was released during crisis (2008-2009), otherwise it is equal to zero. As it was proposed before, this model has been used as a comparison to the original model Terry, Cooley & Zachary, 2010 wihle we wanted to test whether their inference holds up with slightly di? erent and newer data. In the second model we tried to use a slightly di? erent approach. We used a time series model with year dummies and we also used all the variables which we obtained and were statistically signi? ant. Our ? rst model is basic linear regression with cross-sectional data. Our data are a random sample thanks to opusdata. com query which was capable of selecting a random sample of movies. We have well-tried all the variables for multicollinearity with the correlation matrix and there is no proof for multicollinearity in our used variables. The only high collinearity is between home(prenominal) and budget variables, which is almost 0. 75. Af ter running the regressions we have used the Breusch-Pagan test for heteroscedasticity and the chi squared was rightfully high therefore showing signs of strong heteroscedasticity.Even after looking at the graph of residuals against ? tted values it was clear that the heteroscedasticity is present. Therefore we had to run the regressions with the heteroscedasticity robust errors. We therefore tested in both models for presence of these the variables which have an impact on movie international box revenues any signi? cant impact of ? nancial crisis on these revenues Marek Kre? mer, Jan Mati? ka c c Page 3 of 14 International movie revenues Determinants and impact of the ? nancial crisis Data analysis Here we list all the used variables in both models and their a description. ariables used academy awards . . . . . . . . . number of Academy Awards a ? lm earned action . . . . . . . . . . . . . . . . . . level variable for movies in action literary musical style animation . . . . . . . . . . . . . . . savorless variable for movies in animation production method budget . . . . . . . . . . . . . . . . . . the estimated production and promotion cost of a movie comedy . . . . . . . . . . . . . . . . . . matt variable for movies in comedy genre crisis . . . . . . . . . . . . . . . . . . dummy variable for movies released during crisis domestic . . . . . . . . . . . . . . . omestic box o? ce earnings horror . . . . . . . . . . . . . . . . . . monotonous variable for movies in horror genre international . . . . . . . . . . . . international box o? ce earnings kids . . . . . . . . . . . . . . . . . . categorical variable for movies for children rating . . . . . . . . . . . . . . . . . . average user rating from the imdb. com source ratingR . . . . . . . . . . . . . . . . . . is a categorical variable for movies with a restricted rating amatory . . . . . . . . . . . . . . . . . . categorical variable for movies in wild-eyed genre sequel . . . . . . . . . . . . . . . . . categorical variable for movies derived from a antecedently released ? lm y06 ? y10 . . . . . . . . . . . . . . . . . . dummy variable for movies released in a year The list of variables is fol execrableed by both model equations and reggression table comparism, while model 1 and model 2 mean the original Terry, Cooley & Zachary, 2010 model and our new model respectivelly. model 1 international = ? 0 + ? 1 domestic + ? 2 action + ? 3 kids + ? 4 ratingR+ + ? 5 sequel + ? 6 rating + ? 7 academy awards + ? 8 budget + ? 9 crisis model 2 international = + + ? 0 + ? 1 academy awards + ? 2 budget + ? 3 domestic + ? 4 sequel + ? horror + ? 6 romantic + ? 7 comedy + ? 8 action + ? 9 ratingR + ? 10 animation + ? 11 y06 + ? 12 y07 + ? 13 y08 + ? 14 y09 Marek Kre? mer, Jan Mati? ka c c Page 4 of 14 International movie revenues Determinants and impact of the ? nancial crisis knock back 1 Model comparison model 1 domestic action kids rating R sequel rating academy awards budget crisi s horror romantic comedy animation y 06 y 07 y 08 y 09 Constant Observations t statistics in parentheses ? model 2 1. 025 (13. 31) -18. 56? (-2. 29) 1. 028 (12. 70) -13. 43 (-1. 79) 48. 33? (2. 10) 5. 922 (1. 52) 26. 91? (2. 06) 0. 309 (1. 42) 6. 978? (2. 33) 0. 68 (5. 48) -5. 320 (-1. 01) 9. 259? (2. 36) 28. 74? (2. 16) 7. 097 (2. 59) 0. 508 (4. 73) -9. 867? (-2. 23) 13. 41 (1. 79) -17. 77 (-3. 31) 52. 02 (2. 87) -7. 962 (-1. 24) 1. 182 (0. 17) -6. 748 (-1. 01) -11. 79 (-1. 30) -43. 25 (-3. 05) 424 -15. 11? (-2. 41) 424 p 0. 05, p 0. 01, p 0. 001 Marek Kre? mer, Jan Mati? ka c c Page 5 of 14 International movie revenues Determinants and impact of the ? nancial crisis Results model 1 After running the ? rst regression we get quite similar results as Terry, Cooley & Zachary, 2010, so their inference holds up even under our data.The similar results we get are that one dollar in revenues in US makes $1. 02 in international revenues, therefore succesful movie in US is likely to be similarly succesful in international theatres, if movie is a sequel it adds to revenues about(predicate) $26 mil. , every academy award adds about $7 mil. and every additional dollar spent on budget adds about $0. 57 so there is about 57% return on budget. We also have similarly insigni? cant variables which are whether is movie rated as restricted and how great or poorly is movie rated by critics or other people.That means that international audience is not in? uenced by age restrictions and critical movie ratings. When we look at our and theirs results regarding the genres then we get quite di? erent results. They recount that when a movie is of an action genre then it adds about $26 mil. whereas we obtained results that revenues for an action movie should be lower about $13 mil. and our result for children movies is ii times larger and it assigns that a children movie should make about $48 mil. more. It could be explained that movie genre preferences shifted in the last two years.But more likely explanation is the di? erence in our data in labeling the movies. In our data we have had more detailed labeling and movies which they had labeled as action movies, we had labeled adventure movies and so on Therefore the strictly action movie genre is not so probable to make money as it would seem. Action movies are usually of low quality and many of them could be labeled as B-movies which usually are not very likely to have high revenues. The children movies could be acquiring more popular and taking children to the movies could be getting more usual thing.Our last and new variable is the crisis dummy which is not signi? cant and therefore we have no proof that the ? nancial crisis had any e? ect on movie revenues. Our model has quite high R2 which is about 0. 83, that is even higher then Terry, Cooley & Zachary, 2010 have. But the briny reason behind this high R2 is that most of the variation in data is explained by US revenues. If we regress international revenues on domestic alone we hushed get high R2 which is about 0. 59. model 2 In our time series model we get quite similar results as in the ? rst one. We have there ? e new variables which are genres comedy, romantic and horror, animation dummy, which tells us whether the movie is animated or not and year dummies. Our model implies that when a movie is a comedy it will make about $17 mil. less in revenues, when horror about $10 mil. less, when romantic about $13 mil. more and when animated it will add about $52 mil to its revenues. The restricted rating is now also statistically signi? cant and it should add to the revenues about $9 mil. which is quite unexpected. Y ear dummies are statistically non-signi? cant and even when we test them for joint signi? ance they are jointly non-signi? cant. Therefore even in this model there appears no reason to believe that the ? nancial crisis or even year makes di? erence in the movie revenues. Marek Kre? mer, Jan Mati? ka c c Page 6 of 14 International movie revenues Determinants and impact of the ? nancial crisis Conclusion The inferences from our models are quite like we expected. We expected that people are more likely to go to cinema to see movies that had won academy awards, that were succesful in U. S. theatres and that are some kind of sequel to previous succesful movies. The resulting e? cts of di? erent movie genres could be quite puzzling but these e? ects depend highly on quality of the movies released these years and on the mood and taste of current society. If we had had larger sample with data from many years then it is mathematical that we would have seen trends in the di? erent movie genres. The insigni? cance of the ? nancial crisis on movie revenues was also likely because the severity of the crisis and impact on regular citizen has not been so large that it would in? uence his attendence of movie theatres. Marek Kre? mer, Jan Mati? ka c c Page 7 of 14International movie revenues Determinants an d impact of the ? nancial crisis Reference primary Terry, Cooley & Zachary, 2010 Terry, Neil, John W. Cooley, & Miles Zachary (2010). The Determinants of Foreign Box O? ce revenue enhancement for English Language Movies. Journal of International Business and Cultural Studies, 2 (1), 117-127. secondary Eliashberg & Shugan, 1997 Eliashberg, Jehoshua & Steven M. Shugan (1997). choose Critics In? uencers or Predictors? Journal of Marketing, 61, 68-78. Faber & OGuinn, 1984 Faber, Ronald & Thomas OGuinn (1984). E? ect of Media Advertising and Other Sources on Movie Selection.Journalism Quarterly, 61 (summer), 371-377. King, 2007 King, Timothy (2007). Does ? lm criticism a? ect box o? ce earnings? Evidence from movies released in the U. S. in 2003. Journal of Cultural Economics, 31, 171-186. Litman, 1983 Litman, Barry R. (1983). Predicting Success of Theatrical Movies An Empirical Study. Journal of Popular Culture, 16 (spring), 159-175. Ravid, 1999 Ravid, S. Abraham (1999). Information, Blockbusters, and Stars A Study of the claim Industry. Journal of Business, 72 (4), 463-492. Terry, Butler & DeArmond, 2004 Terry, Neil, Michael Butler & DeArno DeArmond (2004).The Economic Impact of Movie Critics on Box O? ce Performance. Academy of Marketing Studies Journal, 8 (1), summon 61-73. data sources opusdata. com Opus data movie data through a query interface. 30-days free trial. http//www. opusdata. com/ imdb. com The Internet Movie Database (IMDb). The biggest, best, most award-winning movie site on the planet. http//www. imdb. com numbers. com The numbers. Box o? ce data, movies stars, idle speculation. http//www. the-numbers. com boxo? cemojo. com Box o? ce mojo. Movie web site with the most comprehensive box o? ce database on the Internet. ttp//www. boxofficemojo. com Marek Kre? mer, Jan Mati? ka c c Page 8 of 14 International movie revenues Determinants and impact of the ? nancial crisis Appendix Descriptive statistics for the dependent variables Marek Kre? mer, Jan Mati? ka c c Page 9 of 14 International movie revenues Determinants and impact of the ? nancial crisis model 1 Regression of the original model published in Terry, Cooley & Zachary, 2010 Marek Kre? mer, Jan Mati? ka c c Page 10 of 14 International movie revenues Determinants and impact of the ? nancial crisis Residuals versus ? tted values plotBreusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 11 of 14 International movie revenues Determinants and impact of the ? nancial crisis model 2 Regression of our model Marek Kre? mer, Jan Mati? ka c c Page 12 of 14 International movie revenues Determinants and impact of the ? nancial crisis Residuals versus ? tted values plot Breusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 13 of 14 International movie revenues Determinants and impact of the ? nancial crisis The correlation matrix Marek Kre? mer, Jan Mati? ka c c Page 14 of 14International Movie revenue enhancements Determ inants and Impact of the Financial CrisisInstitute of Economic Studies Faculty of Social Sciences Charles University in Prague Empirical Project Assignment Econometrics II callable on Friday, 13 January 2012, 11. 00 International movie revenues determinants and impact of the financial crisis Marek Kre? mer, Jan Mati? ka c c International movie revenues Determinants and impact of the ? nancial crisis Table of Contents Abstract Keywords Introduction Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis variables used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion References primary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . secondary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Descriptive statistics for the dependent variables model 1 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted values plot . . . . . Breusch-Pagan test for heteroskedasticity . model 2 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted valu es plot . . . . . . Breusch-Pagan test for heteroskedasticity . The correlation matrix . . . . . . . . . . . . 2 2 2 2 3 3 4 4 4 4 6 6 6 7 8 8 8 8 9 9 10 11 11 12 13 13 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marek Kre? mer, Jan Mati? ka c c Page 1 of 14 International movie revenues Determinants and impact of the ? nancial crisis Abstract This empirical project examines the determinants of international box o? ce revenues for movies produced in United States during 2006 2010. Our sample consists of 424 ? lms released in this period. We also test the hypothesis if the world ? nanc ial crisis had any signi? cant impact on the international box o? ce revenues. Keywords the ? ancial crisis, movie international box o? ce revenue, movies produced in the United States, budget, rating, Academy Awards, Introduction When choosing a topic of our empirical paper we were considering di? erent suggestions. As we both are pretty much interested in movies we ? nally decided to exit a viewer seat for a while and perform an empirical study on the movie industry. While being newcommers in sophisticated movie data analysis, we needed ? rst to get acquainted with important conjectural concepts and empirical papers concerning this topic. Literature survey When going down the history, Litman, 1983 was the ? st who has attempted to predict the ? nancial success of ? lms. He has performed a multiple regression and found a clear evidence that various independent variables have a signi? cant and serious in? uence on the ? nal success of a movie. Litemans work has been gradually getti ng developed, Faber & OGuinn, 1984 tested the in? uence of ? lm advertising. They proved, that movie critics and word-of-mouth are less important then movie previews and excerpts when explaininng movie succes after going on public. Eliashberg & Shugan, 1997 explored the impact of restricted-rating labeled movies on their box o? e performance. Terry, Butler & DeArmond, 2004 analysed the determinants of movie video rental revenue, ? nding Academy Award nominations as the dominant factor. King, 2007 followed their research and used U. S. movie data to ? nd the connection between the criticism and box o? ce earnings Many other authors has extended the sign work of Litman, 1983, but none of them has focused on the key factors of the international box o? ce revenues as we planned to. So we ? nally decided to use Terry, Cooley & Zachary, 2010 as our primary source. Their object of interest is very much similar to our resarch.Therefore we studied their metodology the most and we use their results in the analytical part as a primary resource of comparison. Marek Kre? mer, Jan Mati? ka c c Page 2 of 14 International movie revenues Determinants and impact of the ? nancial crisis Data We got quickly stucked realising that the strong majority of movie data on the internet are not free available. It was quite a surprise because there are many movie-oriented sites with seemingly endless data access. But when there is a need of more profound, well structured and complete set of random data everything gets little bit tricky.After hours of searching, we luckily got to a 30 days free access to this kind of databases opusdata. com and got the core data for our analysis. Then we wanted to add some interesting or usefull variables just as the movie rating or the number of AcademyAwards to complete our dataset. It has been done using well known and free accessed databases imdb. com, numbers. com and boxo? cemojo. com. Thanks to our literature survey we discovered a model which we have thought would be interesting to test on di? erent or new data. The most interesting would be to test it on our domestic data but these are quite di? ult to obtain (as explained before). Anyway, it would be possible to get data for the highest grossing ? lms but that would violate the assumption of random sample. Therefore we decided to use data from U. S. and Canada which we considered the most likely to obtain. We also wanted to test whether the ? nancial crisis have had an impact on movie box o? ce revenues and whether the world ? nancial crisis made people less likely to go to the cinema. Model We considered several models and in the end we used two models. The ? rst one is just the said(prenominal) as the one used in paper Terry, Cooley & Zachary, 2010, but it is slightly modi? d by using di? erent data plus setting the crisis variable. We considered it as a dummy variable, which was 1 if the movie was released during crisis (2008-2009), otherwise it is equal to zero. As i t was proposed before, this model has been used as a comparison to the original model Terry, Cooley & Zachary, 2010 wihle we wanted to test whether their inference holds up with slightly di? erent and newer data. In the second model we tried to use a slightly di? erent approach. We used a time series model with year dummies and we also used all the variables which we obtained and were statistically signi? ant. Our ? rst model is basic linear regression with cross-sectional data. Our data are a random sample thanks to opusdata. com query which was capable of selecting a random sample of movies. We have tested all the variables for multicollinearity with the correlation matrix and there is no proof for multicollinearity in our used variables. The only high collinearity is between domestic and budget variables, which is about 0. 75. After running the regressions we have used the Breusch-Pagan test for heteroscedasticity and the chi squared was actually high therefore showing signs of strong heteroscedasticity.Even after looking at the graph of residuals against ? tted values it was clear that the heteroscedasticity is present. Therefore we had to run the regressions with the heteroscedasticity robust errors. We therefore tested in both models for presence of these the variables which have an impact on movie international box revenues any signi? cant impact of ? nancial crisis on these revenues Marek Kre? mer, Jan Mati? ka c c Page 3 of 14 International movie revenues Determinants and impact of the ? nancial crisis Data analysis Here we list all the used variables in both models and their a description. ariables used academy awards . . . . . . . . . number of Academy Awards a ? lm earned action . . . . . . . . . . . . . . . . . . categorical variable for movies in action genre animation . . . . . . . . . . . . . . . categorical variable for movies in animation production method budget . . . . . . . . . . . . . . . . . . the estimated production and promotion c ost of a movie comedy . . . . . . . . . . . . . . . . . . categorical variable for movies in comedy genre crisis . . . . . . . . . . . . . . . . . . dummy variable for movies released during crisis domestic . . . . . . . . . . . . . . . omestic box o? ce earnings horror . . . . . . . . . . . . . . . . . . categorical variable for movies in horror genre international . . . . . . . . . . . . international box o? ce earnings kids . . . . . . . . . . . . . . . . . . categorical variable for movies for children rating . . . . . . . . . . . . . . . . . . average user rating from the imdb. com source ratingR . . . . . . . . . . . . . . . . . . is a categorical variable for movies with a restricted rating romantic . . . . . . . . . . . . . . . . . . categorical variable for movies in romantic genre sequel . . . . . . . . . . . . . . . . . categorical variable for movies derived from a previously released ? lm y06 ? y10 . . . . . . . . . . . . . . . . . . dummy variable for movies released i n a year The list of variables is followed by both model equations and reggression table comparism, while model 1 and model 2 mean the original Terry, Cooley & Zachary, 2010 model and our new model respectivelly. model 1 international = ? 0 + ? 1 domestic + ? 2 action + ? 3 kids + ? 4 ratingR+ + ? 5 sequel + ? 6 rating + ? 7 academy awards + ? 8 budget + ? 9 crisis model 2 international = + + ? 0 + ? 1 academy awards + ? 2 budget + ? 3 domestic + ? 4 sequel + ? horror + ? 6 romantic + ? 7 comedy + ? 8 action + ? 9 ratingR + ? 10 animation + ? 11 y06 + ? 12 y07 + ? 13 y08 + ? 14 y09 Marek Kre? mer, Jan Mati? ka c c Page 4 of 14 International movie revenues Determinants and impact of the ? nancial crisis Table 1 Model comparison model 1 domestic action kids rating R sequel rating academy awards budget crisis horror romantic comedy animation y 06 y 07 y 08 y 09 Constant Observations t statistics in parentheses ? model 2 1. 025 (13. 31) -18. 56? (-2. 29) 1. 028 (12. 70) -13. 43 (-1. 79 ) 48. 33? (2. 10) 5. 922 (1. 52) 26. 91? (2. 06) 0. 309 (1. 42) 6. 978? (2. 33) 0. 68 (5. 48) -5. 320 (-1. 01) 9. 259? (2. 36) 28. 74? (2. 16) 7. 097 (2. 59) 0. 508 (4. 73) -9. 867? (-2. 23) 13. 41 (1. 79) -17. 77 (-3. 31) 52. 02 (2. 87) -7. 962 (-1. 24) 1. 182 (0. 17) -6. 748 (-1. 01) -11. 79 (-1. 30) -43. 25 (-3. 05) 424 -15. 11? (-2. 41) 424 p 0. 05, p 0. 01, p 0. 001 Marek Kre? mer, Jan Mati? ka c c Page 5 of 14 International movie revenues Determinants and impact of the ? nancial crisis Results model 1 After running the ? rst regression we get quite similar results as Terry, Cooley & Zachary, 2010, so their inference holds up even under our data.The similar results we get are that one dollar in revenues in US makes $1. 02 in international revenues, therefore succesful movie in US is likely to be similarly succesful in international theatres, if movie is a sequel it adds to revenues about $26 mil. , every academy award adds about $7 mil. and every additional dollar spent o n budget adds about $0. 57 so there is about 57% return on budget. We also have similarly insigni? cant variables which are whether is movie rated as restricted and how great or poorly is movie rated by critics or other people.That means that international audience is not in? uenced by age restrictions and critical movie ratings. When we look at our and theirs results regarding the genres then we get quite di? erent results. They say that when a movie is of an action genre then it adds about $26 mil. whereas we obtained results that revenues for an action movie should be lower about $13 mil. and our result for children movies is two times larger and it says that a children movie should make about $48 mil. more. It could be explained that movie genre preferences shifted in the last two years.But more likely explanation is the di? erence in our data in labeling the movies. In our data we have had more detailed labeling and movies which they had labeled as action movies, we had labeled adventure movies and so on Therefore the strictly action movie genre is not so probable to make money as it would seem. Action movies are usually of low quality and many of them could be labeled as B-movies which usually are not very likely to have high revenues. The children movies could be getting more popular and taking children to the movies could be getting more usual thing.Our last and new variable is the crisis dummy which is not signi? cant and therefore we have no proof that the ? nancial crisis had any e? ect on movie revenues. Our model has quite high R2 which is about 0. 83, that is even higher then Terry, Cooley & Zachary, 2010 have. But the main reason behind this high R2 is that most of the variation in data is explained by US revenues. If we regress international revenues on domestic alone we unsounded get high R2 which is about 0. 59. model 2 In our time series model we get quite similar results as in the ? rst one. We have there ? e new variables which are genre s comedy, romantic and horror, animation dummy, which tells us whether the movie is animated or not and year dummies. Our model implies that when a movie is a comedy it will make about $17 mil. less in revenues, when horror about $10 mil. less, when romantic about $13 mil. more and when animated it will add about $52 mil to its revenues. The restricted rating is now also statistically signi? cant and it should add to the revenues about $9 mil. which is quite unexpected. Y ear dummies are statistically non-signi? cant and even when we test them for joint signi? ance they are jointly non-signi? cant. Therefore even in this model there appears no reason to believe that the ? nancial crisis or even year makes di? erence in the movie revenues. Marek Kre? mer, Jan Mati? ka c c Page 6 of 14 International movie revenues Determinants and impact of the ? nancial crisis Conclusion The inferences from our models are quite like we expected. We expected that people are more likely to go to cinem a to see movies that had won academy awards, that were succesful in U. S. theatres and that are some kind of sequel to previous succesful movies. The resulting e? cts of di? erent movie genres could be quite puzzling but these e? ects depend highly on quality of the movies released these years and on the mood and taste of current society. If we had had larger sample with data from many years then it is possible that we would have seen trends in the di? erent movie genres. The insigni? cance of the ? nancial crisis on movie revenues was also likely because the severity of the crisis and impact on regular citizen has not been so large that it would in? uence his attendence of movie theatres. Marek Kre? mer, Jan Mati? ka c c Page 7 of 14International movie revenues Determinants and impact of the ? nancial crisis Reference primary Terry, Cooley & Zachary, 2010 Terry, Neil, John W. Cooley, & Miles Zachary (2010). The Determinants of Foreign Box O? ce Revenue for English Language Movies. Journal of International Business and Cultural Studies, 2 (1), 117-127. secondary Eliashberg & Shugan, 1997 Eliashberg, Jehoshua & Steven M. Shugan (1997). Film Critics In? uencers or Predictors? Journal of Marketing, 61, 68-78. Faber & OGuinn, 1984 Faber, Ronald & Thomas OGuinn (1984). E? ect of Media Advertising and Other Sources on Movie Selection.Journalism Quarterly, 61 (summer), 371-377. King, 2007 King, Timothy (2007). Does ? lm criticism a? ect box o? ce earnings? Evidence from movies released in the U. S. in 2003. Journal of Cultural Economics, 31, 171-186. Litman, 1983 Litman, Barry R. (1983). Predicting Success of Theatrical Movies An Empirical Study. Journal of Popular Culture, 16 (spring), 159-175. Ravid, 1999 Ravid, S. Abraham (1999). Information, Blockbusters, and Stars A Study of the Film Industry. Journal of Business, 72 (4), 463-492. Terry, Butler & DeArmond, 2004 Terry, Neil, Michael Butler & DeArno DeArmond (2004).The Economic Impact of Movie Critics on Box O? c e Performance. Academy of Marketing Studies Journal, 8 (1), scallywag 61-73. data sources opusdata. com Opus data movie data through a query interface. 30-days free trial. http//www. opusdata. com/ imdb. com The Internet Movie Database (IMDb). The biggest, best, most award-winning movie site on the planet. http//www. imdb. com numbers. com The numbers. Box o? ce data, movies stars, idle speculation. http//www. the-numbers. com boxo? cemojo. com Box o? ce mojo. Movie web site with the most comprehensive box o? ce database on the Internet. ttp//www. boxofficemojo. com Marek Kre? mer, Jan Mati? ka c c Page 8 of 14 International movie revenues Determinants and impact of the ? nancial crisis Appendix Descriptive statistics for the dependent variables Marek Kre? mer, Jan Mati? ka c c Page 9 of 14 International movie revenues Determinants and impact of the ? nancial crisis model 1 Regression of the original model published in Terry, Cooley & Zachary, 2010 Marek Kre? mer, Jan Mati? ka c c Page 10 of 14 International movie revenues Determinants and impact of the ? nancial crisis Residuals versus ? tted values plotBreusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 11 of 14 International movie revenues Determinants and impact of the ? nancial crisis model 2 Regression of our model Marek Kre? mer, Jan Mati? ka c c Page 12 of 14 International movie revenues Determinants and impact of the ? nancial crisis Residuals versus ? tted values plot Breusch-Pagan test for heteroskedasticity Marek Kre? mer, Jan Mati? ka c c Page 13 of 14 International movie revenues Determinants and impact of the ? nancial crisis The correlation matrix Marek Kre? mer, Jan Mati? ka c c Page 14 of 14