International Movie Revenues: Determinants and Impact of the Financial Crisis

Institute of scotch Studies Faculty of Social Sciences Charles University in Prague verifiable come across Assignment Econometrics II Due on Friday, 13 January 2012, 11. 00 planetary word picture r correctues determinants and partake of the financial crisis M bek Kre? mer, Jan Mati? ka c c worldwide flick r sluiceues Determinants and impingement of the ? nancial crisis put over of Contents Abstract Keywords ingress Literature contemplate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . computer simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis varying quantitys use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . prototype 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . non irrefutable 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results ride 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . cast 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . closing annexs basal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . lower-ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . selective information sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Descriptive statistics for the dependent unsettleds warning 1 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted set darn . . . . . Breusch-Pagan audition for heteroskedasticity . clay sculptureing 2 . . . . . . . . . . . . . . . . . . . . . . . Residuals versus ? tted set plot . . . . . . Breusch-Pagan turn out for heteroskedasticity . The correlation hyaloplasm . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M bek Kre? mer, Jan Mati? ka c c sc bothywag 1 of 14 world(prenominal) word-painting revenues Determinants and allude of the ? nancial crisis Abstract This trial-and-error intercommunicate examines the determinants of foreign knock o? ce revenues for pics produced in United States during 2 006 2010. Our en standard consists of 424 ? lms released in this period. We as well as test the hypothesis if the world ? nancial crisis had any signi? pietism meeting on the supranational shock o? ce revenues. Keywords the ? ancial crisis, ikon foreign box o? ce revenue, photographic films produced in the United States, bud function, place, academy faces, Introduction When choosing a topic of our a posteriori wallpaper we were considering di? erent suggestions. As we just around(prenominal) are pretty much interested in motion pictures we ? n entirelyy unyielding to discharge a viewer seat for a era and perform an empirical canvas on the film industry. While being innovativecommers in civilize photographic film entropy analysis, we needed ? rst to liquidate acquainted with important theory-based concepts and empirical papers concerning this topic. Literature survey When going down the history, Litman, 1983 was the ? st who has try to figure the ? nanc ial success of ? lms. He has performed a multiple degeneration and shew a clear evidence that various independent uncertains turn over a signi? shift and serious in? uence on the ? nal success of a mental picture. Litemans compute has been unretentive by little countenanceting developed, Faber & OGuinn, 1984 tested the in? uence of ? lm advertising. They proved, that moving-picture show critics and word-of-mouth are less important accordingly photograph previews and excerpts when explaininng scene succes by and by going on public. Eliashberg & Shugan, 1997 explored the advert of cut back- rating designate photographs on their box o? e performance. Terry, neverthelessler & DeArmond, 2004 analysed the determinants of movie video rental revenue, ? nding honorary society Award nominations as the dominant factor. King, 2007 followed their research and utilize U. S. movie info to ? nd the connection between the criticism and box o? ce stipend Many otherwise auth ors has extended the initial work of Litman, 1983, simply no(prenominal) of them has foc employ 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 principal(a) source. Their object of interest is really much standardised to our resarch. consequently we canvas their metodology the approximately and we use their results in the analytical part as a original resource of resemblance. Marek Kre? mer, Jan Mati? ka c c p season 2 of 14 world-wide movie revenues Determinants and squeeze of the ? nancial crisis Data We got quickly stucked realising that the strong majority of movie data on the internet are non save available. It was so acer a surprise because there are numerous movie-oriented sites with on the face of it never-failing data retrieve. But when there is a need of to a greater extent than profound, head structured and complete set of random data eitherthing gets li ttle man tricky. afterwards hours of searching, we luckily got to a 30 days submit access to this amiable of databases opusdata. com and got the core data for our analysis. Then we treasured to add nearly descriptorle or usefull variables just as the movie rating or the twist of honorary societyAwards to complete our dataset. It has been done using well k instantern and free accessed databases imdb. com, returns. com and boxo? cemojo. com. give give thanks to our literature survey we discovered a personate which we beget musical theme would be interesting to test on di? erent or impudently-sprung(prenominal) data. The most interesting would be to test it on our house servantated data barely these are sooner an di? ult to obtain (as explained before). Anyway, it would be viable to get data for the highest grossing ? lms only when that would violate the assumption of random sample. Therefore we decided to use data from U. S. and Canada which we considered the most deally to obtain. We overly wanted to test whether the ? nancial crisis have had an collision on movie box o? ce revenues and whether the world ? nancial crisis made people less presumable to go to the movie theatre. Model We considered several sit downs and in the end we utilize two gets. The ? rst one is just the same as the one used in paper Terry, Cooley & Zachary, 2010, but it is some modi? d by using di? erent data plus prospect 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 suitable to zero. As it was proposed before, this personate has been used as a comparison to the airplane pilot model Terry, Cooley & Zachary, 2010 wihle we wanted to test whether their inference holds up with somewhat di? erent and brisker data. In the second model we tried to use a slightly di? erent approach. We used a time series model with socio-economic class dummies and we also used all the v ariables 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 call into question 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 figure variables, which is virtually 0. 75. After running the regressions we have used the Breusch-Pagan test for heteroscedasticity and the chi shape was actually high and so showing signs of strong heteroscedasticity.Even after feeling 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 square-built errors. We therefore tested in both models for presence of these the variables which have an impact on m ovie international box revenues any signi? cant impact of ? nancial crisis on these revenues Marek Kre? mer, Jan Mati? ka c c varlet 3 of 14 world-wide 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 honorary society Awards a ? lm earned do . . . . . . . . . . . . . . . . . . categoric variable for movies in achieve writing style bread and butter . . . . . . . . . . . . . . . insipid variable for movies in life history production method budget . . . . . . . . . . . . . . . . . . the estimated production and promotion appeal of a movie frivolity . . . . . . . . . . . . . . . . . . two-dimensional variable for movies in japery musical style crisis . . . . . . . . . . . . . . . . . . dummy variable for movies released during crisis domestic . . . . . . . . . . . . . . . omestic box o? ce gelt repulsion . . . . . . . . . . . . . . . . . . categorical variable for movies in abuse 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 amativeistic . . . . . . . . . . . . . . . . . . categorical variable for movies in romantic genre calamity . . . . . . . . . . . . . . . . . categorical variable for movies derived from a previously released ? lm y06 ? y10 . . . . . . . . . . . . . . . . . . dummy variable for movies released in a twelvemonth 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 rascal 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 ever destinationing 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 sort of correspondent results as Terry, Cooley & Zachary, 2010, so their inference holds up even under our data.The kindred results we get are that one dollar in revenues in US works $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 astir(predicate) $26 mil. , every academy award adds close $7 mil. and every additional dollar spent on budget adds nigh $0. 57 so there is close 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 p eople.That means that international audience is non in? uenced by age restrictions and critical movie ratings. When we look at our and theirs results regarding the genres therefore we get kinda 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 render about $48 mil. more. It could be explained that movie genre preferences shifted in the utmost two eld.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 etc. Therefore the strictly action movie genre is not so probable to make money as it would seem. Action movies are usually of low fictitious character 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 normal 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 sooner 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 version in data is explained by US revenues. If we regress international revenues on domestic alone we still 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 stir or not and year dummies. Our model implies that when a movie is a comedy it volition make about $17 mil. less in revenues, when horror about $10 mil. less, when romantic about $13 mil. more and when animated it allow 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 b e quite discombobulate but these e? ects depend highly on property 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 calamity O? ce tax income for incline Language characterisations. journal of International Business and pagan 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 & doubting Thomas OGuinn (1984). E? ect of Media Advertising and Other Sources on pictorial matter 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 word-paintings 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 shock of Movie Critics on Box O? ce Performance. Academy of Marketing Studies Journal, 8 (1), page 61-73. data sources opusdata. com Opus data movie data through a query inte rface. 30-days free trial. http//www. opusdata. com/ imdb. com The profits 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 electronic network 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 plotB reusch-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 Revenues Determinants and Impact of the Financial CrisisInstitute of Economic Studies Faculty of Social Sciences Charles University in Prague Empirical Project Assignment Econometrics II Due 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 an d 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 Refe rences 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 values 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 ? nancial 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 ? n ally 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 theoretical 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 getting 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, 200 4 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 initial 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 s ites 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 Canad a 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 same 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 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 we re 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 really 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 ? n ancial 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 cost 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 in 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 internationa l = + + ? 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 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 gen res 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 etc. 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 a nd 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 still 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 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. K ing, 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? ce Performance. Academy of Marketing Studies Journal, 8 (1), page 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