We are publishing Ukrainian scientific work about movies distribution in Ukraine. According to our knowledge this is first research on this topic. This paper despite some limitation of the initial data raises important questions about principles and peculiarities of Ukrainian motion pictures market. The author, graduate of Kyiv School of Economics, uses big amount of international research papers about Hollywood economics and others countries movies markets. There are also interactive visualization of movies box office in Ukraine, Poland and Commonwealth of Independent States*.
The term “blockbuster” was originally a name of the largest conventional bomb used in World War II by UK Air Force. It had enough power to destroy an entire city block (Wikipedia). Later blockbuster was used to denote successful theatrical play. And in motion pictures industry it appeared after the nationwide release of “Jaws” in the United States of America in 1975. Since the blockbuster means “a motion picture which managed to accumulate high revenues”. For the US this means more than $100 million cumulatively in movie-theaters (Eliashberg, Elberse, Leenders 2005). Of course in Ukraine no movie has ever been able to hit such an amount. For example, the historical maximum is of about $8 million by “Avatar” in 2009. At the same time in Poland, which has 10 million people less than in Ukraine, “Avatar” has grossed about $26 million over the same period (http://boxofficemojo.com).
The motion picture industry plays a significant role not only in the cultural sphere but also in the economics. In the USA it is a major private-sector employer that supported 2.4 million high quality and high paying jobs, and over $140 billion in total wages in 2008 (Supplementary Report of MPAA April 2010). There are also positive externalities such as tourism, real estate and community development associated with it. Despite the fact that Ukrainian film-makers were playing an important role in former Soviet Union, nowadays national movie industry is almost dead – in 2009 there were produced only 13 Ukrainian films (Statistics of State Service of Cinematography) . Since Ukraine became independent, during the economic crises of the 90-ies, because of huge piracy industry, changes in social and business structure, movie production was neither government nor business priority. Local audience got access to “free” market and switched to Hollywood production. However, little by little the situation is changing. Hollywood competes with Russian movies which gained one third of the Ukrainian market by 2009 year (Working documents of International Conference Movie Industry in Ukraine 2010). To make impulse for development of national motion pictures industry, Ukrainian parliament passed a bill according which national producers and distributors of Ukrainian movies are granted a tax break for the next 5 years, effective already on January 1, 2011 (Amendments to the Law of Ukraine “On cinematography” and other laws of Ukraine 2010). Hence experts expect a faster growth of the movie industry in Ukraine (Gnativ 2010).
However, it is not enough to change legislation to make a successful movie. This could only bring the money into the industry. And would big money earn big profits? Can we refer to experience of other countries to address this question?
There are several economic theories about the determinants of success of movies. One is a “blockbuster strategy” suggesting that the public can be “herded” to a movie-theater. It states that motion picture audiences pick movies according to “how heavily they were advertised, whether star actors were engaged in them, and their revenues at the box-office tournament” (De Vany 2005). Another point of view is presented in the “lemming” theory, according to which quality matters. First, the public come to the opening screens influenced by the factors mentioned above or by so called non-informative cascade. Further spectators would make a decision is it worth to watch the movie or not, based on its’ quality characteristics (Lane & Husemann 2004). As measures of quality one could use critics' reviews in the media, awards at movie-festivals, word-of-mouth information that create informative cascade.
Most of the existing empirical studies about movie success were conducted using the US data and do not take into account some specific parameters for other territories, such as language, a country of production, presence of local actors, awards at local film-festivals, specific dates and events.
This study aims to check blockbuster hypothesis for Ukraine. However, Ukrainian motion pictures market is much different from US by foreign supply and much lower demand. To my knowledge there are no economic studies about motion picture industry of Post-communist countries. So for comparing Ukraine with similar markets, analysis is extended also to Commonwealth of Independent States (Azerbaijan, Armenia, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Uzbekistan) and Poland.
The main source of the data is publicly available internet database “Box Office Mojo” with information about budgets, weekend box offices, number of screens, date, and the cast of the movies nationally released in Poland, Commonwealth of Independent States* and Ukraine during 2007 to 2010 years. There are excluded screening of motion pictures on film festivals and noncommercial events. Information about Russian movies was additionally collected from kinopoisk.ru and about Polish - from Polish Film Institute pisf.pl.
A thesis for the degree of MA in Economics
Author: Yevgen Nasadyuk
Thesis Supervisor: Professor Vakhitov, Volodymyr
Kyiv School of Economics 2011
There are two main approaches to the explanation of movie’s success factors. One is the communication theory approach that studies why people prefer to see a particular movie contrary to other entertainment possibilities. Usually such data are collected by direct surveying moviegoers. Another way is the economic approach that uses “the money talks” principle and examines economic factors influenced collective movie attendance.
Particularly the object of interest is box office revenue of motion picture. As influenced variables such parameters are considered: production costs, genre, release date, marketing budget, distribution company, popularity of cast, if movie based on well-known story or sequel, number of screens, opening weekend, critique’s review, festivals awards, age rating. Some studies examine financial success in general without focusing on one particular variable. Others while using whole parameters are focusing on impact of a certain factor as star power or budget. This study provides review of both type studies for US and other available countries.
One of the earliest economic models (Lіtman 1983) assumes that motion picture box office success іs dependent оn three decision-making areas:
- Artistic (story, actors, director, production budget and rating);
- Release circumstances (release date, competition, number of screens);
- Marketing (advertisement, reviews, movie awards)
Later, Litman and Kohl (1989) used empirical model to predict the box office revenues for distributor from the following variables: genre, MPAA (Motion Picture Association of America) rating, sequels or based on well-known ideas, country of production, star power of actor or director, production budget, critical reviews, distributor, release date, number of opening screens, market forces. They found only few genres are significant - science-fiction, fantasy and drama, not significant MPAA rating. However its’ main result is that the star power variable is significant and highly correlated with financial success.
Latest most comprehensive book “Hollywood Economics” by Arthur De Vany (2004) covers a wide range of issues in movie industry economics, describing the motion picture market as a dynamic tournament as in sports where the champion gets lion’s share of revenues. The box office revenue distribution is found to follow nonlinear Bose-Einstein process and Pareto distribution. A presence of star-actors increases movie’s probability for higher revenue. An average big budgets lead to higher box offices. The author finds also positive effect of animated and action motion pictures for US domestic market.
However, there are also studies that attempt to determine other approaches of motion picture success. De Silva (1998) considers attendance of movie-theaters as a function of viewer’s demographic characteristics (e.g. age, marital status) and movie related variables: a director’s name, advertising, reviews. Eliashberg and Sawhney (1994) model the dynamics of movie consumption in terms of the determinants of enjoyment. Collins at el. (2003) analyzed repeated consumption of movie. They found that there is a small share of movie-theater visitors who may see the same picture more than once. This group is tended to be in the age 10 – 14 years old. The fact of “repeated attendance” is considered as a signal of movie’s success. Kai-Lung Hui and Ivan P.L. Png (2002) studied movie markets for 38 countries such as Canada, Hong Kong, Japan, the United Kingdom and the United States and found negative impact of TV ownership on attending movie theaters.
Movie industry papers apply not only OLS models, but also more complicated approaches. Sharda and Delen (2002) convert box office forecasting problem into classification problem ranging movie from a “flop” to a “blockbuster” using neural networks. Ramsden (2009) applies System Dynamic Models. He finds basic epidemic model (Kermack, McKendrick 1927), originally applied to disease expansion, fits well to the movie attendance data.
The studies mentioned above cover primarily the US domestic motion pictures market. However there are studies on other countries as well.
Bagella and Becchetti (1999) investigate movie produced in Italy during 1985 – 1996 years. They reject hypotheses of the direct impact of popularity of director and star-actors on movie box office. Controlling for this effect, they show that only the comic genre and only one local production studio “Filmauro” have a significant marginal impact on box office performances.
Zarin-Nejadan and Criado (2000) applies cross-sectional regression analysis for 600 movies released in Switzerland in 1995-1997. Their results show significant effect of critics’ reviews and movies’ award at the prestigious European film festivals on the box office success in the local motion picture market. Genre, presence of star-actors, director and country of production are also found significant when controlled for production cost.
Blanco and Rodriguez (2001) analyzed the significance of qualitative characteristics of movies using results of Encuesta sobre Habitos de Consumo Cultural (Cultural Consumption Habits Survey), cоnducted in Spain in 1998. They found differences in the valuation of Spanish and American movies. Hollywood motion pictures are more popular among young and married people with low education level. Presence of star actors has a positive effect, while director’s popularity influence is negative. Influence of critical reviews and advertising are significant but less than “word-of-mouth” effect. Awards have a negative impact on probability to select American film.
Collins, Hand and Snell (2002) adopted De Vany and Walls’ approach for UK movie pictures and transform the revenue data into a binary variable and estimate a probability that a film’s revenue will exceed a given threshold value; in other words, a probability of becoming a blockbuster. As expected, the presence of a star name and positive reviews increase the probability of success.
Jansen (2002) analyzes German films released between 1993 and 1998 and finds human capitals importance for movie success: previous achievements of the production company and the director are significant for the current success. Another interesting result is the uselessness of subsidizing German movies because it leads to unreasonably excessive budget. Star-actors effect is also found significant for German movie-goers.
Jordi McKenzie (2006) finds for Australian movies for 1997 – 2005 an "increasing returns to information" characteristic to be a general feature of the local film industry. Model of Bose-Einstein dynamics is also found relevant with bifurcation point in weekly revenue at a round fifth week. Similarly De Vany and Walls identified equilibrium between positive information of word of mouth and growing box office at the seventh week.
Elliott and Simmons (2007) analyze the effect of media advertisement for motion pictures in the United Kingdom. Applying 2SLS and bootstrapping they show that the impacts of types of advertising on box office revenues vary both in terms of channels and magnitude of impact. Authors find that television advertising is more effective than outdoor and radio advertising with a supply-side i.e. increases not directly box office but opening screens. However, the impact of press advertising is significant only on total box office for movies nominated for the Oscars and BAFTA awards.
Wall (2009) shows high return to information and winner-take-all nature of Thailand motion picture market for films exhibited during 2004 – 2008 years. The special feature of this market is that despite high part of Hollywood imports, some top records of box office belong to local movies. As dependent variable the author uses opening weekend box office shares and movie life-time.
Despite all discovered results, most of the researches agree that “nobody knows anything” about motion picture industry (De Vany 2004).
This study extend existing literature in few ways: i) firstly provides economic approach of motion pictures success for Post-Communist countries ii) applies time series dynamics for weekend box offices and examines dependence of total movie box office on its budget, release date, genre and country of production.
Motion pictures market is not similar to classic AD/AS model. From supply side there is a completion between movie theaters, between each movie that is monopolistically produced. Also there is usually second-price discrimination that leads to discounts for different types of consumers and time of spending. However, movie theaters charge the same price during all life-time of motion pictures.
Demand is influenced by many factors starting from price of complementary goods, as popcorn and cola, and finishing tastes of society (Mankiw 2002). This study focuses on internal characteristics of motion pictures that could affect its demand.
Blockbuster hypothesis is that more costly movie would earn more profit. So the bigger is a budget which includes costs of production, cast, special effects, marketing support, the more revenue of a motion picture would be. Audiences can interpret high production costs as signals of a motion picture’s high quality, i.e., professionalism of concept and execution (Hennig-Thurau et al 2007). In our model we assume marketing cost as a part of production cost. And any movie maker would not advertise “bad” good. That is advertisement cost is significantly influence box office (De Vanny 2004, Elliot & Simmons 2007).
Follow we provide motivation of including additional determinants of movie success. Various authors find significant impact of star actor / actress (DeVany 2004, Eliashberg et al 2005, Topf 2010). However, the definition of star is subjective. Some authors use James Ulmer’s list of popular actors / actresses (DeVany and Walls 2004), by high box office performance of last few movies where actor / actress was playing title role, movie festivals awards etc. Because of the lack of the data we would use dummy variable only for US actor / actress start power if one’s profile is contained in the Mojo database (details about the data are in the next section). Similar logic is to estimate star power of director.
Each genre has own fan base. Most likely children would like to see new animation picture, youth would pay for action movie, as well comedies are liked by most of the audience. Belonging movie to specific genre could attract viewers.
The logic behind the date of release i.e. date of screening is simple. During holidays people have more free time and desire for good time spending. So attendance of movie-theaters is assumed to increase. There is also possible seasonal effect when pupils and students have vacation.
Effect of cultural differences is taking into account by independent variable Country of production. Despite the fact that in sample countries all movies are screening on local language, quality of dubbing, mentality differences could have negative effect. Some authors find positive effect on box office for localy produced movies.
Most of the researches use as dependent variable a total box office revenue of the motion picture. To overcome heteroskedasticity for cross-sectional analysis we use logarithm of total box office.
The cross-section regression equation is as follows:
logRevenuei = β1 + β2logBudgeti + β3StarActori +β4StarDirectori + β5Countryi +β6Sequeli+ Г[Genre]i` + μi (1)
where logBudget is logarithm of motion picture production budget, StarActor and StarDirector are dummy variables if there is a star actor, famous director in a movie and Г is a set of dummy variables for genre. Country is set of dummies for countries of production companies (in case of co-producing country of major studio). μ is a error-term.
For time series analysis we use movie weekend’s box office shares to overcome inflation, scale effect, seasonal effect and other countries’ fixed effects:
Ѕit= BOit / Σ k=1..KBOkt (2)
where Sit – box office share of i’s movie, BOit – box office of movie i’s at weekend t.
Time series analysis regression is considered from the fact that opening weekend box office has significant effect on later movie performance. So we estimate AR(1) process:
St = a0 + a1St-1 + μi (3)
where St — movie's share in total box office of all screened movies during week t, St-1 — share of box office in previous week.
To check seasonal effect we build two additional specifications:
logRevenuei = β1 + Г[Weekend]i` + μi (4)
logRevenuei = β1 + Г[Month]i` + μi (5)
where Г are sets of dummy variables of correspondent weekend and month.
The main source of the data is publicly available internet database “Box Office Mojo”. The sample includes budgets, weekend box offices, number of screens, date, and the cast of the movies nationally released in Poland, Commonwealth of Independent States and Ukraine. So there are excluded screening of motion pictures on film festivals and noncommercial events. Information about Russian movies was additionally collected from kinopoisk.ru and about Polish - from Polish Film Institute pisf.pl
In Table 1 aggregated statistics is presented. There are 11062 weekend records for 1413 released movies in sample countries during 2007 to 2010 years. Detailed descriptive statistics is shown in Table А1.
|Average duration, week||3.81||4.87||3.91||4.45|
|Weekend average box office, $||190,054.2||351691.6||76,919.38||260,362|
|Average box office share||0.08||0.031||0.086||0.053|
Before September 27, 2007 CIS* data include box offices only for best 10 movies of the relative weekend and do not include number of screens. Afterwards it accounts weekend revenue of all screened movies even those shown in one movie theatre. Similar change in Ukrainian statistics is since May 17, 2007. And in Poland – since May 11, 2007. Also there are some motion pictures that were released in one country and not in another.
On Figure 1 box office share over time is presented for “random” movie.
We truncate sample data for cross-sectional analysis of blockbuster hypothesis according to available information about movies’ budget. For most of the motion pictures such data is not presented. From 1413 movies only 247 include budget. However they gain more than 50% of total box office over countries as shown in Table 2. Over rows there is total box office by correspondent countries. In column “With available budget” total box office of such movies is presented for each country. Column “Ratio” shows relation of box office of movies with known budget to movies without information about budget.
|Movies with avaliable information about budget||Movies without information about budget||Total||Ratio|
|Total box office, $||1,551,311,093.00||1,240,281,026.00||2,880,124,719.00||0.56|
|Box office in Poland, $||212,699,103.00||193,603,459.00||476,299,311.00||0.52|
|Box office in CIS*, $||1,238,315,115.00||971,256,711.00||2,291,995,100.00||0.56|
|Box office in Ukraine, $||100,296,875.00||75,420,856.00||181,145,136.00||0.57|
The new sample data is equally distributed over years as shown in Table 3.
|Year||Amount of movies with avaliable information about budget|
Aggregate descriptive statistics of box office revenues and budgets over countries is presented in Table B2.
Total box office revenue of each movie in all countries is presented on the panel a) of Figure 2. On the X axis there is amount of motion picture, on the Y axis – box office revenue in dollars. On the panel b) there are movies’ budgets in the same order as it ranked in panel a).
Figure 2. а) Movies' total box offices b) Movies' budgets
There are 114 observed actors / actresses played in 144 (58%) movies in sample. There are 101 observed directors of motion pictures that made 115 (46%) movies. Titles of all movies in the sample with names of actors and directors are presented in Table B3.
There are 21 genres in the sample. It is distributed into 5 categories as shown in Table 4.
|Action, Crime, War, Western, Adventure,|
Horror, Thriller, Detective, Criminal
|Documentary, Period, Sci-Fi, Sports, Biography||11||13||9|
|Drama, Music, Musical, Romance,|
Romantic, Family, Fantasy, Melodrama
On the Figure 3 there is a distribution of box office revenues across countries – movie producers.
Figure 3. Distribution of box offices across countries - movies producers
Cross-section OLS regression results with over all genres over countries are presented in Table B1. Base genre is considered and so is omitted "Action". For each countries there are found significant genres. For Poland animated pictures at average has larger total box office than "Action" movie by 1.3% and "Melodrama" – larger by 3.66%.
In CIS* and Ukraine movies in genre "Drama" gains less that "Action" by 1.73% and 1.17% correspondently. There is also negative effect of "Thrillers" by 0.9% and 1.3% for CIS* and Ukraine.
For all countries impacts of motion picture budget and sequel are positive and significant minimum at 5% confident level. Star power of actor / actress and director is found insignificant. R-squared values are 0.373, 0.521 and 0.466 for Poland, CIS and Ukraine correspondently.
Results of regression with aggregating genres and countries of production are shown in Table 5. It has better R-squared values. Effects of budget and sequel are also increased. For Ukraine genre "Comedy" becomes significant and positive.
|VARIABLES||Logarithm of total revenue||Logarithm of total revenue||Logarithm of total revenue|
|Logarithm of budget||0.482***||1.038***||0.946***|
|Star actor / actress||0.320||0.243||0.306|
|Aggregated genre 2 (Animation)||1.389***||0.0470||0.0948|
|Aggregated genre 3||0.601||0.115||0.323|
|Aggregated genre 4||0.418||-0.290||0.0268|
|Aggregated genre 5 (Comedy)||0.105||0.0260||0.623**|
|Country producer US||2.352*||2.520***||0.145|
|Country producer Russia||0||5.036***||2.583**|
|Country producer Poland||4.891***||0||-2.673*|
|Country producer EU||1.062||1.705*||-0.634|
|Country producer Ukraine||0||0||0.181|
Some countries of production have significant effect. We use “Others” countries as base category. First of all, locally produced movies have at average larger total box office by 4.89% in Poland and 5.03% in CIS*. US motion pictures earn additional 2.53% and 2.52% in Poland and CIS*. In Ukraine movies produced in Russia benefit by 2.58%. And impact of Polish pictures on box office is less by 2.67% at 10% significance level.
Excluding outlier movie “Avatar” does not change significance level of any variables. Absolute value of some parameters was minor changed. Result is present in Table B2.
Now consider time series specification. Result of lagged regression is shown in Table 8. R-squared values are high for all countries that means that box office at current weekend is much depend from success of previous weekend. However for Poland coefficient of previous weekend box office is higher than for CIS and Ukraine that implies lower speed of decreasing revenue with time.
|VARIABLES||Share of box office||Share of box office||Share of box office|
Weekend effect estimation results are presented in Table B2. Base weekend is last one, 52-th of a year. For Poland effect of each weekend except summer's one is found significant and positive. Motion pictures released in Poland during first 8 weekends of the year have the largest seasonal effect at average by additional 1.4%. For CIS* there is also similar trend but less significant. For Ukraine only first 3 weekends of the year have positive effect comparing with 52-th weekend of the year . However R-squared values are small 0.068, 0.019 and 0.022 for Poland, CIS* and Ukraine correspondently. This implies low influence of seasonal effect on box office revenue.
Results for regression with aggregated months are presented in Table 9. Base month is December. We see that R-squared values become less. While for Poland month effects are similar to weekend’s one. February add the most at average 1.01% of box office. While in July Polish box office at average are lower by 0.225%. For CIS* first 4 months still have positive effects and additionally positive effect found for autumn months. In Ukraine seasonal effect is positive and significant only in January.
This study examines internal characteristics of a motion picture that lead to box office success in Ukraine, CIS* (Azerbaijan, Armenia, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Uzbekistan) and Poland. Blockbuster hypothesis that bigger budget increases revenue, is not rejected for all sample countries.
Based on Poland and CIS*s positive and significant effect of country production we could conclude that locally produced movies have additional box office revenue on domestic market.
US motion pictures have positive effect on box office in CIS* and Poland. And Russian movies are benefit in Ukraine.
Effect of sequel motion picture is positive and significant for all sample countries.
“Animation” genre is significant for Poland. And “Comedy” is significant for Ukraine.
Applying time-series analysis we prove hypothesis that weekend box office strongly depend on success of previous weekend.
Main peculiarities of Ukrainian motion picture industry are:
- faster decreasing of weekend box office revenue with time compare to Poland and CIS*
- small seasonal effect.
Results of this research could be used by Ukrainian movie producers by creating high cost local comedies.
Further research could be extended by including lists of popular local actors / actresses, advertisement costs, production studio, mixed genres, using new technological features as 3D movies.
Box Office Mojo http://boxofficemojo.com
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Table A1. Descriptive time series statistics
|Obs||Mean||Std. Dev.||Min||Max||Obs||Mean||Std. Dev.||Min||Max||Obs||Mean||Std. Dev.||Min||Max|
|Box office share||2247||0.08||0.11||0||0.92||6460||0.03||0.09||0||0.91||2355||0.09||0.14||0||0.93|
|Weekend of release||2247||3.81||3.59||1||53||6460||4.87||5.33||1||123||2355||3.91||4.47||1||53|
|N||mean, $||N||mean, $||N||mean, $|
Table B1. Cross-sectional regression with genres
Table B2. Estimation of weekend effect
Table C1. Information about movies in the sample
|Pirates of the Caribbean: At World's End||Adventure||300000000||Johnny Depp||Gore Verbinski|
|Harry Potter and the Half Blood Prince||Fantasy||250000000||Daniel Radcliffe||David Yates|
|Avatar||Sci-Fi||237000000||Sam Worthington||James Cameron|
|Harry Potter and the Deathly Hallows (Part One)||Fantasy||225000000||Daniel Radcliffe||David Yates|
|The Chronicles of Narnia: Prince Caspian||Fantasy||225000000||Ben Barnes||Andrew Adamson|
|2012||Action||200000000||John Cusack||Roland Emmerich|
|A Christmas Carol (2009)||Animation||200000000||Jim Carrey||Robert Zemeckis|
|Alice in Wonderland (2010)||Fantasy||200000000||Johnny Depp||Tim Burton|
|Iron Man 2||Action||200000000|
|Prince of Persia: The Sands of Time||Adventure||200000000||Jake Gyllenhaal||Mike Newell|
|Quantum of Solace||Action||200000000||Daniel Craig||Marc Forster|
|Terminator Salvation: The Future Begins||Sci-Fi||200000000||Christian Bale||McG|
|Transformers: Revenge of the Fallen||Sci-Fi||200000000||Shia LaBeouf||Michael Bay|
|Indiana Jones and the Kingdom of the Crystal Skull||Period||185000000||Harrison Ford||Steven Spielberg|
|The Dark Knight||Action||185000000||Christian Bale||Christopher Nolan|
|Wall-E||Animation||180000000||Fred Willard*||Andrew Stanton|
|Monsters Vs. Aliens||Animation||175000000|
|Tron Legacy||Sci-Fi||170000000||Jeff Bridges||Joseph Kosinski|
|How to Train Your Dragon||Animation||165000000||Jay Baruchel|
|Shrek Forever After||Animation||165000000||Mike Myers|
|Inception||Sci-Fi||160000000||Leonardo DiCaprio||Christopher Nolan|
|Shrek the Third||Animation||160000000||Mike Myers||Chris Miller|
|The Chronicles of Narnia: The Voyage of the Dawn T||Fantasy||155000000||Ben Barnes||Michael Apted|
|Angels & Demons||Thriller||150000000||Tom Hanks||Ron Howard|
|Hancock||Fantasy||150000000||Will Smith||Peter Berg|
|Harry Potter and the Order of the Phoenix||Fantasy||150000000||Daniel Radcliffe||David Yates|
|Madagascar: Escape 2 Africa||Animation||150000000||Ben Stiller||Tom McGrath|
|Night at the Museum: Battle of the Smithsonian||Family||150000000||Ben Stiller||Shawn Levy|
|Ratatouille||Animation||150000000||Brad Bird*||Brad Bird|
|The Curious Case of Benjamin Button||Fantasy||150000000||Brad Pitt||David Fincher|
|The Incredible Hulk||Action||150000000||Edward Norton||Louis Leterrier|
|The Last Airbender||Fantasy||150000000||Noah Ringer||M. Night Shyamalan|
|Flushed Away||Animation||149000000||Hugh Jackman||David Bowers|
|The Mummy: Tomb of the Dragon Emperor||Period||145000000||Brendan Fraser||Rob Cohen|
|Kung Fu Panda||Animation||130000000||Jack Black|
|Megamind||Animation||130000000||Will Ferrell||Tom McGrath|
|Watchmen||Action||130000000||Malin Akerman||Zack Snyder|
|Clash of the Titans (2010)||Fantasy||125000000||Sam Worthington||Louis Leterrier|
|Knight & Day||Comedy||117000000||Tom Cruise||James Mangold|
|Night at the Museum||Family||110000000||Ben Stiller||Shawn Levy|
|Salt||Action||110000000||Angelina Jolie||Phillip Noyce|
|Little Fockers||Comedy||100000000||Ben Stiller||Paul Weitz|
|Sex and the City 2||Romantic||100000000||Sarah Jessica Parker||Michael Patrick King|
|Ice Age: Dawn of the Dinosaurs||Animation||90000000||Ray Romano||Carlos Saldanha|
|Sherlock Holmes||Adventure||90000000||Robert Downey, Jr.||Guy Ritchie|
|Cats& Dogs: Revenge of Kitty Galore||Family||85000000||Christina Applegate||Brad Peyton|
|Charlotte's Web||Family||85000000||Dakota Fanning||Gary Winick|
|Dr. Seuss' Horton Hears a Who!||Animation||85000000||Joey King*||Jimmy Hayward|
|Fast and Furious||Action||85000000|
|Hellboy II: The Golden Army||Action||85000000||Ron Perlman||Guillermo del Toro|
|The Holiday||Romantic||85000000||Cameron Diaz||Nancy Meyers|
|Bedtime Stories||Family||80000000||Adam Shankman|
|Chi bi (Red Cliff: Part I)||Action||80000000|
|Four Christmases||Comedy||80000000||Reese Witherspoon||Seth Gordon|
|Nine||Drama||80000000||Daniel Day-Lewis||Rob Marshall|
|The Day the Earth Stood Still (2008)||Sci-Fi||80000000||Keanu Reeves|
|Charlie Wilson's War||War||75000000||Tom Hanks||Mike Nichols|
|Valkyrie||Thriller||75000000||Tom Cruise||Bryan Singer|
|Wanted||Action||75000000||Morgan Freeman||Timur Bekmambetov|
|Inglourious Basterds||War||70000000||Brad Pitt||Quentin Tarantino|
|Wall Street: Money Never Sleeps||Drama||70000000||Michael Douglas||Oliver Stone|
|Yes Man||Comedy||70000000||Jim Carrey|
|Despicable Me||Animation||69000000||Steve Carell||Chris Renaud|
|The Twilight Saga: Eclipse||Romance||68000000||Kristen Stewart||David Slade|
|Astro Boy||Animation||65000000||Kristen Bell*||David Bowers|
|Due Date||Comedy||65000000||Robert Downey, Jr.||Todd Phillips|
|Alvin and the Chipmunks||Family||60000000||Jason Lee||Tim Hill|
|Eat Pray Love||Drama||60000000||Julia Roberts||Ryan Murphy|
|Invictus||Drama||60000000||Matt Damon||Clint Eastwood|
|Resident Evil: Afterlife||Action||60000000||Milla Jovovich||Paul W.S. Anderson|
|The Tale of Despereaux||Animation||60000000|
|Year One||Adventure||60000000||Jack Black||Harold Ramis|
|Changeling||Thriller||55000000||Angelina Jolie||Clint Eastwood|
|Date Night||Comedy||55000000||Steve Carell||Shawn Levy|
|Seven Pounds||Drama||55000000||Will Smith||Gabriele Muccino|
|Mamma Mia!||Musical||52000000||Meryl Streep|
|Utomlyonnye solntsem 2 (Burnt by the Sun 2)||Drama||52000000|
|Valentine's Day||Romantic||52000000||Jessica Alba||Garry Marshall|
|Hannibal Rising||Thriller||50000000||Rhys Ifans|
|Law Abiding Citizen||Thriller||50000000||Jamie Foxx||F. Gary Gray|
|The Twilight Saga: New Moon||Romance||50000000||Kristen Stewart||Chris Weitz|
|Tooth Fairy||Fantasy||48000000||The Rock||Michael Lembeck|
|The Warrior's Way||Western||42000000||Kate Bosworth||Sngmoo Lee|
|Cirque du Freak: The Vampire's Assistant||Fantasy||40000000||John C. Reilly||Paul Weitz|
|Marie Antoinette||Period||40000000||Kirsten Dunst||Sofia Coppola|
|The Bounty Hunter||Action||40000000||Gerard Butler||Andy Tennant|
|The Final Destination||Horror||40000000||David R. Ellis|
|The Prestige||Fantasy||40000000||Christian Bale||Christopher Nolan|
|The Proposal||Romantic||40000000||Sandra Bullock||Anne Fletcher|
|Shoot 'Em Up||Action||39000000||Clive Owen||Michael Davis|
|Burn After Reading||Comedy||37000000||George Clooney|
|Twilight||Romance||37000000||Kristen Stewart||Catherine Hardwicke|
|Obitaemyy ostrov (The Inhabited Island: Part I)||Fantasy||36500000|
|21||Drama||35000000||Jim Sturgess||Robert Luketic|
|A Nightmare on Elm Street (2010)||Horror||35000000||Jackie Earle Haley|
|Superhero Movie||Comedy||35000000||Tracy Morgan||Craig Mazin|
|D-War (Dragon Wars)||Action||32000000||Jason Behr|
|30 Days of Night||Horror||30000000||Josh Hartnett||David Slade|
|Hannah Montana The Movie||Family||30000000||Miley Cyrus||Peter Chelsom|
|Step Up 3-D||Music||30000000||Jon Chu|
|The Invisible||Horror||30000000||Marcia Gay Harden||David S. Goyer|
|Transporter 3||Action||30000000||Jason Statham||Olivier Megaton|
|Les Bronzés 3 - amis pour la vie||Comedy||28000000|
|Pineapple Express||Action||27000000||Seth Rogen||David Gordon Green|
|1408||Horror||25000000||John Cusack||Mikael Hafstrom|
|Blindness||Drama||25000000||Mark Ruffalo||Fernando Meirelles|
|Dear John||Drama||25000000||Amanda Seyfried||Lasse Hallstrom|
|Deception (2008)||Thriller||25000000||Hugh Jackman||Marcel Langenegger|
|Fly Me to the Moon||Animation||25000000||Christopher Lloyd|
|What Just Happened?||Comedy||25000000||Robert DeNiro||Barry Levinson|
|Zombieland||Horror||23600000||Woody Harrelson||Ruben Fleisher|
|Coco avant Chanel||Drama||23000000||Audrey Tautou|
|Mad Money||Crime||22000000||Diane Keaton||Callie Khouri|
|Stone||Drama||22000000||Frances Conroy||John Curran|
|We Own the Night||Crime||21000000||Joaquin Phoenix||James Gray|
|Butterfly on a Wheel||Thriller||20000000||Pierce Brosnan|
|Jackass 3-D||Comedy||20000000||Johnny Knoxville||Jeff Tremaine|
|Kickin' It Old Skool||Comedy||20000000||Jamie Kennedy||Harvey Glazer|
|Mr. Brooks||Thriller||20000000||Kevin Costner||Bruce A. Evans|
|Saw 3D||Horror||20000000||Tobin Bell|
|She's Out of My League||Romantic||20000000||Jay Baruchel||Jim Field Smith|
|Superbad||Comedy||20000000||Jonah Hill||Greg Mottola|
|The Horsemen||Horror||20000000||Dennis Quaid|
|Vampires Suck||Horror||20000000||Ken Jeong|
|El Laberinto del Fauno (Pan's Labyrinth)||Fantasy||19000000||Guillermo del Toro|
|Leap Year||Romantic||19000000||Amy Adams|
|I Come with the Rain||Thriller||18000000|
|Observe and Report||Comedy||18000000||Seth Rogen||Jody Hill|
|The Mist||Horror||18000000||Alexa Davalos||Frank Darabont|
|The Illusionist (2010)||Animation||17000000||Sylvain Chomet|
|The Women (2008)||Comedy||16000000||Meg Ryan||Diane English|
|Funny Games||Horror||15000000||Naomi Watts|
|Tetro||Drama||15000000||Vincent Gallo||Francis Ford Coppola|
|The Hills Have Eyes 2||Horror||15000000|
|Whatever Works||Comedy||15000000||Larry David||Woody Allen|
|New York, I Love You||Romance||14700000||Justin Bartha*|
|A Perfect Getaway||Thriller||14000000||Chris Hemsworth||David Twohy|
|The Life Before Her Eyes||Drama||13000000||Uma Thurman||Vadim Perelman|
|Employee of the Month||Comedy||12000000||Dane Cook|
|Step Up||Music||12000000||Channing Tatum||Anne Fletcher|
|L'arbre (The Tree)||Drama||11046000|
|Saw V||Horror||10800000||Tobin Bell||David Hackl|
|Blonde Ambition||Romantic||10000000||Jessica Simpson|
|MacGruber||Comedy||10000000||Will Forte||Jorma Taccone|
|Over Her Dead Body||Fantasy||10000000||Lake Bell|
|Samiy luchshiy film 2 (The Very Best Film 2)||Comedy||10000000|
|Janosik. Prawdziwa historia||Drama||8400000|
|Juno||Comedy||7500000||Ellen Page||Jason Reitman|
|Boy s tenyu 2 (Shadow Boxing 2)||Action||6000000|
|City Island||Comedy||6000000||Alan Arkin|
|High Security Vacation||Comedy||5000000|
|Ironiya sudby. Prodolzhenie (The Irony of Fate 2)||Melodrama||5000000|
|V Tsenturiya. V poiskakh zacharovannykh sokrovishc||Adventure||5000000|
|Samiy luchshiy film (The Very Best Film)||Comedy||4500000|
|Bumaznyj soldat (Paper Soldier)||Drama||4000000|
|Gora samotsvetov V||Animation||4000000|
|If I Had Known I Was a Genius||Drama||4000000|
|Lubov morkov 2 (Lovey Dovey 2)||Comedy||4000000|
|Scoop||Romantic||4000000||Woody Allen||Woody Allen|
|Saint John of Las Vegas||Comedy||3800000||Steve Buscemi|
|De laatste dagen van Emma Blank||Comedy||3720000|
|Lyubov v bolshom gorode (Love in the Big City)||Comedy||3500000|
|Lubov morkov (Lovey-Dovey)||Comedy||3000000|
|Antikiller D.K: Lyubov bez pamyati||War||2500000|
|The Power of Fear||Horror||2500000|
|Vsyo mogut koroli||Melodrama||2500000|
|Nasha Russia: Yaytsa sudby||Comedy||2000000|
|Kochaj i tancz||Melodrama||1965281|
|Wszystko, co kocham (All That I Love)||Drama||1260000|
|General. Zamach na Gibraltarze||War||1038000|
|Idiots and Angels||Animation||125000|