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What makes a blockbuster? Peculiarities of Ukraine

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.

 

 

Box offices in Ukraine
Box offices in Poland
Box ofiices in CIS* (Azerbaijan, Armenia, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Uzbekistan)
Log scale

 

 

 

A thesis for the degree of MA in Economics

Author: Yevgen Nasadyuk

Thesis Supervisor: Professor Vakhitov, Volodymyr

Kyiv School of Economics 2011


LITERATURE REVIEW

METHODOLOGY

DATA DISCRIPTION

EMPIRICAL RESULTS

CONCLUSION

WORKS CITED

LITERATURE REVIEW

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.


METHODOLOGY

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 + β3StarActori4StarDirectori + β5Countryi6Sequeli+ 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.

DATA DISCRIPTION

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.

Table 1. Aggregated statistics of weekend records
 PolandCIS*UkraineTotal
Total records22476460235511062
Movies58712395661413
Average duration, week3.814.873.914.45
Weekend average box office, $190,054.2351691.676,919.38260,362
Average box office share0.080.0310.0860.053
Average screens74.74107.435.4985.97

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.

Figure 1. Box office shares of "Shrek Forever After" per week for 1) Poland 2) CIS* 3) Ukraine

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.

Table 2. Total box office between movies over countries
 Movies with avaliable information about budgetMovies without information about budgetTotalRatio
Total box office, $1,551,311,093.001,240,281,026.002,880,124,719.000.56
Box office in Poland, $212,699,103.00193,603,459.00476,299,311.000.52
Box office in CIS*, $1,238,315,115.00971,256,711.002,291,995,100.000.56
Box office in Ukraine, $100,296,875.0075,420,856.00181,145,136.000.57

The new sample data is equally distributed over years as shown in Table 3.

Table 3. Total box office records across years for movies with information about budget
YearAmount of movies with avaliable information about budget
200785
2008130
2009152
2010152
Всього519

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.

Table 4. Aggregated genre statistics
 PolandCIS*Ukraine
Action, Crime, War, Western, Adventure,
Horror, Thriller, Detective, Criminal
446149
Animation182320
Documentary, Period, Sci-Fi, Sports, Biography11139
Drama, Music, Musical, Romance,
Romantic, Family, Fantasy, Melodrama
486552
Comedy153020
Total136192150

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

EMPIRICAL RESULTS

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.

Table 5. Regression with aggregated genres and country of production
 PolandCIS*Ukraine
VARIABLESLogarithm of total revenueLogarithm of total revenueLogarithm of total revenue
Logarithm of budget0.482***1.038***0.946***
 (0.124)(0.0951)(0.107)
Sequel0.726**0.689**0.669***
 (0.297)(0.275)(0.211)
Star director0.225-0.0184-0.0345
 (0.297)(0.283)(0.243)
Star actor / actress0.3200.2430.306
 (0.382)(0.360)(0.322)
Aggregated genre 2 (Animation)1.389***0.04700.0948
 (0.383)(0.352)(0.294)
Aggregated genre 30.6010.1150.323
 (0.443)(0.433)(0.377)
Aggregated genre 40.418-0.2900.0268
 (0.272)(0.247)(0.203)
Aggregated genre 5 (Comedy)0.1050.02600.623**
 (0.397)(0.314)(0.273)
Country producer US2.352*2.520***0.145
 (1.337)(0.819)(1.025)
Country producer Russia05.036***2.583**
 0(0.874)(1.081)
Country producer Poland4.891***0-2.673*
 (1.438)0(1.466)
Country producer EU1.0621.705*-0.634
 (1.386)(0.889)(1.080)
Country producer Ukraine000.181
 00(1.478)
Constant1.488-6.448***-4.920**
 (2.583)(1.797)(2.112)
    
Observations136192150
R-squared0.4490.5870.577

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.

Table 6. Result of lagged regression
 PolandCIS*Ukraine
VARIABLESShare of box officeShare of box officeShare of box office
L.bshares0.690***0.485***0.465***
 -0.00776-0.00378-0.0109
Constant-0.00191-0.00139***0.00448**
 -0.00122-0.000419-0.00216
Observations151044561461
R-squared0.840.7870.556

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.

Table 7. Month effect estimation
 PolandCIS*Ukraine
VARIABLESlogRevenuelogRevenuelogRevenue
    
January0.782***0.960***0.676***
 -0.132-0.152-0.19
February1.010***0.935***0.199
 -0.155-0.157-0.19
March0.415***0.766***0.238
 -0.135-0.141-0.186
April0.1850.584***0.122
 -0.141-0.154-0.202
May0.383***0.206-0.0452
 -0.134-0.145-0.194
June0.09190.354**0.304
 -0.136-0.158-0.204
July-0.225*0.1740.226
 -0.134-0.158-0.203
August0.306**0.363**-0.0487
 -0.138-0.152-0.193
September0.434***0.571***-0.0476
 -0.138-0.148-0.193
October0.441***0.322**0.122
 -0.136-0.138-0.193
November0.494***0.1880.119
 -0.129-0.137-0.191
Constant10.98***9.530***9.662***
 -0.0864-0.0962-0.132
    
Observations224764602354
R-squared0.0450.0130.01

CONCLUSIONS

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.


WORK CITED

Box Office Mojo http://boxofficemojo.com

Bagella Michele, Becchetti Leonardo, 1999, The Determinants of Motion Picture Box Office Performance: Evidence from Movies Produced in Italy, Journal of Cultural Economics, Vol. 23, No. 4, 1: 237-256

Baker Wayne E.; Faulkner Robert R., 1991, Role as Resource in the Hollywood Film Industry, The American Journal of Sociology, Vol. 97, No. 2. 1:279-309.

Blanco Victor F., Rodriguez Juan P., 2001, Building Stronger National Movie Industries: The Case Of Spain, Working Papers 29-02, Instituto de Estudios Fiscales, 1: 15-24

Collins Alan, Hand Chris and Snell Martin C., 2001, What Makes a Blockbuster? Economic Analysis of Film Success in the United Kingdom, Managerial and Decision Economics, vol. 23(6), 1: 343-354

Hennig-Thurau Thorsten, Houston Mark B., Walsh Gianfranco, 2007, Determinants of motion picture box office and profitability: an interrelationship approach, Review of Managerial Science, Volume 1,  April, 1: 65-92

Eliashberg Jehoshua, Elberse Anita, Leenders Mark, 2005, The motion picture industry: critical issues in practice, current research & new research direction, www.hbs.edu, 1: 12-20

Elliott Caroline, Simmons Robert, 2008, Determinants of UK box office success: the impact of quality signals, Review of Industrial Organization, vol. 33, 1: 93-111

Ольга Гнатів, "Повернення українських фільмів?" газета Kyivpost від 10.06.2010

Hui Kai-Lung, Png Ivan P.L, 2002, On the Supply of Creative Work: Evidence from the Movies, American Economic Review, American Economic Association, vol. 92(2), 1: 217-220

Makiw Gregory N., 2002, Macroeconomics, Worth Publishers Economics 5e, 1: 29

Sharda Ramesh, Delen Dursun, 2006, Predicting box-office success of motion pictures with neural networks, Expert Systems with Applications, vol 30, 1: 243-254

Topf Patrick, 2010, Examining Success at the Domestic Box-Office in the Motion Picture Industry, Illinois Wesleyan University, 1:

DeVany Arthur, 2004, Hollywood Economics. New York: Routledge, 1:122-139

Wall William Douglas, 2009, The market for Motion Pictures in Thailand: Rank, Revenue, and Survival at the Box Office, International Journal of Business and Economics, vol. 8, 1: 115-131

Wikipedia

Zarin-Nejadan Milad, Criado Carlos Ordas, 2004, The Determinants of Revenues in the Swiss Motion Picture Market, Universite de Neuchatel, 1: 3-6

APPENDIX A. Descriptive statistics

Table A1. Descriptive time series statistics

 Poland    CIS*    Ukraine    
 ObsMeanStd. Dev.MinMaxObsMeanStd. Dev.MinMaxObsMeanStd. Dev.MinMax
Revenue2247190054.2315379.57243716846460351691.61196146319700000235576919.38160967.401974417
Screen share22470.080.0500.361070.030.0500.3820920.080.0600.47
Box office share22470.080.1100.9264600.030.0900.9123550.090.1400.93
Screens224774.7443.6312356107107.4183.911329209235.4926.761144
Year22472008.691.142007201064602008.811.062007201023552008.571.1520072010
Week224728.3815.11152646028.6415.1115223552715.11152
Weekend of release22473.813.5915364604.875.33112323553.914.47153

Table A2. Average statistics of cross-sectional data over countries

 Poland CIS* Ukraine 
 Nmean, $Nmean, $Nmean, $
Revenue1361,564,0001926,450,000150668,646
Sequel261341321
Budget13684,960,00019264,950,00015077,070,000

APPENDIX B. Estimation outputs

Table B1. Cross-sectional regression with genres

 PolandCIS*Ukraine
VARIABLESlogRevenuelogRevenuelogRevenue
logBudget0.287**0.894***0.608***
 -0.144-0.115-0.13
Sequel0.685**0.728**0.622**
 -0.343-0.317-0.258
bdirector0.4120.004030.0343
 -0.34-0.322-0.288
bactor0.288-0.591-0.660*
 -0.417-0.372-0.339
_IGenre_20.536-0.6070.0438
 -0.895-0.763-0.741
_IGenre_31.373***-0.274-0.168
 -0.496-0.461-0.389
_IGenre_4-0.232-0.1330.211
 -0.535-0.452-0.396
_IGenre_50-1.3720
 -0-1.155-0
_IGenre_600.333-0.218
 -0-1.588-1.228
_IGenre_70-2.0380
 -0-1.598-0
_IGenre_80.148-1.730***-1.178***
 -0.555-0.47-0.429
_IGenre_9-0.000646-0.751-0.529
 -0.608-0.669-0.517
_IGenre_100.268-0.123-0.0982
 -0.523-0.506-0.413
_IGenre_11-0.428-0.359-0.752
 -0.569-0.547-0.493
_IGenre_123.661**1.519**0.313
 -1.518-0.749-0.689
_IGenre_131.0710.231-0.36
 -1.159-1.185-0.895
_IGenre_142.4080.7610.457
 -1.478-1.597-1.226
_IGenre_150.196-1.266-0.661
 -0.788-0.959-0.737
_IGenre_160.5580.45-0.463
 -0.8-0.847-0.654
_IGenre_17-0.2580.04650.13
 -0.696-0.776-0.53
_IGenre_180.5230.02230.338
 -0.639-0.606-0.557
_IGenre_19-0.89-0.972*-1.302***
 -0.602-0.564-0.466
_IGenre_20-0.6710.765-0.292
 -0.903-0.958-0.655
_IGenre_210-1.024-1.42
 -0-1.58-1.209
Constant7.424***-0.2792.597
 -2.499-1.934-2.222
    
Observations136192150
R-squared0.3730.5210.446

Table B2. Estimation of weekend effect

 PolandCIS*Ukraine
VARIABLESlogRevenuelogRevenuelogRevenue
_IWeek_11.147***1.457***1.236**
 -0.284-0.438-0.484
_IWeek_21.231***0.5370.842*
 -0.282-0.37-0.457
_IWeek_31.533***0.650*0.756*
 -0.291-0.37-0.451
_IWeek_41.416***1.009***0.4
 -0.314-0.378-0.442
_IWeek_51.411***0.671*0.207
 -0.32-0.372-0.429
_IWeek_61.752***0.791**0.347
 -0.323-0.381-0.432
_IWeek_71.658***0.760**0.365
 -0.311-0.365-0.432
_IWeek_81.215***1.004***0.295
 -0.348-0.38-0.434
_IWeek_91.542***0.602*-0.304
 -0.317-0.358-0.417
_IWeek_101.332***0.4490.176
 -0.311-0.354-0.44
_IWeek_110.919***0.648*0.394
 -0.308-0.359-0.463
_IWeek_121.215***0.5290.164
 -0.317-0.36-0.43
_IWeek_131.127***0.761**0.251
 -0.291-0.363-0.436
_IWeek_140.3880.660*0.0172
 -0.289-0.363-0.438
_IWeek_150.566**0.4830.172
 -0.287-0.359-0.454
_IWeek_160.711**0.3730.443
 -0.287-0.353-0.438
_IWeek_170.739**0.0211-0.0964
 -0.291-0.363-0.46
_IWeek_181.043***1.047***-0.28
 -0.327-0.404-0.451
_IWeek_190.949***0.3290.0855
 -0.32-0.365-0.447
_IWeek_200.870***0.0528-0.499
 -0.289-0.363-0.442
_IWeek_210.842***-0.2730.157
 -0.293-0.367-0.473
_IWeek_221.120***-0.115-0.482
 -0.289-0.371-0.451
_IWeek_230.874***0.230.48
 -0.32-0.373-0.48
_IWeek_240.847***0.5290.125
 -0.291-0.377-0.454
_IWeek_250.855***0.3210.398
 -0.291-0.373-0.454
_IWeek_260.620**0.2330.173
 -0.291-0.374-0.449
_IWeek_270.322-0.2520.343
 -0.277-0.365-0.46
_IWeek_280.1250.1770.146
 -0.277-0.377-0.449
_IWeek_290.191-0.05680.426
 -0.297-0.37-0.46
_IWeek_300.474*-0.1730.233
 -0.281-0.38-0.457
_IWeek_310.501*0.0948-0.0492
 -0.279-0.367-0.444
_IWeek_320.721**0.264-0.0543
 -0.299-0.365-0.442
_IWeek_330.734**0.2210.0505
 -0.295-0.369-0.436
_IWeek_340.714**0.2020.0305
 -0.295-0.358-0.44
_IWeek_351.180***0.131-0.347
 -0.278-0.356-0.419
_IWeek_360.911***0.4820.0679
 -0.295-0.357-0.436
_IWeek_370.966***0.290.0555
 -0.278-0.353-0.436
_IWeek_381.146***0.309-0.214
 -0.293-0.354-0.432
_IWeek_390.919***0.576-0.263
 -0.301-0.359-0.427
_IWeek_401.279***0.0957-0.0842
 -0.365-0.338-0.43
_IWeek_410.986***-0.001990.193
 -0.278-0.336-0.434
_IWeek_420.795***0.3140.224
 -0.273-0.344-0.44
_IWeek_431.090***0.266-0.00866
 -0.274-0.342-0.427
_IWeek_440.774***0.3280.223
 -0.272-0.343-0.438
_IWeek_451.177***0.09020.144
 -0.274-0.336-0.424
_IWeek_461.208***-0.0198-0.151
 -0.275-0.338-0.432
_IWeek_471.039***-0.2510.077
 -0.273-0.335-0.424
_IWeek_480.921***-0.343-0.0301
 -0.269-0.357-0.432
_IWeek_490.821***0.0966-0.0428
 -0.272-0.344-0.424
_IWeek_500.749***-0.0768-0.221
 -0.277-0.337-0.44
_IWeek_510.192-0.4320.106
 -0.279-0.34-0.469
Constant10.43***9.691***9.709***
 -0.196-0.276-0.329
    
Observations224764602354
R-squared0.0680.0190.022

APPENDIX C. Information about movies

Table C1. Information about movies in the sample

Movie's titleGenreBudgetActorDirector
Pirates of the Caribbean: At World's EndAdventure300000000Johnny DeppGore Verbinski
TangledAnimation260000000Mandy Moore 
Spider-Man 3Action258000000  
Harry Potter and the Half Blood PrinceFantasy250000000Daniel RadcliffeDavid Yates
AvatarSci-Fi237000000Sam WorthingtonJames Cameron
Harry Potter and the Deathly Hallows (Part One)Fantasy225000000Daniel RadcliffeDavid Yates
The Chronicles of Narnia: Prince CaspianFantasy225000000Ben BarnesAndrew Adamson
2012Action200000000John CusackRoland Emmerich
A Christmas Carol (2009)Animation200000000Jim CarreyRobert Zemeckis
Alice in Wonderland (2010)Fantasy200000000Johnny DeppTim Burton
Iron Man 2Action200000000  
Prince of Persia: The Sands of TimeAdventure200000000Jake GyllenhaalMike Newell
Quantum of SolaceAction200000000Daniel CraigMarc Forster
Terminator Salvation: The Future BeginsSci-Fi200000000Christian BaleMcG
Transformers: Revenge of the FallenSci-Fi200000000Shia LaBeoufMichael Bay
Indiana Jones and the Kingdom of the Crystal SkullPeriod185000000Harrison FordSteven Spielberg
The Dark KnightAction185000000Christian BaleChristopher Nolan
Wall-EAnimation180000000Fred Willard*Andrew Stanton
Monsters Vs. AliensAnimation175000000  
UpAnimation175000000Edward Asner* 
Tron LegacySci-Fi170000000Jeff BridgesJoseph Kosinski
How to Train Your DragonAnimation165000000Jay Baruchel 
Shrek Forever AfterAnimation165000000Mike Myers 
InceptionSci-Fi160000000Leonardo DiCaprioChristopher Nolan
Shrek the ThirdAnimation160000000Mike MyersChris Miller
The Chronicles of Narnia: The Voyage of the Dawn TFantasy155000000Ben BarnesMichael Apted
Angels & DemonsThriller150000000Tom HanksRon Howard
BoltAnimation150000000John Travolta 
HancockFantasy150000000Will SmithPeter Berg
Harry Potter and the Order of the PhoenixFantasy150000000Daniel RadcliffeDavid Yates
Madagascar: Escape 2 AfricaAnimation150000000Ben StillerTom McGrath
Night at the Museum: Battle of the SmithsonianFamily150000000Ben StillerShawn Levy
RatatouilleAnimation150000000Brad Bird*Brad Bird
The Curious Case of Benjamin ButtonFantasy150000000Brad PittDavid Fincher
The Incredible HulkAction150000000Edward NortonLouis Leterrier
The Last AirbenderFantasy150000000Noah RingerM. Night Shyamalan
TransformersSci-Fi150000000  
Flushed AwayAnimation149000000Hugh JackmanDavid Bowers
The Mummy: Tomb of the Dragon EmperorPeriod145000000Brendan FraserRob Cohen
Kung Fu PandaAnimation130000000Jack Black 
MegamindAnimation130000000Will FerrellTom McGrath
WatchmenAction130000000Malin AkermanZack Snyder
Clash of the Titans (2010)Fantasy125000000Sam WorthingtonLouis Leterrier
Knight & DayComedy117000000Tom CruiseJames Mangold
Night at the MuseumFamily110000000Ben StillerShawn Levy
SaltAction110000000Angelina JoliePhillip Noyce
Little FockersComedy100000000Ben StillerPaul Weitz
Sex and the City 2Romantic100000000Sarah Jessica ParkerMichael Patrick King
Ice Age: Dawn of the DinosaursAnimation90000000Ray RomanoCarlos Saldanha
Sherlock HolmesAdventure90000000Robert Downey, Jr.Guy Ritchie
Cats& Dogs: Revenge of Kitty GaloreFamily85000000Christina ApplegateBrad Peyton
Charlotte's WebFamily85000000Dakota FanningGary Winick
Dr. Seuss' Horton Hears a Who!Animation85000000Joey King*Jimmy Hayward
EnchantedFamily85000000Amy Adams 
Fast and FuriousAction85000000  
Hellboy II: The Golden ArmyAction85000000Ron PerlmanGuillermo del Toro
The HolidayRomantic85000000Cameron DiazNancy Meyers
Bedtime StoriesFamily80000000 Adam Shankman
Chi bi (Red Cliff: Part I)Action80000000  
Four ChristmasesComedy80000000Reese WitherspoonSeth Gordon
NineDrama80000000Daniel Day-LewisRob Marshall
OceansDocumentary80000000Pierce Brosnan* 
The Day the Earth Stood Still (2008)Sci-Fi80000000Keanu Reeves 
Charlie Wilson's WarWar75000000Tom HanksMike Nichols
ValkyrieThriller75000000Tom CruiseBryan Singer
WantedAction75000000Morgan FreemanTimur Bekmambetov
Inglourious BasterdsWar70000000Brad PittQuentin Tarantino
Wall Street: Money Never SleepsDrama70000000Michael DouglasOliver Stone
Yes ManComedy70000000Jim Carrey 
Despicable MeAnimation69000000Steve CarellChris Renaud
The Twilight Saga: EclipseRomance68000000Kristen StewartDavid Slade
Astro BoyAnimation65000000Kristen Bell*David Bowers
Due DateComedy65000000Robert Downey, Jr.Todd Phillips
Alvin and the ChipmunksFamily60000000Jason LeeTim Hill
Eat Pray LoveDrama60000000Julia RobertsRyan Murphy
InvictusDrama60000000Matt DamonClint Eastwood
Resident Evil: AfterlifeAction60000000Milla JovovichPaul W.S. Anderson
The Tale of DespereauxAnimation60000000  
Year OneAdventure60000000Jack BlackHarold Ramis
ChangelingThriller55000000Angelina JolieClint Eastwood
Date NightComedy55000000Steve CarellShawn Levy
Seven PoundsDrama55000000Will SmithGabriele Muccino
Mamma Mia!Musical52000000Meryl Streep 
Utomlyonnye solntsem 2 (Burnt by the Sun 2)Drama52000000  
Valentine's DayRomantic52000000Jessica AlbaGarry Marshall
Hannibal RisingThriller50000000Rhys Ifans 
Law Abiding CitizenThriller50000000Jamie FoxxF. Gary Gray
The Twilight Saga: New MoonRomance50000000Kristen StewartChris Weitz
Tooth FairyFantasy48000000The RockMichael Lembeck
Mr. NobodySci-Fi47000000  
The Warrior's WayWestern42000000Kate BosworthSngmoo Lee
ApocalyptoPeriod40000000 Mel Gibson
Cirque du Freak: The Vampire's AssistantFantasy40000000John C. ReillyPaul Weitz
Marie AntoinettePeriod40000000Kirsten DunstSofia Coppola
The Bounty HunterAction40000000Gerard ButlerAndy Tennant
The Final DestinationHorror40000000 David R. Ellis
The PrestigeFantasy40000000Christian BaleChristopher Nolan
The ProposalRomantic40000000Sandra BullockAnne Fletcher
Shoot 'Em UpAction39000000Clive OwenMichael Davis
Burn After ReadingComedy37000000George Clooney 
TwilightRomance37000000Kristen StewartCatherine Hardwicke
Obitaemyy ostrov (The Inhabited Island: Part I)Fantasy36500000  
21Drama35000000Jim SturgessRobert Luketic
A Nightmare on Elm Street (2010)Horror35000000Jackie Earle Haley 
Superhero MovieComedy35000000Tracy MorganCraig Mazin
D-War (Dragon Wars)Action32000000Jason Behr 
30 Days of NightHorror30000000Josh HartnettDavid Slade
9Animation30000000Elijah Wood 
Hannah Montana The MovieFamily30000000Miley CyrusPeter Chelsom
Step Up 3-DMusic30000000 Jon Chu
The InvisibleHorror30000000Marcia Gay HardenDavid S. Goyer
Transporter 3Action30000000Jason StathamOlivier Megaton
Les Bronzés 3 - amis pour la vieComedy28000000  
Pineapple ExpressAction27000000Seth RogenDavid Gordon Green
1408Horror25000000John CusackMikael Hafstrom
BlindnessDrama25000000Mark RuffaloFernando Meirelles
Dance FlickComedy25000000  
Dear JohnDrama25000000Amanda SeyfriedLasse Hallstrom
Deception (2008)Thriller25000000Hugh JackmanMarcel Langenegger
Fly Me to the MoonAnimation25000000Christopher Lloyd 
Taras BulbaAction25000000  
What Just Happened?Comedy25000000Robert DeNiroBarry Levinson
Die BuddenbrooksDrama23814000  
ZombielandHorror23600000Woody HarrelsonRuben Fleisher
Coco avant ChanelDrama23000000Audrey Tautou 
Mad MoneyCrime22000000Diane KeatonCallie Khouri
StoneDrama22000000Frances ConroyJohn Curran
We Own the NightCrime21000000Joaquin PhoenixJames Gray
AdmiralDrama20000000  
Butterfly on a WheelThriller20000000Pierce Brosnan 
DoubtDrama20000000Meryl Streep 
Jackass 3-DComedy20000000Johnny KnoxvilleJeff Tremaine
Kickin' It Old SkoolComedy20000000Jamie KennedyHarvey Glazer
Mr. BrooksThriller20000000Kevin CostnerBruce A. Evans
ObsessedThriller20000000Beyonce Knowles 
PremonitionThriller20000000Sandra Bullock 
Saw 3DHorror20000000Tobin Bell 
She's Out of My LeagueRomantic20000000Jay BaruchelJim Field Smith
SuperbadComedy20000000Jonah HillGreg Mottola
The HorsemenHorror20000000Dennis Quaid 
Vampires SuckHorror20000000Ken Jeong 
El Laberinto del Fauno (Pan's Labyrinth)Fantasy19000000 Guillermo del Toro
Leap YearRomantic19000000Amy Adams 
I Come with the RainThriller18000000  
Observe and ReportComedy18000000Seth RogenJody Hill
The MistHorror18000000Alexa DavalosFrank Darabont
The Illusionist (2010)Animation17000000 Sylvain Chomet
99 francsDrama16250000  
The Women (2008)Comedy16000000Meg RyanDiane English
Funny GamesHorror15000000Naomi Watts 
StilyagiMelodrama15000000  
TetroDrama15000000Vincent GalloFrancis Ford Coppola
The Hills Have Eyes 2Horror15000000  
TsarDrama15000000  
Whatever WorksComedy15000000Larry DavidWoody Allen
New York, I Love YouRomance14700000Justin Bartha* 
A Perfect GetawayThriller14000000Chris HemsworthDavid Twohy
GigolaDrama14000000  
The Life Before Her EyesDrama13000000Uma ThurmanVadim Perelman
Employee of the MonthComedy12000000Dane Cook 
Step UpMusic12000000Channing TatumAnne Fletcher
TriangleThriller12000000  
L'arbre (The Tree)Drama11046000  
Saw VHorror10800000Tobin BellDavid Hackl
Blonde AmbitionRomantic10000000Jessica Simpson 
MacGruberComedy10000000Will ForteJorma Taccone
Over Her Dead BodyFantasy10000000Lake Bell 
Samiy luchshiy film 2 (The Very Best Film 2)Comedy10000000  
Janosik. Prawdziwa historiaDrama8400000  
Black LightningAction8000000  
P2Horror8000000Wes Bentley 
JunoComedy7500000Ellen PageJason Reitman
CyrusComedy7000000Marisa Tomei 
KandagarAction7000000  
KatynDrama6250000  
Boy s tenyu 2 (Shadow Boxing 2)Action6000000  
City IslandComedy6000000Alan Arkin 
Nepobedimyy (Unbeatable)Action6000000  
Antidur (Antidope)Criminal5000000  
ArtefaktFantasy5000000  
High Security VacationComedy5000000  
Ironiya sudby. Prodolzhenie (The Irony of Fate 2)Melodrama5000000  
Stritreysery (Streetracers)War5000000  
V Tsenturiya. V poiskakh zacharovannykh sokrovishcAdventure5000000  
Laskovyy mayDrama4500000  
Samiy luchshiy film (The Very Best Film)Comedy4500000  
Bumaznyj soldat (Paper Soldier)Drama4000000  
Gora samotsvetov VAnimation4000000  
If I Had Known I Was a GeniusDrama4000000  
Lubov morkov 2 (Lovey Dovey 2)Comedy4000000  
ScoopRomantic4000000Woody AllenWoody Allen
Saint John of Las VegasComedy3800000Steve Buscemi 
De laatste dagen van Emma BlankComedy3720000  
Lyubov v bolshom gorode (Love in the Big City)Comedy3500000  
Tarif NovogodniyMelodrama3200000  
Frontière(s)Horror3000000  
Lubov morkov (Lovey-Dovey)Comedy3000000  
OrangeloveDrama3000000  
Free RainerDrama2600000  
Antikiller D.K: Lyubov bez pamyatiWar2500000  
The Power of FearHorror2500000  
Vsyo mogut koroliMelodrama2500000  
Love's ContractComedy2000000  
Nasha Russia: Yaytsa sudbyComedy2000000  
Pro lyuboffMelodrama2000000  
Skazhi LeoThriller2000000  
Kochaj i tanczMelodrama1965281  
Trick (2010)Action1600000  
Wojna polsko-ruskaDrama1440000  
Wszystko, co kocham (All That I Love)Drama1260000  
IndiMelodrama1200000  
General. Zamach na GibraltarzeWar1038000  
Zift (2008)Drama857000  
MonstersSci-Fi500000  
GolubkaDrama400000  
OnceMusic150000  
Idiots and AngelsAnimation125000