This document analyzes factors that contribute to blockbuster success in the motion picture markets of Ukraine, Poland, and CIS countries. It summarizes previous literature on this topic and presents an empirical analysis using box office data from 2007-2010. Regression models show that higher budgets, sequels, and local country of production positively impact total revenue. Weekend box office revenues are also strongly dependent on the previous weekend's success. The findings provide insights into the peculiarities of post-Soviet movie markets.

1. Kyiv School of Economics
What makes a blockbuster?
Peculiarities of Ukraine
Yevgen Nasadyuk
June 17, 2011 1

3. Content
 Motivation
 Literature review
 Methodology
 Data
 Results
 Conclusions
3

4. Motivation
 Specific market structure of motion pictures industry in
Ukraine;
 Absence of previous research of Post-Soviet movie
markets.
4

5. Literature review
One of the earliest theory (Litman, 1983) maintains that
motion picture success is dependent on three decision-
making areas:
 the creative sphere;
 the scheduling and release pattern;
 the marketing effort.
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6. Literature review
 Blockbuster hypothesis (DeVany 2004, “Hollywood
Economics”): big budget leads to higher box office.
 Also positive effect of: a presence of star-actors, sequel,
animated and action genres on movie revenue.
 Bagella and Becchetti (1999): Italian comedies are most
successful among locally produced movies.
 Wall (2009): high return to information for Thailand
motion picture market. Dependent variables: opening
weekend box office shares and movie life-time.
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7. My contribution
This study extend existing literature in few ways:
 for the first time provides economic approach of motion
pictures success for Post-Communist countries
examining dependence of total movie box office on its
budget, release date, genre and country of production.
 applies time series dynamics for weekend box offices
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8. The methodology
Null-hypothesis: higher budget higher box offices.
Model: modified DeVany (2004)
log Re venuei  1   2 log Budgeti  3 StarActor   4 StarDirector 
 5Countryi   6 Sequeli  [Genre]i  i
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9. The methodology
 For autoregression model we use movie weekend’s
box office shares:
BOit
Sit  K
 BO
k 1
kt
 AR model:
S it  a 0  a1 S it 1   i
 Model to check date effect:
log Re venueit   0  [ Date]i   i

10. Data
Input data contains 11062 records of weekend box office for 1413 released
movies from 2007 to 2010 year in Ukraine, Poland and CIS. Sources of data
are public available movies’ databases http://boxofficemojo.com,
http://kinopoisk.ru, http://pisf.pl
Table 1. Aggregated statistics of weekend records
Poland CIS Ukraine Total
Total records 2,247 6,460 2,355 11,062
Movies 587 1,239 566 1,413
Average duration 3.81 4.87 3.91 4.45
Weekend average box office 190,054 351,691 76,919 260362
Average box office share 0.08 0.031 0.086 0.053
Average screens 74.7 107.4 35.4 85.9
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11. Data
Sample for OLS regression:
 247 motion pictures (more than 50% of total box office)
 114 actors / actresses in 144 (58%) movies
 101 directors in 115 (46%) movies.
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12. Data
80000000 350000000
70000000 300000000
60000000
250000000
50000000
200000000
40000000
150000000
30000000
100000000
20000000
10000000 50000000
Total box ofice
Budget
0 0
Rank 0 50 100 150 200 250 300 Rank 0 50 100 150 200 250 300
a) Box office revenue b) Budget
Figure 1. Total revenue and budget distribution across motion pictures

13. Data
Table 2. Genre statistics
Poland CIS Ukraine
Action 17 23 21
Adventure 3 5 3
Crime 0 3 1
Thriller 9 12 10
War 3 3 4
Western 0 1 1
Horror 12 14 9
Action 44 61 49
Animation 18 23 20
Documentary 0 1 0
Sci-Fi 7 9 6
Period 4 3 3
Sci-Fi 11 13 9
Drama 13 24 13
Family 8 7 7
Fantasy 13 16 14
Melodrama 1 6 4
Music 3 3 3
Romantic 10 9 11
Drama 48 65 52
Comedy 15 30 20
Total 136 192 150
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14. Data
Figure 2. Distribution of box offices across movies producers

15. Results
Table 4. Result of autoregression model
Poland CIS Ukraine
VARIABLES Share of Share of box Share of
box office office box office
Lag of BO share 0.69*** 0.48*** 0.46***
(0.007) (0.003) (0.01)
Constant -0.001 -0.001*** 0.004**
(0.001) (0.0004) (0.002)
Observations 1,510 4,456 1,461
R-squared 0.84 0.78 0.55
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16. Results
Table 5. Month effect estimation
Poland CIS Ukraine
VARIABLES LogRevenue LogRevenue LogRevenue
January 0.78*** 0.96*** 0.67***
February 1.01*** 0.93*** 0.19
March 0.41*** 0.76*** 0.23
April 0.18 0.58*** 0.12
May 0.38*** 0.20 -0.04
June 0.09 0.35** 0.30
July -0.22* 0.17 0.22
August 0.30** 0.36** -0.04
September 0.43*** 0.57*** -0.04
October 0.44*** 0.32** 0.12
November 0.49*** 0.18 0.11
Constant 10.98*** 9.53*** 9.66***
Observations 2,247 6,460 2,354
R-squared 0.04 0.01 0.01
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17. Results
Table 6. Regression with aggregated genres and country of production
Poland CIS Ukraine
VARIABLES Logarithm of Logarithm of Logarithm of total
total revenue total revenue revenue
Log of budget 0.48*** 1.03*** 0.94***
(0.12) (0.09) (0.1)
Sequel 0.72** 0.68** 0.66***
(0.29) (0.27) (0.21)
Star director 0.22 -0.01 -0.03
(0.29) (0.28) (0.24)
Star actor / actress 0.32 0.24 0.3
(0.38) (0.36) (0.32)
Animation 1.38*** 0.04 0.09
(0.38) (0.35) (0.29)
Sci-Fi 0.6 0.11 0.32
(0.44) (0.43) (0.37)
Drama 0.41 -0.29 0.02
(0.27) (0.24) (0.2)
Comedy 0.1 0.02 0.62**
(0.39) (0.31) (0.27)
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18. Results
Table 6. Regression with aggregated genres and country of production - Continued
Poland CIS Ukraine
VARIABLES Logarithm of Logarithm of Logarithm of
total revenue total revenue total revenue
US 2.35* 2.52*** 0.14
(1.33) (0.81) (1.02)
Russia 0 5.03*** 2.58**
(0) (0.87) (1.0)
Poland 4.89*** 0 -2.67*
(1.43) (0) (1.46)
Europe 1.06 1.7* -0.63
(1.38) (0.88) (1.08)
Ukraine 0 0 0.18
(0) (0) (1.47)
Constant 1.48 -6.44*** -4.92**
(2.58) (1.79) (2.11)
Observations 136 192 150
R-squared 0.44 0.58 0.57

19. Conclusions
 Blockbuster hypothesis that bigger budget increases
revenue, is not rejected for all countries (similar to
DeVany, McKenzie)
 Weekend box office strongly depend on success of
previous weekend (DeVany, Litman, Collins et al.)
 Positive effect of local produced movies (similar to Wall,
Bagella and Becchetti, Zarin-Nejadan and Criado)
 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.
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20. Conclusions
 Main peculiarities of Ukrainian movie market:
i. faster decreasing of weekend box office revenue
ii. small seasonal effect.
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21. Thank you for attention!
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