Movie Variety and the City

In Kyung Kim

doi:10.1515/bejeap-2019-0398

Published by De Gruyter May 13, 2020

Movie Variety and the City

In Kyung Kim

From the journal The B.E. Journal of Economic Analysis & Policy

https://doi.org/10.1515/bejeap-2019-0398

Showing a limited preview of this publication:

Abstract

In this article, I study the effect of entry and ownership structure on product variety within a city. Using longitudinal data on theaters in Korea, I find that the positive effect of entry on city-wide movie variety is limited only to the first few entrants. This finding, together with the observation that movie variety in a theater does not respond to entry, suggests that a theater's incentive to soften price competition by screening less popular movies not otherwise available in the city decreases as more theaters enter. I also find evidence that movie variety is greater in more concentrated cities, implying that a chain that owns multiple theaters in a city may differentiate the movie lineup offered in each theater more than when the theaters are individually owned in order to avoid cannibalization or to preempt entry.

Keywords: movie theater industry; ownership structure; product variety

JEL Classification numbers: L13; L22; L82

Corresponding author: In Kyung Kim, Department of Economics, Nazarbayev University, 010000, Astana, Kazakhstan, E-mail: in.kim@nu.edu.kz

Funding source: Ministry of Education of the Republic of Korea

Award Identifier / Grant number: AKS-2018-INC-2230011

Funding source: Nazarbayev University

Award Identifier / Grant number: SHSS2018004

Research funding: Financial support from the Seed Program for Korean Studies through the Ministry of Education of the Republic of Korea (AKS-2018-INC-2230011) and the Small Grant Program at Nazarbayev University (SHSS2018004) is gratefully acknowledged.
Competing interest: The author declares that he has no conflict of interest.

Appendix

Table A1:

Movie theaters in 2010.

Theater type	Theaters		Screens
Theater type	Number	%	Number	%
Chain	216	71.8%	1657	82.7%
CGV	77	25.6%	623	31.1%
Primus	25	8.3%	183	9.1%
Lotte	65	21.6%	478	23.9%
Cinus	33	11.0%	240	12.0%
Megabox	16	5.3%	133	6.6%
Non-chain	85	28.2%	346	17.3%
Total	301		2003

Table A2:

Demographic information (2010).

Variable	Small and mid-sized cities		Metropolitan cities
Variable	Avg.	Std. Dev.	Avg.	Std. Dev.
Population (in 1000)	365	251	3160	2978
Per capita income (in 10,000 US dollars)	2.4	1.3	2.3	1.3
% female	50.0	1.1	50.2	0.8
% 20s	12.7	2.3	14.5	1.2

Note: The table presents demographic information separately for the 47 small and mid-sized cities and the seven metropolitan cities in Korea for the year 2010. % female (% 20s) is the proportion of women (the proportion of people in their 20s). Source: Korean Statistical Information Service.

Table A3:

The joint distribution of entry and exit.

		Number of exits (%)
		0	1	2	3 +	Total
Number	0	31.9	10.6	2.1	0.0	44.7
of	1	12.8	6.4	2.1	0.0	21.3
entrants	2	6.4	4.3	2.1	6.4	19.1
(%)	3 +	0.0	10.6	2.1	2.1	14.9
	Total	51.1	31.9	8.5	8.5	100.0

Note: The table shows the joint frequency distribution of entry and exit of theaters in a city.

Table A4:

Separating the effects of entry and exit.

Variable	City-level variety		Theater-level variety
Variable	(1)	(2)	(3)	(4)
Entry	2.618***	2.870***	−0.055	−0.033
	(0.686)	(0.747)	(0.149)	(0.212)
Entry × Large city		−1.724*		−0.039
		(0.963)		(0.241)
Exit	−0.940	−0.764	−0.216	0.117
	(0.601)	(0.551)	(0.139)	(0.221)
Exit × Large city		−0.378		−0.407*
		(0.864)		(0.227)
Number of events
Entry	14		44
Exit	12		48
Observations	1150		4731

Note: The table presents estimation results of model (4) where movie variety in a theater and movie variety in a city are used as the dependent variable one by one. Standard errors (clustered by event) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Table A5:

Entry and city-level movie variety using screen counts as the measure of competitive intensity.

Variable	All movies		First-run movies
Variable	(1)	(2)	(3)	(4)
1[Number of screens8≥j]
j = 2	2.224***	2.164***	2.088***	1.996***
	(0.306)	(0.486)	(0.265)	(0.391)
j = 3	2.901***	3.063***	2.767***	2.767***
	(0.480)	(0.503)	(0.483)	(0.449)
j = 4	1.492*	1.522**	1.203*	1.236**
	(0.800)	(0.688)	(0.605)	(0.561)
j = 5	−1.268	−0.874	−1.132*	−0.734
	(0.806)	(0.693)	(0.617)	(0.561)
j = 6	0.438	0.298	0.318	0.188
	(0.648)	(0.620)	(0.617)	(0.620)
Fixed effects
City	Yes	Yes	Yes	Yes
Year-month	Yes	No	Yes	No
Province-year-month	No	Yes	No	Yes
R-squared	0.385	0.430	0.424	0.470
Observations	8805	8805	8805	8805

Note: The table presents estimation results of model (1) where Moviesit is the dependent variable and the indicators for the number of theaters are replaced with another set of indicators: 1[Number of screens8≥j],j=2,⋯,6. Standard errors (clustered by city) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Table A6:

Entry and theater-level movie variety using screen counts as the measure of competitive intensity.

Variable	All movies		First-run movies
Variable	(1)	(2)	(3)	(4)
1[Competitors’ screens8≥j]
j = 1	−0.138	−0.138	−0.039	−0.026
	(0.276)	(0.337)	(0.170)	(0.205)
j = 2	−0.027	0.118	−0.091	0.051
	(0.166)	(0.178)	(0.142)	(0.156)
j = 3	0.271*	0.252*	0.196	0.156
	(0.149)	(0.145)	(0.138)	(0.132)
j = 4	−0.268	−0.294	−0.245	−0.254
	(0.201)	(0.194)	(0.207)	(0.194)
j = 5	−0.107	−0.166	−0.099	−0.172
	(0.129)	(0.132)	(0.127)	(0.130)
j = 6	0.079	0.037	0.083	0.065
	(0.132)	(0.160)	(0.128)	(0.149)
Number of screens	0.826***	0.897***	0.747***	0.825***
	(0.201)	(0.166)	(0.157)	(0.128)
1[Entering week]	−2.106***	−2.045***	−3.265***	−3.244***
	(0.502)	(0.517)	(0.313)	(0.317)
1[Exiting week]	−2.818***	−2.798***	−2.961***	−2.939***
	(0.344)	(0.351)	(0.344)	(0.340)
Fixed effects
Theater	Yes	Yes	Yes	Yes
Year-month	Yes	No	Yes	No
Province-year-month	No	Yes	No	Yes
R-squared	0.295	0.318	0.317	0.341
Observations	25,732	25,732	25,732	25,732

Note: The table presents estimation results of (2) where Moviesct is the dependent variable and the indicators for the number of competitors are replaced with another set of indicators: 1[Competitors’ screens8≥j],j=1,⋯,6. Standard errors (clustered by theater) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Table A7:

City-level movie variety: additional robust analysis.

	Log-linear specification				Dropping outliers
	All movies		First-run movies		All movies		First-run movies
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
1[N ≥ j]
j = 2	0.202***	0.161***	0.198***	0.158***	1.916***	1.420**	1.850***	1.404***
	(0.052)	(0.055)	(0.048)	(0.046)	(0.568)	(0.564)	(0.508)	(0.450)
j = 3	0.148**	0.166**	0.131**	0.149**	2.177***	2.464***	1.961**	2.212***
	(0.062)	(0.065)	(0.058)	(0.060)	(0.790)	(0.711)	(0.758)	(0.661)
j = 4	−0.018	−0.026	−0.020	−0.027	0.104	0.316	0.038	0.235
	(0.059)	(0.053)	(0.055)	(0.050)	(0.610)	(0.358)	(0.576)	(0.344)
j = 5	0.006	−0.002	0.007	−0.001	0.436	0.459	0.418	0.436*
	(0.018)	(0.025)	(0.017)	(0.022)	(0.299)	(0.295)	(0.284)	(0.246)
j = 6	0.070*	0.074	0.050*	0.055	0.968	0.862	0.555	0.470
	(0.036)	(0.049)	(0.028)	(0.043)	(0.672)	(0.742)	(0.555)	(0.619)
Fixed effects
City	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year-month	Yes	No	Yes	No	Yes	No	Yes	No
Province-year-month	No	Yes	No	Yes	No	Yes	No	Yes
R-squared	0.365	0.424	0.372	0.433	0.379	0.443	0.394	0.458
Observations	8805	8805	8805	8805	8793	8793	8793	8793

Note: The table presents results of additional robustness analysis. Columns (1)–(4) consider log-linear specifications, and columns (5)–(8) drop observations where the number of movies in the city exceeds 30. Standard errors (clustered by city) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Table A8:

Theater-level movie variety: additional robust analysis.

	Log-linear specification				Dropping outliers
	All movies		First-run movies		All movies		First-run movies
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
1[Competitors ≥ j]
j = 1	−0.001	−0.007	−0.001	−0.006	−0.011	0.011	−0.047	−0.042
	(0.017)	(0.022)	(0.013)	(0.017)	(0.152)	(0.190)	(0.157)	(0.195)
j = 2	0.014	0.026	0.007	0.019	0.142	0.268	0.096	0.238
	(0.021)	(0.026)	(0.018)	(0.022)	(0.196)	(0.201)	(0.186)	(0.193)
j = 3	0.003	0.004	−0.003	−0.002	0.012	0.074	−0.037	0.009
	(0.017)	(0.016)	(0.015)	(0.014)	(0.157)	(0.151)	(0.158)	(0.145)
j = 4	0.000	−0.002	0.000	−0.005	0.041	−0.007	0.049	−0.027
	(0.015)	(0.017)	(0.013)	(0.015)	(0.129)	(0.153)	(0.128)	(0.145)
j = 5	−0.006	−0.007	−0.006	−0.005	−0.084	−0.131	−0.107	−0.125
	(0.012)	(0.013)	(0.010)	(0.011)	(0.113)	(0.122)	(0.109)	(0.116)
j = 6	−0.007	−0.010	−0.006	−0.010	−0.027	−0.072	−0.032	−0.096
	(0.017)	(0.014)	(0.015)	(0.013)	(0.138)	(0.131)	(0.141)	(0.132)
Number of screens	0.074***	0.081***	0.065***	0.072***	0.743***	0.809***	0.722***	0.795***
	(0.014)	(0.012)	(0.011)	(0.009)	(0.176)	(0.140)	(0.157)	(0.122)
1[Entering week]	−0.312***	−0.306***	−0.387***	−0.386***	−2.799***	−2.762***	−3.381***	−3.362***
	(0.057)	(0.059)	(0.050)	(0.050)	(0.394)	(0.404)	(0.284)	(0.286)
1[Exiting week]	−0.383***	−0.379***	−0.358***	−0.353***	−2.783***	−2.752***	−2.942***	−2.905***
	(0.065)	(0.065)	(0.052)	(0.052)	(0.340)	(0.345)	(0.347)	(0.343)
Fixed effects
Theater	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year-month	Yes	No	Yes	No	Yes	No	Yes	No
Province-year-month	No	Yes	No	Yes	No	Yes	No	Yes
R-squared	0.279	0.301	0.297	0.320	0.309	0.333	0.316	0.341
Observations	25,732	25,732	25,732	25,732	25,658	25,658	25,658	25,658

Note: The table presents results of additional robustness analysis. Columns (1)–(4) consider log-linear specifications, and columns (5)–(8) drop observations where the number of movies in the theater exceeds 20. Standard errors (clustered by theater) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Table A9:

Ownership structure: additional robust analysis.

	Log-linear specification				Dropping outliers
	All movies		First-run movies		All movies		First-run movies
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
HHI	0.650***	0.694***	0.663***	0.706***	0.001***	0.001***	0.001***	0.001***
	(0.103)	(0.108)	(0.107)	(0.112)	(0.000)	(0.000)	(0.000)	(0.000)
Market screen count	0.093***	0.096***	0.093***	0.096***	0.801***	0.810***	0.793***	0.801***
	(0.012)	(0.013)	(0.012)	(0.013)	(0.083)	(0.090)	(0.080)	(0.087)
Market screen count²	−0.001***	−0.001***	−0.001***	−0.001***	−0.010***	−0.010***	−0.010***	−0.010***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.002)	(0.001)	(0.001)
Fixed effects
Province	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year-month	Yes	No	Yes	No	Yes	No	Yes	No
Province-year-month	No	Yes	No	Yes	No	Yes	No	Yes
R-squared	0.693	0.716	0.694	0.716	0.722	0.742	0.728	0.748
Observations	8805	8805	8805	8805	8793	8793	8793	8793

Note: The table presents results of additional robustness analysis. Columns (1)–(4) consider log-linear specifications, and columns (5)–(8) drop observations where the number of movies in the city exceeds 30. In columns (1)–(4), HHI is re-scaled to have values between 0 and 1. Standard errors (clustered by city) are in parentheses. The notation *** indicates significance at 1% level, ** at 5% level, * at 10% level.

Figure A1:

Separating the effects of entry and exit. Notes: The four panels of the figure show estimated variety relative to one month prior to entry or exit along with 95% confidence bands.

References

Anderson, S. P., and A. de Palma. 1992. “Multiproduct Firms: A Nested Logit Approach.” Journal of Industrial Economics 40 (3): 261–76. https://doi.org/10.2307/2950539.Search in Google Scholar

Argentesi, E., P. Buccirossi, R. Cervone, T. Duso, and A. Marrazzo. 2016. The Effect of Retail Mergers on Prices and Variety: An Ex-Post Evaluation. Discussion Paper, DICE Discussion Paper.Search in Google Scholar

Berry, S., and J. Waldfogel. 2001. “Do Mergers Increase Product Variety? Evidence From Radio Broadcasting.” Quarterly Journal of Economics 116 (3): 1009–25. https://doi.org/10.1162/00335530152466296.Search in Google Scholar

Cachon, G. P., C. Terwiesch, and Y. Xu. 2008. “On the Effects of Consumer Search and Firm Entry in a Multiproduct Competitive Market.” Marketing Science 27 (3): 461–73. https://doi.org/10.1287/mksc.1070.0304.Search in Google Scholar

Carlton, D. W., and J. D. Dana. 2008. “Product Variety and Demand Uncertainty: Why Markups Vary with Quality.” Journal of Industrial Economics 56 (3): 535–52. https://doi.org/10.1111/j.1467-6451.2008.00353.x.Search in Google Scholar

Chisholm, D. C., M. S. McMillan, and G. Norman. 2010. “Product Differentiation and Film-programming Choice: Do First-run Movie Theatres Show the Same Films?.” Journal of Cultural Economics 34 (2): 31–145. https://doi.org/10.1007/s10824-010-9118-y.Search in Google Scholar

Courty, P., and J. Nasiry. 2018. “Loss aversion and the uniform pricing puzzle for media and entertainment products.” Economic Theory 66 (1): 105–40. https://doi.org/10.1007/s00199-017-1055-y.Search in Google Scholar

Davis, P. 2005. “The Effect of Local Competition on Retail Prices in the US Motion Picture Exhibition Market.” Journal of Law and Economics 48 (2): 677–708. https://doi.org/10.1086/428018.Search in Google Scholar

George, L. 2007. “Whats Fit to Print: The Effect of Ownership Concentration on Product Variety in Daily Newspaper Markets.” Information Economics and Policy 19 (3): 285–303. https://doi.org/10.1016/j.infoecopol.2007.04.002.Search in Google Scholar

Honhon, D., V. Gaur, and S. Seshadri. 2010. “Assortment Planning and Inventory Decisions Under Stockout-Based Substitution.” Operations Research 58 (5): 1364–79. https://doi.org/10.1287/opre.1090.0805.Search in Google Scholar

Hotelling, H. 1929. “Stability in competition.” Economic Journal 39 (153): 41–57. https://doi.org/10.2307/2224214.Search in Google Scholar

Kaiser, U., and M. Reisinger. 2017. Strategic Product Variety Choice: Theory and Empirical Evidence. Working Paper.Search in Google Scholar

Kim, I. K. 2018. Effects of Chain Affiliation in the Movie Theater Industry. Available at SSRN 3325591.10.2139/ssrn.3325591Search in Google Scholar

Kim, I. K. 2019. Organizational Form and Performance: The Cinema Chain Case. Available at SSRN 3424510.10.2139/ssrn.3424510Search in Google Scholar

Lancaster, K. 1979. Variety, Equity, and Efficiency. New York: Columbia University Press.10.7312/lanc94538Search in Google Scholar

Matsa, D. A. 2011. “Competition and Product Quality in the Supermarket Industry.” Quarterly Journal of Economics 126 (3): 1539–91. https://doi.org/10.2139/ssrn.1440414.Search in Google Scholar

Olivares, M., and G. P. Cachon. 2009. “Competing Retailers and Inventory: An Empirical Investigation of General Motors' Dealerships in Isolated U.S. Markets.” Management Science 55 (9): 1586–604. https://doi.org/10.1287/mnsc.1090.1050.Search in Google Scholar

Orbach, B. Y., and L. Einav. 2007. “Uniform Prices for Differentiated Goods: The Case of the Movie-Theater Industry.” International Review of Law and Economics 27 (2): 129–53. https://doi.org/10.1016/j.irle.2007.06.002.Search in Google Scholar

Orhun, A. Y., S. Venkataraman, and P. K. Chintagunta. 2015. “Impact of Competition on Product Decisions: Movie Choices of Exhibitors.” Marketing Science 35 (1): 73–92. https://doi.org/10.1287/mksc.2015.0909.Search in Google Scholar

Ren, C. R., Y. Hu, Y. J. Hu, and J. Hausman. 2011. “Managing Product Variety and Collocation in a Competitive Environment: An Empirical Investigation of Consumer Electronics Retailing.” Management Science 57 (6): 1009–24. https://doi.org/10.1287/mnsc.1110.1327.Search in Google Scholar

Ryzin, G. V., and S. Mahajan. 1999. “On the Relationship Between Inventory Costs and Variety Benefits in Retail Assortments.” Management Science 45 (11): 1496–509. https://doi.org/10.1287/mnsc.45.11.1496.Search in Google Scholar

Salop, S. C. 1979. “Monopolistic Competition with Outside Goods.” Bell Journal of Economics 141–56. https://doi.org/10.2307/3003323.Search in Google Scholar

Smith, S. A., and N. Agrawal. 2000. “Management of Multi-item Retail Inventory Systems with Demand Substitution.” Operations Research 48 (1): 50–64. https://doi.org/10.1287/opre.48.1.50.12443.Search in Google Scholar

Sorensen, A. T. 2007. “Bestseller Lists and Product Variety.” Journal of Industrial Economics 55 (4): 715–38. https://doi.org/10.1111/j.1467-6451.2007.00327.x.Search in Google Scholar

Sweeting, A. 2010. “The Effects of Mergers on Product Positioning: Evidence from the Music Radio Industry.” Rand Journal of Economics 41 (2): 372–97. https://doi.org/10.1111/j.1756-2171.2010.00104.x.Search in Google Scholar

Watson, R. 2009. “Product Variety and Competition in the Retail Market for Eyeglasses.” Journal of Industrial Economics 57 (2): 217–51. https://doi.org/10.1111/j.1467-6451.2009.00381.x.Search in Google Scholar

Received: 2019-09-11

Accepted: 2020-04-01

Published Online: 2020-05-13

Movie Variety and the City

Abstract

References

Journal and Issue

Articles in the same Issue