Economics Of The Movie Business Essay Example
Economics Of The Movie Business Essay Example

Economics Of The Movie Business Essay Example

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  • Pages: 10 (2656 words)
  • Published: July 25, 2017
  • Type: Essay
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This section will discuss the evolution of blind bidding in the film industry, with a focus on its development from the Golden Age of Hollywood (1930s-1940s) to its end in early 1986. Theatre owners did not worry about blind bidding for many years because it was not the primary method used to license movies during this time. Block booking, which involved selling both high and low quality films as a package without allowing theatre owners to screen them, was how most movies were licensed during this era. However, everything changed after the landmark United States vs.Paramount et al. decision by the Supreme Court in 1948, which found violations of the Sherman Act by five major film companies that produced, distributed and owned theatres as well as three studios that didn't own theatres. One significant outcome of this ruling was the elimination of


block booking. Following this decision, movie licenses were granted through product splitting, open bidding or blind bidding. Product splitting involved theatre owners deciding among themselves who had first negotiating rights for a given movie with a film studio in a particular market.Theatre owners had the opportunity to bid on movies through open auction after screening them, a process known as open bidding. Blind command was rare until the 1960s and was restricted from 1969 to 1971 by an agreement between film companies and the Department of Justice. This agreement limited blind command to three movies per studio per year and was renewed twice before being revoked on January 1, 1975. Despite this, movie companies found blind command necessary for funding blockbuster movies, leading to its persistence from 1975-1985. Chapter 2 reviews literature o

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blind command, natural experiments, and policy impacts with Section 2.1 discussing two studies that arrive at different conclusions regarding anti-blind command laws' impact on mean commands and returns for theatre owners using blind bidding. Blumenthal (1998) finds that average commands decrease for blind-bidding theatre proprietors but lead to higher returns while the writer argues that risk-averse proprietors fare worse under blind bidding due to greater volatility in their returns. Forsythe, Isaac, and Palfrey's (1989) study found anti-blind-command laws unnecessary in sealed-bid first-price auctions as purchasers would eventually learn that withholding information benefits marketers.Despite economic theory suggesting that bidders in auctions with uncertain merchandise value reduce their bidding to avoid losses, the film industry appears to contradict this belief. Film companies continue to trade highly anticipated movies while screening unfavourable ones. Blumenthal's (1988) justification for theatre owners seeking relief from blind command is due to lower utility in blind-bid environments compared to preview ones. To investigate this theory, the author analysed 18 movies from a national theatre chain in 1982 using generalized least squares. It was hypothesised that theatre owners in blind-bid provinces would submit lower commands based on economic theory. In blind-bid theatres, limited information contained within offer letters plays a larger role in determining bids when compared to trade screen venues. Blind-bid theatres have higher mean returns but also experience higher volatility. Dependent variables tested include movie commands and returns, while predictors include movie budget and release size, theatre operating expenses, an index variable identifying theatres in blind-bidding areas and the number of theatres in metropolitan areas. Film revenue is calculated by subtracting movie cost from box office gross earnings.The author of the text

conducted a test on movie budget and release size by interacting it with the blind-bidding variable. It was found that theater owners in blind-bidding areas submitted lower average bids compared to trade screen ones. Blind-bid theater owners placed greater emphasis on offer letter information, resulting in a decrease in bidding for every million-dollar increase in movie cost. Theater proprietors bid $8,900 and $5,100 extra amounts for blind command and trade screen 1s respectively. According to Blumenthal's theory of public-service utility, risk-averse theatre proprietors fare worse due to increased volatility despite heightened revenues but cannot reduce their bids due to competitive pressure. Forsythe, Isaac, and Palfrey (1989) conducted an experiment concluding that anti-blind command laws are unnecessary as buyers assume that a seller's decision to use blind command points is unfavorable leading to most points no longer being blindly commanded. The experiment involved a single marketer competing with multiple buyers where the former decides whether to disclose positive or negative information about auctioned points resulting in the highest bid if the marketer chooses to reveal such information.The auctioned point has both a common value and private value element, and it is sold through a first price auction with certain commands. The game considers various Nash equilibria, but the writers focus on the "presume the worst" solution as it is the only achievable result if the auction follows a consecutive equilibrium. This type of equilibrium occurs when buyers guess about a seller's motives and adopt a strategy that aligns with their best interest. To achieve this solution, sellers continue to conceal bid points until there are no unsuspecting buyers left who bid without assuming the worst. Eventually,

buyers learn that withholding information is not in their best interest, forcing sellers to divulge information for lower quality levels until blind bids disappear entirely. In five of six auctions using blind bidding, the average winning bid decreases over time and buyers drastically lower their expectations for item values. Therefore, anti-blind bidding laws are unnecessary because blind bidding would phase out over time according to these studies' conclusions. These two studies provide crucial insights into auctions.Blumenthal (1988) discovered that blind bidding can negatively impact theatre proprietors. However, converting to a multifaceted theatre can help reduce the risk of showing mediocre or poor movies. Forsythe, Isaac, and Palfrey (1989) suggest that combining the risks of showing both mediocre and blockbuster movies is preferable to exhibiting a single inferior movie. Film companies may have withheld information about their blockbuster movies before this change due to opportunism and fear of not obtaining the highest auction price. Thus, cost benefits from blind bidding may have outweighed trading screen movies. This section discusses three natural experiments as references for testing anti-blind bidding laws' effects on outcomes such as issue, admission prices, and delays. Natural experiments compare two similar groups exposed or not exposed to policy changes to observe differences in results; researchers have little control over observed situations in quasi-experiments compared to social experiments with proper experimental design (Card & Krueger 1994; Milyo & Wardfogel 1999).(2004) and Milyo and Wardfogel (1999) both investigate the effects of exogenous changes in jurisprudence on retail markets. Card and Krueger's (1994) study examines the impact of a 50-cent increase in New Jersey's fast food industry's minimum wage on employment, while Milyo and Wardfogel (1999)

investigate the lifting of a ban on spirits advertisement in Rhode Island. Bergen et al.'s (2004) research analyzes point pricing laws' net effects for supermarkets, which require retailers to individually label each item with a price. All three studies are natural experiments conducted in geographically comparable areas, where it is presumed that intervention effects are not correlated with outcome variables or uncontrolled independent variables associated with them. Additionally, Card and Krueger compare New Jersey and Pennsylvania descriptive statistics such as wages, prices, and employment measures descriptively; for example, New Jersey has an average starting wage of $4.61 compared to Pennsylvania's $4.63.Both the 2004 study by Bergen et al. and the 1999 study by Milyo and Wardforgel examine the effects of point pricing laws in specific regions. Bergen et al.'s research focuses on a narrow tri-state area consisting of Clifton, New Jersey, Tarrytown, New York, and Greenwich, Connecticut, while Milyo and Wardforgel analyze Southern Rhode Island, Northwest Boston suburbs, and the Rhode Island-Massachusetts border. These regional targets were selected based on geographic proximity as well as similarities in population size, density, and access to quality public schools. Both studies use multiple control groups to observe how different variables impact their findings. Additionally, Card and Krueger's 1994 study compares full-time-equivalent employment between New Jersey and Pennsylvania while also comparing employment in New Jersey fast food shops paying at least the new minimum wage compared to those paying under it. Similarly, Milyo and Wardforgel compare retail prices in Rhode Island with those in Massachusetts using Rhode Island wholesale prices as a second control group. Lastly,Bergen et prices in New Jersey to those in two other states with

point pricing laws - New York and Connecticut - where Connecticut exempted shops from a pricing jurisprudence that required an electronic shelf label system ensuring price accuracy on shelves being equal to those at checkout registers; thus they used Connecticut shops which did not comply with this regulation as samples for comparison purposes.The author employed the concept of multiple control groups to analyze theater proprietorship, making reference to Card and Krueger's 1994 research that utilized the difference-in-differences calculator for assessing admission prices. This approach has an advantage in eliminating industry-related factors common to both intervention and control groups. The author leveraged these empirical studies for a natural experiment on anti-blind command laws, where it was important to select homogeneous intervention and control groups for credible outcomes. To bolster the experiment's robustness, several control groups were encouraged. The paper utilizes Card and Krueger's technique (1994) along with the difference-in-differences calculator to scrutinize admission prices by examining the effect of anti-blind command laws alleged by theatre owners and film companies. Specifically, this study focuses on Ohio and Pennsylvania's strictest laws which eliminated blind command while imposing severe limitations on warrants as they offer strong evidence supporting proprietors' claims about their impact. To compare admission prices pre-and post-implementation of these laws, Cleveland and Detroit were chosen in Ohio while Philadelphia, Pittsburgh, and Detroit were selected in Pennsylvania. Using the difference-in-differences calculator led to findings suggesting that there was a rise in admission prices due to these laws' implementation.Theater owners claimed that higher admission prices were a result of blind command, which covered losses from bad movies and paid warrants. They believed anti-blind command laws in some states would

guarantee elimination of this burden. Initially, movie companies considered comparing mean monetary values between Philadelphia and Pittsburgh to those in Manhattan but decided against it due to the high cost of living there. Both theater owners and movie companies argued that anti-blind command laws could lead to delays in movie releases, resulting in increased costs for consumers. They also claimed that bidding wars for movie leases would further hike up costs. However, economic principles state that demand for factor inputs is derived from demand for the final product; thus, admission prices at theaters vary based on factors such as film star popularity and ticket sales rather than external industry factors like rent or wages. Variable costs may increase due to minimum wage disparities across different states.The implementation of anti-blind bidding laws could potentially affect admission prices for movies if they limit the supply or cause additional expenses for film companies. Data was collected from weekly Variety reports on theaters in 15 cities, with a sample size ranging from 10-20 theaters per city (excluding those charging one dollar admission). Table 5.1 displays descriptive statistics on both downtown and suburban theaters in metropolitan areas, including Detroit's options such as Adams, Fox, Renaissance, Dearborn, Americana West, and Macomb Mall. Due to the new laws regarding blind bidding practices, it is unclear which movies were bid on during the year of implementation since bids are typically made six to twelve months before screening. Ohio passed their law in October 1978 but bids may have been placed between April to October 1979 while Pennsylvania's law was effective May 1980 with bids being made for screenings between November 1980 to May

1981. Warner Brothers' distributional president Barry Reardon stated that conforming to these laws resulted in an added expense of around $50k per movie screen.The text discusses the use of mean admission values to investigate the delayed effects of anti-blind command jurisprudence on movies, using two treatment and control groups. Descriptive statistics are presented in Table 3.1 for these variables. The immediate effect of the Ohio law is measured by comparing mean monetary values in 1976 and 1977 to those in 1979 and 1980, while mean monetary values in 1975 and 1976 are compared to those in 1980 and 1981 for both groups before and after the Pennsylvania law. This comparison is done using a difference-in-differences calculator as a natural experiment to measure the impact of each law, assuming that the intervention and control groups are indistinguishable except for the new legislation. Additionally, it is noted that this method has an advantage over comparing agencies of intervention/control groups after the laws have been implemented.This technique relies on less assumptive factors affecting admission prices that may have similarly influenced both groups. To understand the significance of the difference-in-differences calculator, one must analyze the initial differences between the intervention and control groups. The change in price in the control group determines how prices would have appeared in the intervention group if no law had been implemented. Conversely, changes in pricing in the intervention group demonstrate how average prices behaved after implementing the law. By calculating second differences, this calculator assesses the impact of the law by measuring disparities between what occurred with mean prices versus what would have happened without any action taken. Figure 5.1 displays mean admission

prices for Detroit and Cleveland from 1975-1981, where Detroit's mean values remained consistently higher than Cleveland's by approximately 59 cents throughout this time period.By examining mean admission prices over time, we can determine if similar factors influence these values for both treatment and control groups. When there is a discrepancy in pricing prior to intervention, unobserved variables are more likely to be dissimilar.Table 3.2 presents the results of the difference-in-differences calculator, which show that before implementing anti-blind command laws, Cleveland and Detroit had stable average admission monetary values. Two years after enacting the law, both cities saw an increase in mean monetary values; Detroit's increased by seven cents and Cleveland's increased by 16 cents. The latter represents what would have happened if the law had not been implemented. Second differences revealed that Ohio Law significantly raised Cleveland's mean monetary values by nine cents. However, looking at admission monetary values three years before and after implementation led to different conclusions: both cities experienced a 20-21 cent increase in mean monetary values, with Cleveland having lower average admission prices by one cent according to the difference-in-differences calculator. Figure 5 shows Philadelphia, Pittsburgh, and Detroit's admission monetary values while Table 5.3 displays norm monetary value data for these cities from 1977 to 1983 where prices were nearly identical during the first two years but differed steadily in later years with a steady difference of ten and fifteen cents seen for Pittsburgh and Philadelphia respectively.From 1979 to 1980, there was a significant increase in the difference between mean monetary values, ranging from 36 to 41 cents. It can be assumed that this trend continued as evidenced by sustained differences

in those years. The table displays first and second differences of mean admittance monetary values. Upon analysis of the data, it was discovered that two years before and after the anti-blind command law passed, Pittsburgh's and Philadelphia's mean monetary values increased by 43 cents; however, Detroit only increased by 11 cents. If Pennsylvania had not enacted this law, Detroit's prices would have behaved similarly to those in Pittsburgh and Philadelphia. A difference-in-differences calculator revealed that this law resulted in a statistically significant increase of 32 cents for admittance monetary values. Comparing three years before and after the passing of the law showed an even greater increase of 53 cents for mean admittance monetary values in both Philadelphia and Pittsburgh. An examination into the impact of anti-blind laws on admission prices found higher prices in Cleveland, Philadelphia, and Pittsburgh across three out of four difference-in-differences calculations for Ohio and Pennsylvania.Although Pennsylvania's law had a greater effect than Ohio's, resulting in a reduction of one cent in average admission prices, it is feasible that film studios faced increased peripheral expenses in states with anti-blind laws because they had to create distinct sales prints exclusively for testing films intended for trade.

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