Economics Of The Movie Business Essay

In this section I stand a look backward of the movie business with an emphasis on how contrivance play evolved from the Golden Age of Hollywood in the 1930s and 1940s until its demise in the beginning of 1986. For many decades harbour door press step upding was non a concern for field of operations owners, be political campaign it was non the dominant method by which asks were licensed. During the Golden Age, block booking was the steering a majority of blasts were licensed. With this method, high and low quality films were sold together in a mess to business firm owners, with turn up an opportunity to administer screen them.The landmark United States vs. Paramount et al. purpose by the peremptory Court in 1948 altered the motion picture distri stillion system. The five major movie companies that produced, distri preciselyed, and operated line of businesss as intimately as the leash studios which did not own domains were every found in violation of the Sherman motion for attempting to monopolize the industry. One of the major consequences of this decision was the elimination of block booking. later the Paramount decision, films were licensed by product splitting, open evokeding, or screenland pop the questionding.Product splitting was when theater owners decided among themselves which unitary had the first of either opportunity to negotiate for a film with a movie studio in a assumption market. Open bidding referred to a situation in which theater owners had the opportunity to trade screen films in the lead bidding. trick bidding was used infrequently until the 1960s, which prompted a two-year agreement from January 1, 1969 to January 1, 1971 between the movie companies and the part of Justice. This agreement limited 1 9 the number of films which could be unreasoning bid to three per studio per year.The two-year agreement was re radicaled twice, which limited the practice through January 1, 1975. However, the Department of Justice revoked all trimions bound cheat bidding after this date and the practice accelerated rapidly. Movie companies perceived ruse bidding as a necessary way to finance blockbuster films, and it persisted for an eleven year period from 1975-1985. Chapter 2 LITERATURE REVIEW In this chapter, I will review the economic literature on blind bidding, exit, and inhering experiments. The selected papers motivate my empirical model of the actions of blind bidding. Section 2. 1 discusses the blind bidding literature.Section 2. 2 surveys ingrained experiments testing the force of a policy change. 2. 1 Blind call upding In this section, I discuss two studies which arrive at diametrical conclusions slightly the dissemble of the anti-blind bidding jurisprudences. Although neither study addresses explicitly the issues of exit, portal values, and delays, the empirical markings atomic number 18 relevent. Blumenthal (1998) arises that fair bids ar trim back for blind bid th eater owners and as a result their returns argon high. However, since the returns of blind bid theater owners are more volatile, she reason outs risk averse theater owners are worseoff under blind bidding, legitimizing their efforts to pass anti-blind bidding polices. Forsythe, Isaac, and Palfrey (1989) model the behavior of n buyers and one vendor in a sealed-bid, first- legal injury auction. They conclude that the anti-blind bidding jurisprudences were unnecessary as buyers would learn that a marketer withholds breeding when it is unfavorable. A marketer would abjure blind bidding once all buyers learn that withholding information was in the sellers best interest and not theirs. I find that practices in the motion picture industry were not consistent with this prediction,because the movie companies trade screened unfavorable films and blind bid highly anticipated films. Blumenthal (1988) justifies theater owners principle to seek relief from blind bidding by showing tha t they experience lower utility in blind-bid environments than preview ones. The author uses generalized least squares to test three hypotheses about film bids or film returns for blind-bid and trade screen theaters using the rental terms of 18 films from a national theater twine in 1982. First, she hypothesizes that theater owners in blind-bid states submit lower bids, because in accordance witheconomic theory, bidders reduce their bids on average in an auction where there is uncertainty about the value of a product. Second, blind-bid theater owners send out a great emphasis on the limited information contained in a bid letter. Therefore, bid letter information will explain a larger per centimeage of the variance for bids in blind-bid theaters than trade screen ones. Third, mean returns are higher for blind-bid theaters, but they experience greater volatility than trade screen theaters. Depending on the hypothesis in question, the dependent variable is either film bids or film returns.1 She includes film budget and saturation as predictor variables, since higher budgeted films and wider released films would be an indication of larger expect returns by the movie companies. Other independent variables include theater operating expenses, an indicator variable signifying theaters in blind bid states, and the number of movie theaters located within the metropolitan area. The Film returns are the boxwood office revenue less the price paid for the film. blind bidding dummy variable was interacted with film budget and saturation to test the mo hypothesis. The author finds theater owners submit lower average bids in blindbidding states than in trade screen ones. With regards to the second hypothesis, blindbid theater owners place a greater emphasis on bid letter information for every million dollar growth in film cost, blind bid theater owners bid an additional $8,900 while trade screen ones bid an additional $5,100. Regarding the final hypothesis, Blumenthal mo dels utility as a function of the mean and variance of film returns which measures the breaker point of risk aversion among theater owners. In terms of utility, risk averse theater owners are worse off, because higher revenues are accompanied by greater volatility.Theater owners are unable to reduce their bids plenty to offset the extra volatility because of competitive forces. Using a laboratory experiment in some(prenominal) markets, Forsythe, Isaac, and Palfrey (1989) deliberate the anti-blind bidding righteousnesss unnecessary. They find an residuum where buyers learn to assume the worst about a sellers decision to blind bid percentage points causing most items to no longer be blind bid. The game has a adept seller versus n buyers, and the former must(prenominal) decide whether to reveal information about the item to all buyers. A seller reveals his information to buyers if the news is favorable, and does not if it is unfavorable.A seller obtains the highest bid if he r eveals his information. The auctioned item has both a putting green value and private value component. After a seller decides whether to reveal their information, the item is auctioned in a sealed bid first price auction. Several possible Nash equilibria are considered in the game, but the authors focus on the ? assume the worst? solution, because all other outcomes cannot be obtained so long as the auction stick withs a sequential equilibrium. This type of equilibrium occurs when buyers make conjectures about a sellers motives when they hook up with a strategy which is consistent with the sellers best interest.To obtain an ?assume the worst? solution, a seller continues to blind bid items as long as there is at least one unsophisticated buyer a buyer who bids the average of all quality levels, rather than assumes the worst about no revealed information. With the passage of time, buyers learn that when a seller withholds information it is not in their interest, forcing sellers to reveal information for lower quality levels. Eventually, the market reaches a point where no items are blind bid. In five of the six blind-bid auctions, the average winning bid declines over time. Although blind bidding is not eliminated bythe conclusion of the auctions, it is practiced less frequently and buyers dramatically lower their expectations for the value for the auctioned item. The authors conclude the anti-blind bidding laws are unnecessary, because with the passage of time, blind bidding would birth been phased out completely. These two studies offer two important insights. Although Blumenthal (1988) concludes theater owners are worse off under blind bidding, she does not consider that theater owners can diversify the risk of films by converting to the multiplex theater. In this manner, theater owners can pool the risk of mediocre andblockbuster films rather than run the risk of exhibiting a single inferior film. Regarding Forsythe, Isaac and Palfrey (1989), if the mo vie companies did not reveal their information for blockbuster films, they were not obtaining the highest auction price. Since the movie companies must have acted in their own self-interest, I assume blind bidding provided some cost pull ins which outweighed the decision to trade screen films. 2. 2 innate(p) Experiments In this section, I discuss three natural experiments which provide a reference for testing the effects of the anti-blind bidding laws on exit, price of assenting prices, and delays.Natural experiments are lots used to pick up the effect of a policy change. A researcher examines two groups which have homogeneous characteristics, one of which is exposed to a policy change while the other is not, and observes how the outcome differs between the two. Natural experiments are called quasi experiments, because the researcher has little or no crack over the observed situation, which is in contrast to social experiments where researchers implement proper experimental design. Card and Krueger (1994), Milyo and Wardfogel (1999), and Bergen, Levy, Rubin and Zeliger (2004), conduct natural experiments assuming an exogenous changein a law. All three natural experiments assume the discourse effect is not correlated with the outcome variable and any uncontrolled independent variables correlated with it. Card and Krueger (1994) investigate the effect on employment of a 50 cent raise in the New Jersey marginal wage in the fast food industry. Milyo and Wardfogel (1999) examine the impact on prices of advertised and non-advertised items after a ban on liquor advertising is lifted in Rhode Island. The ban permitted retailers to charge higher prices which was considered specially helpful to small ? mom and popretailers that could not offer the price discounts of larger chains. Bergen et tal. (2004) investigate the electronic network effects of item pricing laws for supermarkets which require that retailers label every item individually with a price tag to help ensure that consumers are not overcharged at the register. The three empirical studies conduct natural experiments in similar geographic regions. Card and Krueger (1994) match the neighboring states of New Jersey and dad. The authors use descriptive statistics from their data to argue that wages, prices, and employment measures are similar.For example, the mean starting wage for New Jersey and pascal is $4. 61 and $4. 63, respectively, forwards New Jerseys adjoin in the minimum wage. Bergen et tal. (2004) target a narrow tri-state region of Clifton, New Jersey, Tarrytown, New York, and Greenwich, Connecticut to study the impact of item pricing laws. come together geographic proximity is one factor for the selected towns as the greatest distance that separates the towns is only approximately 50 miles. In addition, these towns have similar population size, population densities, and access to quality public schools.Milyo and Wardforgel (1999) follow a similar strategy to Bergen et tal. (2004) by comparing adjacent states but narrowing their focus to three areas gray Rhode Island, Northwest Boston suburbs, and the Rhode Island and Massachusetts border. In addition, the three studies utilize multiple control groups which provide the benefit of observing how sensitive the results are to different controls. Card and Krueger (1994) compare full-time-equivalent employment (FTE) for New Jersey and public address system, but also compare FTE in New Jersey fast food stores which already paidat least the new minimum wage to those in New Jersey that paid under the new minimum. Milyo and Wardforgel (1999) compare retail prices in Rhode Island with those from Massachusetts, but also use Rhode Island wholesale prices as a second control. Bergen et tal. (2004) compare prices in New Jersey with two controls New York and Connecticut both of which have item pricing laws. However, Connecticut exempted stores from the law which installed the electronic shelf label s ystem because it ensured that the price at the shelf was the same as the price at the register.Therefore, the authors used Connecticut stores to observe how prices differed among non item pricing law stores and those which used the electronic shelf system. I adopt the idea of multiple control groups when I examine the exit of theater owners. The Card and Krueger (1994) study has additional significance to my study because they use the variety-in-differences estimator, and I adopt this method for the analysis of admission prices. The primary benefit of this method is that the researcher is able to cancel out other industry factors which are common to the treatment and control group through second differencing.Therefore, the difference-in-differences measures the impact on the outcome solely from the policy change. These empirical studies provided some important insights on how to conduct my natural experiment on the anti-blind bidding laws. When selecting treatment and control group s, it is important to select homogenous regions so that there is a believable rationale that the control group will behave like the treatment group. expend of multiple control groups is encouraged in natural experiments to test the robustness of the results.In addition, I follow the method of Card and Krueger (1994) and use the difference-in-differences estimator to examine admission prices. Chapter 3 ADMISSION PRICES In this paper, I investigate the claims made by theater owners and movie companies about the impact of the anti-blind bidding laws on admission prices. I examine the impact of the strictest laws of Ohio and Pennsylvania, which eliminated blind bidding and placed severe restrictions on guarantees. I selected these states, because they pose the strongest case for the laws having an impact according to theater owners claims.I compare average admission prices in these states forward and after the passage of the law with prices in two states that never had such a law. F or Ohio, I compare average prices in Cleveland with those in Detroit. For Pennsylvania, I compare average prices from Philadelphia and Pittsburgh with those of Detroit. 1 Using the difference-in-differences estimator, I find some evidence that the laws raised admission prices. Theater owners argued that admission prices were higher under blind bidding, because they had to increase their prices to cover losses incurred from inferior films and to compensate for the guarantees they paid.According to theater owners, the anti-blind bidding laws would eliminate the burden of blind bidding, and in some states also guarantees, so that lower prices would follow. Movie companies claimed initially considered comparing average Philadelphia and Pittsburgh prices with those in Manhattan. I decided against using New York City as a control because prices were consistently higher there than in any other market because of the high cost of living in the area. The laws would have the opposite effect fo r two reasons. Theater owners would identify blockbuster films after viewing the preview, and a bidding war would ensue.Since film rentals were bid higher, this cost would be passed along to moviegoers. In addition, movie companies claimed that the anti-blind bidding laws would cause delays in the release of films, and this cost would be passed on to consumers. 3. 1 Model I consider the claims of theater owners and movie companies about admission prices to be invalid because of what is universally accepted in economics about the fill for factor inputs. The demand for a factor input (e. g. labor or capital) is a derived demand in that demand for the factor and its price is contingent upon the demand for the final product.For example, the demand for movie stars depends not only on their current salaries, but also the total tickets sold. Movie stars would be unable to command high salaries if there is not an overwhelming demand for motion pictures. Therefore, prices charged at movie theaters, an input, are determined by demand. On the other hand, admission prices are likely to differ across cities collect to costs outside the control of the industry. For example, theater owners in New York City had higher rent or mortgage payments than those in Atlanta, Georgia because of the relatively high cost of land.Another factor that varied regionally was the price of labor. Theater owners facing higher minimum wages had greater variable costs than those in states with lower minimums. I expect the anti-blind bidding laws to influence admission prices if they impacted marginal costs, or if they restrict the supply of films. Although the laws did not affect theater owners marginal costs, they may have impacted the movie companies. spare expenses were incurred because gross sales prints had to be specially made for the purposes of trade screening. This cost was not present in blind bidding states. 3. 2 Data and MethodsI obtained the data from Variety, which reported thea ters from 15 cities on a periodical basis. Variety sampled most cities once a month with about 10 to 20 theaters per sample. The same theaters were principally sampled, but over longer periods of time, the sample changed as some exited the marketplace. I sampled each city quarterly. On occasion, Variety reported theaters which charged one dollar for admission. These observations were dropped from the data set, since they were second-run movie houses. display board 5. 1 shows the descriptive statistics for the data. Any city sampled was a representation of the metropolitan area.Therefore, the sample contained some downtown theaters as well as many suburban theaters. For example, Detroit included downtown theaters such as the Adams, Fox, and Renaissance, and theaters such as the Dearborn, Americana West, and Macomb mall from surrounding areas of Wayne, Oakland, and Macomb counties. During the first year that the ant-blind bidding laws were in effect, it is not clear which films we re blind bid. This is because theater owners bid on films six months to one year in advance of the release date. For example, Ohio enacted the law in October 1978, but theater owners may have been bidding for films to be released in___________________________________________________________________________ 2 According to Barry Reardon, distributional chairman at Warner Brothers, the additional expense to trade screen amounted to approximately $50,000 per film in Jim Robbins, ? Distribs Adapt to AntiBlind Bid Laws? , Variety, July 3, 1985, 80. 3 A sales print is a reel of film with the movie preview. April 1979 or as far away as October 1979. The Pennsylvania law became effective in May 1980. At that date, theater owners would bid on films for November 1980 up to May 1981. I address the lagged effect of an anti-blind bidding law on films byexamining average admission prices using two different treatment and control groups 1) two days before and after a law, and 2) three years befor e and after a law. Table 3. 1 provides the descriptive statistics for these variables. For the Ohio law, I calculate average prices in 1976 and 1977 (pre-treatment group) and average prices in 1979 and 1980 (post-treatment group). This measures the immediate effect of the law even though some of the admission prices in 1979 will be for films which were not trade screened. For three years before and after the law, I use average prices in 1975 and1976 compared with those in 1980 and 1981. In this case, all films in the posttreatment group were trade screened. For the Pennsylvania law, I use the same procedure for selecting the pre and post-treatment groups. I consider the passage of the Ohio and Pennsylvania laws a natural experiment, and I proceed to measure the impact of a law by using the difference-indifferences estimator defined as the change in the population means from the treatment group less the change in population means from the control group. This method has an advantage o ver comparing the means of the treatment and control group after thelaws because the latter assumes the treatment and control groups are identical in every way except for the law. The difference-in-differences estimator makes the weaker assumption that regardless of the overall factors impact admission prices, they affected the treatment and control groups in the same way. In order to understand the moment of the difference-in-differences estimator, consider the interpretation of first differences between the treatment and control. The change in price in the control group informs us how prices would have behaved in the treatment group if the law wasnot implemented. The change in price in the treatment group tells us how the average price behaved given the enactment of the law. By taking second differences, I obtain the difference-in-differences estimator which measures the effect of the law by taking the difference in what happened with average prices compared with what would have happened to them. 3. 3 Cleveland and Detroit Figure 5. 1 displays average admission prices for Cleveland and Detroit from 1975-1981. Detroits average prices remain consistently above Clevelands by approximately 59 cents throughout the observed period.I examine average admission prices over time to see if the assumption that overall factors that affect them are the same for both treatment and control groups. Unobserved factors are more likely to be different if the trend in prices diverges before the treatment effect. Average admission prices for Cleveland and Detroit remain relatively steady before the implementation of the law implying the assumption of a common trend appears valid. The results for the difference-in-differences estimator are shown in Table 3. 2. Comparing average prices two years before and after the law, I find Detroitsprices increase by seven cents and Clevelands rise by 16 cents. The seven cent increase in average prices represents how Cleveland prices would ha ve behaved in the absence of the anti-blind bidding law. After taking second differences, I find that the Ohio law significantly increases Clevelands average prices by nine cents. Examining admission prices three years before and after the law does not produce the same conclusion. Clevelands and Detroits average prices increase by 20 and 21 cents, respectively. The difference-in-differences estimator shows that Clevelands average prices are significantly lower by one cent.3. 4 Philadelphia, Pittsburgh and Detroit Figure 5. 2 shows average prices in Philadelphia and Pittsburgh versus those in Detroit from 1977-1983. For the first two years, prices are tight identical. In 1979 and 1980, the difference in average prices remains relatively steady at 10 and 15 cents, respectively. Beyond 1980, the difference in average prices increases, ranging from 36 to 41 cents. The assumption that factors have a common trend appears satisfied because the difference in average prices maintains itself in 1979 and 1980. The first and second differences for average admission prices are shown in Table 5. 3.Comparing average prices two years before and after the Pennsylvania law, I find Philadelphias and Pittsburghs average prices rise by 43 cents while Detroits increases by 11 cents. Detroits prices are assumed to be behaving like Philadelphias and Pittsburghs if Pennsylvania had never passed an anti-blind bidding law. The difference-in-differences estimator shows that the law results in a statistically significant 32 cent increase in admission prices. Comparing three years before and after the law produces a similar result, the law causes higher average admission prices for Philadelphia and Pittsburgh by 53 cents.3. 5 Conclusion I examine the impact of the Ohio and Pennsylvania anti-blind bidding laws on admission prices and I find higher admission prices in Cleveland, Philadelphia, and Pittsburgh in three of the four difference-in-differences estimators. The impact of the Pennsyl vania law is more robust than the Ohio law because in one case, average admission prices decline by one cent. A potential story for higher average admission prices is that the movie companies marginal costs increased in anti-blind bidding states, because sales prints had to be produced exclusively for trade screening films.