Entertainment and Media: Markets and Economics

1. Entertainment and Media: Markets and Economics Professor William Greene

2. Entertainment and Media: Markets and Economics Uncertainty Fall 2004 Professor W. Greene

3. Uncertainty and Information • Randomness • Do movies fail randomly? Chaos, complexity and movie stars • Modeling randomness; probability • Expected utility • Variance and the winner’s curse • Gambling

4. Unpredictability • What is randomness? • Is there “true” randomness? • What is the context? • The lack of information and randomness • Back to earth • Complexity • Chaotic systems

5. More Losers than Winners • Movie success for the studios • Numerical majority of movies ‘lose’ money • Why? • Why is film success not predictable? • What is the strategic reaction? • Box office ‘loss’ is not the whole story Production Distribution Exhibition 30-50% Costs ?

6. Producing Flops (R.Caves) • Sequence of small failures • Multiple stage production, large SUNK cost at each • Each stage is sensibly funded. Failure comes at the end of the chain. • By then, the costs are sunk. Large success Production Costs Production Costs Large failure Small failure Large failure $ $ Options

7. Growing a Ten Ton Turkey In prospect, 5 stages in sequence: 1 2 3 4 5 Release Costs: 20 20 20 20 20 E[Cost] = 100 Revenue 0 0 0 0 0 100 E[Revenue] = 100 After a disappointing first stage, E[R] falls by 10% Looking forward, costs needed to complete the project. Costs: {20} 20 20 20 20 E[Cost] = 80 {sunk} E[Revenue] = 90 Ex post: Total cost 100M, Total Revenue if expectations are met, 90. Note the crucial role of SUNK, nonrecoverable costs (i.e., the output of those costs cannot be sold on any market)

8. Art for Art’s Sake (The Alamo?) • Production function view of inputs – indifference to final product • Creative production view – “the masterpiece” (e.g., directors) • Incentives – internalized. • A problem of moral hazard: Separation of decision from costs of those decisions. (Principals and agents) • Noteworthy examples: • Bonfire of the Vanities • Heaven’s Gate • Gigli (‘Production’ cost $25M(J) + $25M(B) + $25M+ Box Office: < 4M (J Lo/Ben star vehicle. Which problem sank this film?)

9. Unpredictable Failure • Wisdom • Audiences and box office are uncertain • Stars have power to make movies succeed. [Helena (Kim Basinger) gets boxed.] • Better wisdom • Movies are “complex systems” • Complexity mixes order and chaos • Even with stars, movies are unpredictable

10. Order and Chaos • Audience behavior • Pure randomness  movies do equally well (rolls of the dice) • Information cascades  chaotic behavior and herding • Actual behavior embodies both: Complex system

11. Dynamic Systems Dynamic “systems” evolve through time t1 t2 t3 t4 t5 and so on t… • State variable (movie success, however measured) = X(t) takes a value • at each point in time. We follow it through time …. • X(t) is determined by: X(t-1) and new information Z(t) • The process must start somewhere (e.g., opening night, Z(0) = the general climate of the area, mood of the audience, events of the day).

12. Stable and Chaotic Systems • Stable systems • Not necessarily predictable, but regularly behaved • Don’t depend very much on where the process starts • Chaotic systems • Totally unpredictable • Depend crucially on where the process starts • Trivial differences in the starting point produces wild differences and oscillations in the state variables.

13. End Results of Chaos Power law distributions of rewards This is a winner take all market. DeVany and Walls: Bose-Einstein Dynamics

14. Power Law Outcomes Are Unpredictable Note: Box Office – “negative costs” – other. Only part of the accounting

15. Strategies for Dealing with the Risk of Failure in the Movies • Portfolio? Not if deVany and Walls are correct. (WHY?) • Hire a really big star? • Movie as brand name? (The Matrix, Harry Potter, …) • Profit Sharing Contracts

16. Movie Riddles (S. Ravid) • Making Movies is hugely risky, and almost all of them lose money. • Why do they keep making movies? Nobody knows. • Why do they keep paying megabucks for big stars? • Why are so few G and so many R movies made? • Why do they keep making big “event” movies (like THE ALAMO)? • Strategies for avoiding risk. Are They all Crazy or Just Risk Averse? Some Movie Puzzles and Possible Solutions,” A. Ravid, Rutgers.

17. Risk Aversion? • Movie makers are risk averse. • Studios are public corporations • Stock holders can be risk averse, corporations should not be. • An incompatibility between “agents” (movie makers) and “principals” (stockholders). Not good.

18. Quantifying Uncertainty • Probability:Likelihood of the occurrence of an event • Objective: Long run frequency • Subjective: Individual belief • “True probabilities?” • Human behavior • Always based on perceived probability • Sometimes perceptions coincide with “truth” • Consequences that depend on the law of large numbers result from objective likelihoods

19. Uncertainty and Expectation • (Obvious proposition?) Likelihood of occurrence varies directly with probability. Maps belief to a mathematical construct. • Reducing information: • The set of possible outcomes 1, 2, …, N • The set of perceived probabilities p1, p2, …, pN Expected outcome =

20. Expectation • Averaging process • Reduction of information • Basis for decision making • Averaging in everyday life: Estimation • How long will something take? • How much will some item cost? • Etc.

21. Subjective Decision Making Decision makers evaluate outcomes on a subjective basis +100 0 -100 Expectation = 0 Director input and decisions New director, debut film: Outcomes are not symmetric. Flop on debut film can derail career. More cautious. Experienced director: Just the latest project. Go ahead and incur the risk. Now, add the artistic element.

22. Star Power • Natural response by movie makers to avoid blame for failure. • Failure occurs for many reasons and no reason • Conventional wisdom – stars make a movie • De Vany: Audiences make the movie. • Current trend (somewhat) away from stars.

23. The Star’s the Thing? • DeVany and Walls, et. al. Stars do not guarantee success. • On average (not always) stars do keep movies out of the bottom.

24. Car Chases? • Why so much violence (and sex) • R rated movies have lower average box than G and PG. • Sex doesn’t sell. Violence or violence and sex do OK on average, but have LOWER VARIANCE. Risk avoidance. • S&V are rarely major flops. Low variance, so AGENTS keep their jobs.

25. Really Big Movies • Titanic, Pearl Harbor, Alamo • Big budgets  lower variance • (Big stars make big budgets)

26. Regression Results Sample is 175 movies.

27. Profit Sharing Contracts (Weinstein) • Simple Risk Sharing by Bigger Stars • Hanks/Zemekis: (Gump) Fixed % of Gross, no fixed fee. • Midler/Dreyfuss (Down and Out in Beverly Hills) All fixed fee, $600,000. Low cost • Why the participants in the “Net?” • Small bargaining strength • Last residual claimant to output from production • Least favorable position in risk chain.

28. There is No Net • Forrest Gump (1994) (Paramount Pictures) • US Box Office $330M • Foreign Box Office $350M Total, About $830M • Soundtracks, etc. $150M • Net profit -$ 62M (!) A disappearing act? • U.S. Box  50% to Exhibitors (Theaters) • Paramount Receives Approx $191M • Distribution “Fee” = 32% $ 62M • Distribution Cost $ 67M (Advt., Prints, Screening, etc.) • Advt. Overhead $ 7M (10% of Distribution Cost) • Production “Negative” Cost $112M • (Tom Hanks, Robert Zemekis, $20M (8% of GROSS, each) • Studio Overhead $15M • Interest on Negative Costs $ 6M • Net Profits from the Project -$62M • Winston Groom, Author 19% of NET = 0 • Eric Roth, Screenwriter 19% of NET = 0 • Coming to America (1988) – The Art Buchwald Case

29. Box Office Success is Only the Beginning

30. Quantifying Uncertainty • Distance of outcomes from expectation • Likelihood of distant outcomes • Variance = • Usually use square root = standard deviation = 

31. The Winner’s Curse • Bidding situation (sealed bid auctions) • Publishing (Jack Welch’s book) • Offshore oil leases • Broadcast frequencies • Baseball, football, basketball, hockey players • Art (The masterpiece effect) • General Result: High bidder bids over the value of the property – Winner’s regret.

32. Understanding The Winner’s Curse Assumptions • Property has a true value:  • Bidders combine private and public information to form an estimate of  • N bids submitted, B1,…,BN • Bidders do not collude • Bids are randomly distributed around the true value • Bids are unbiased – on average right, but some higher than  and some lower • Maximum bid wins the auction

33. Probable Examples of the Winner’s Curse • Bill Clinton: Between Hope and History: 70% returned • Johnnie Cochrane: Journey to Justice: $3.5M advance, 350,000 of 650,000 unsold • Whoopie Goldberg: $6M advance, total failure • (?) Jack Welch $6M. Hillary Clinton, $8M • Why? • Trade publishers integrated into large publishing firms; organizational complexity and separation of decisions from ultimate consequences (corporate levels) • Incentives of publishers. Signalling value of advances to celebrity authors and large first printings. • Market characteristic – winner take all markets, most entrants fail, with or without a celebrity author.

34. Winner’s Regret • Under the assumptions, the maximum bid is almost guaranteed to be too high • Expected value of [Max(B1,…,BN) - ] depends on • Number of bids • Standard deviation • Distribution of bids (normal, something else?) • Regret = this difference • E[Regret] = f(N, ), increases in both N and 

35. Strategy for Avoiding the Curse • Learn N, f(.),  through experience and research • Scale back bids • Collude: Professional sports • Does it work? What else is needed? Assumptions about how other players behave.