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Entertainment and Media: Markets and EconomicsPowerPoint Presentation

Entertainment and Media: Markets and Economics

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Entertainment and Media: Markets and Economics. Professor William Greene. Entertainment and Media: Markets and Economics. Uncertainty Fall 2004 Professor W. Greene. Uncertainty and Information. Randomness Do movies fail randomly? Chaos, complexity and movie stars

Entertainment and Media: Markets and Economics

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Entertainment and Media: Markets and Economics

Professor William Greene

Entertainment and Media: Markets and Economics

Uncertainty

Fall 2004

Professor W. Greene

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

- What is randomness?
- Is there “true” randomness?
- What is the context?

- The lack of information and randomness
- Back to earth
- Complexity
- Chaotic systems

- 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

?

- 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

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)

- 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?)

- 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

- Audience behavior
- Pure randomness movies do equally well (rolls of the dice)
- Information cascades chaotic behavior and herding

- Actual behavior embodies both: Complex system

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).

- 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.

Power law

distributions

of rewards

This is a

winner take

all market.

DeVany and Walls: Bose-Einstein Dynamics

Note: Box Office – “negative costs” – other. Only part of the accounting

- 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

- 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.

- 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.

- 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

- (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 =

- 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.

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.

- 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.

- DeVany and Walls, et. al. Stars do not guarantee success.
- On average (not always) stars do keep movies out of the bottom.

- 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.

- Titanic, Pearl Harbor, Alamo
- Big budgets lower variance
- (Big stars make big budgets)

Sample is 175 movies.

- 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.

- 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

Box Office Success is Only the Beginning

- Distance of outcomes from expectation
- Likelihood of distant outcomes
- Variance =
- Usually use square root = standard deviation =

- 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.

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

- 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.

- 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

- 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.