Vertical Integration in Hollywood

The WSJ reports that the US DOJ is "mulling over" repealing many rules governing how movies are distributed. The so-called Paramount decision of 1948 severely restricted movie studios' ability contract over how movies would be displayed. In the intervening 70, this decision has been the subject of much scrutiny by economists. As related in Hanssen's (2010)  examination of how vertical integration facilitated consumer benefit enhancing length-of-run decisions:

The passage of time has not been kind to the economic arguments underlying the Paramount decision. Kenney and Klein (1983) and Hanssen (2000) provide efficiency rationales for block booking. De Vany and Eckert (1991) and Orbach and Einav (2007) discuss how minimum ticket prices reduced monitoring costs. De Vany and Eckert (1991) argue that the system of runs, clearances, and zoning served to provide low-cost access to large numbers of filmgoers.

Granted, these and other studies resulting from the decision greatly enhanced our understanding of when vertical foreclosure might harm consumers. However, I am not sure this somewhat esoteric knowledge has been worth 70 years of inefficiency, lost consumer surplus and producer profit.

Meeting in the Middle

Non-strategic bargaining for pre-schoolers.

Book recommendations (add your own in the comments)

Everybody Lies
Randomistas
Algorithms to Live By
Streaming, Stealing, Sharing
Amazon, the Everything Store
Machine, Platform, Crowd
The Platform Revolution

Markets in Information

Why are millennials leaving Illinois?

Because they can:

And as millennials come to appreciate the debt load they’re expected to burden over the next two to three decades – the average Chicago household is on the hook for at least $125,000 in state and local pension debt – expect more of them to head for the border.

Unfunded pension liabilities are mounting with no relief in sight. Cities like Central Falls have used the threat of bankruptcy to fix funding before they lost all their residents.  Unfortunately for the people of Illinois, there is no provision for bankruptcy of a state.

A low priced entrant upsets Orlando airport incumbents

Eight generations of Vanderbilt MBA students have heard the story of the Orlando airport gas stations who refuse to post prices.  When travelers fill up their gas tanks before they return their rental cars they are outraged at prices that are $2/gallon higher than in the rest of Orlando.

It has taken only eight years, but entry is providing some competition to the two incumbents:

Thursday morning, Wawa opened its doors — and its gas pumps — just a block from the two gas stations closest to Orlando International Airport that charge much higher than market prices: $5.99 a gallon. Those prices leave a bad taste in the mouths of unsuspecting vacationers in a hurry to top off their rental cars before flying home.

Wawa, the convenience store with a cultlike following, will feature a bright electronic sign advertising its normal market-rate gasoline. To promote its opening, Wawa was charging $2.99 a gallon this morning, well below the Orlando average of $3.45 per gallon of regular.

That's good news to consumers stuck paying the inflated rates.

"This is ridiculous," businessman Joseph Kutka said this week after paying $70.40 to gas up his rental at Suncoast Energys before catching a flight back to Wisconsin. Like most customers at Suncoast and across the street at Sun Gas, Kutka didn't notice the price until the fuel was flowing. "They're scamming their customers. I would have stopped somewhere else if I'd known."

After years of complaints, will Wawa and the free market force prices lower? It's possible.

"At this point, we haven't made a decision," Sun Gas co-owner Larry Nieves said Wednesday. "We haven't decided what we're going to do."

I hope Mr. Nieves was using the royal "We" and not referring to potential joint decision making with his Suncoast rival.  Agreeing to not compete [by refusing to advertise] can be a violation of the antitrust laws.   

Corporate Budgeting: Paying People to Lie

Michael Jensen's timeless classic is available here.  In it he describes how stock market analysts set earnings expectations for a company's stock.  Since the CEO is paid in stock options which will decline in value if earnings fall short of analysts' expectations, the CEO wants to ensure that each division makes enough money to meet analysts' expectations.  In consultation with division managers, she turns analysts' earnings expectations into performance metrics, with each division manager's bonus tied to meeting her division's share of company earnings. 

With these incentives, each division manager has an incentive to understate (or lie about) how much her division can earn.  As a results, the negotiated division budgets need not reflect what managers actually know.   Important decisions are then made based on based on budgets constructed from lies.

Fortunately, there is an easy fix:

 [by]...changing the way organizations pay people. In particular to stop this highly counterproductive behavior we must stop using budgets or targets in the compensation formulas and promotion systems for employees and managers. This means taking all kinks, discontinuities and non-linearities out of the pay-for-performance profile of each employee and manager. Such purely linear compensation formulas provide no incentives to lie, or to withhold and distort information, or to game the system.

With a linear compensation scheme, there is no incentive to understate how much a division will earn.  And with better information, better decisions are made:

I believe that solving the problems could easily result in large productivity and value increases - sometimes as much as 50 to 100% improvements in productivity.

Managerial Econ: Top 5 problem-solving mistakes by MBA's

Managerial Econ: Top 5 problem-solving mistakes by MBA's: School has started, and I just finished grading the first assignment (group HW from the end of chapter questions). The most common comments...

Big data and the curse of dimensionality

I just finished a fabulous book, Everybody Lies, written by Seth Stephens-Davidowitz.  From the Amazon description of the book:

Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?

I particularly liked the metaphors that Stephens-Davidowitz uses to describe his results.  For example,  in describing why it is easy to come up with variables that correlate with the stock market, but hard to find ones that can make accurate predictions, he uses the metaphor of coin flipping:

Suppose your strategy for predicting the stock market is to find a lucky coin -- but one that will be found through careful testing. Here's your methodology: You label one thousand coins - 1 to 1,000. Every morning, for two years, you flip each coin, record whether it came up heads or tails, and then note whether the Standard & Poor's Index went up or down that day. You pore through all your data. And voila! You've found something. It turns out that 70.3 percent of the time when Coin 391 came up heads the S&P Index rose. The relationship is statistically significant! Highly so! You have found your lucky coin! 

Just flip Coin 391 every morning and buy stocks whenever it comes up heads. Your days of Target T-shirts and ramen noodle dinners are over. Coin 391 is your ticket to the good life!

Every statistics user should know that when running 1000 hypothesis tests, on average 50 of them will show statistically significant results, even when there is no relationship.  This is the size of Type I error (5%) in classical hypothesis testing.

Instead, split your sample in two and use half the data to "find" (estimate) one lucky coin; and the other half to test it.

BOTTOM LINE:  the more tests you run, the more likely it is that at least one of them will show a statistically significant relationship, even if there is none.  This is likely behind what has become known as the replication crisis, that has hit the field of psychology particularly hard as only one third of the results from the most cited articles could be replicated.  It is likely that academics are testing lots of hypotheses and publishing the few that turn out to be statistically significant.  This is analogous to finding a lucky coin, as it only appears to be lucky.  Once you test it outside the sample, the luck disappears.

TRUTH IN BLOGGING:  the field of economics has its own replication crisis, only two thirds of top results could be replicated.

Multilateral Bargaining (new section of Ch 16 for next edition)

The same principle of the previous section—that the party with the better outside option will receive a bigger share of the proverbial pie—can be applied to bargaining among several players. For example, two hospitals bargain to get into a payer (insurer) network; or Coke and Pepsi bargain to get onto the shelf of a retailer.

 To make this concrete, and very simple, imagine that the retailer has only five customers: one who will buy only Coke; one who will buy only Pepsi; and three switchers will buy Coke if Pepsi is not available and vice-versa. If each customer generates $1 profit, then profit is generated according to the following rules:

• No profit if no agreements are made. 
• $4 profit if the retailer sells only Coke. 
• $4 profit if the retailer sells only Pepsi. 
• $5 profit if retailer sells both Coke and Pepsi.

The three switchers put the retailer in a position to capture most of the profit.

 To see how this works, look at Figure X, where the circles denote different combinations of agreements among the three players. When no agreement is reached, no profit is generated, denoted by the bottom circle. If the retailer (“r”) and Coke (“c”) negotiate successfully (left circle), they will split $4, computed relative to disagreement values ($0) of the bottom circle, yielding a profit split of r=$2 and c=$2. Similarly, if the retailer and Pepsi (“p”), negotiate successfully (right circle), the profit is split r=$2 and p=$2. Circles below each agreement circle serve as “threat points” or “alternatives” to the agreements above.

 In the top circle, when the retailer carries both goods, the retailer uses the threat of agreement with one of the parties to extract concessions from the other. For example, when the retailer bargains with Pepsi about how much profit Pepsi should receive when the retailer sells both products, the retailer’s outside alternative is the $2 profit it would receive from agreeing with Coke.  In contrast, Pepsi’s alternative is zero. Theory predicts that the retailer should receive $2 more than Pepsi, or r=p+2, where r and p are the profits going to the retailer and Pepsi when the retailer carries both products. Similarly, when the retailer bargains with Coke, theory predicts the retailer will receive $2 more than Coke, or r=c+2.

 We know that all profit will be distributed among the three players, or that 5=r+c+p, which gives us three equations and three unknowns. The profit split that satisfies these three equations is denoted in the top circle: r=$3, c=$1, p=$1.

 So far, we have shown how the retailer uses the threat of agreement with one of the manufacturers to influence negotiations with the other. Our model tells us:

1.  what matters (the number of switching consumers, which measures the degree of substitution between the goods); 
2.  why it matters (the higher the number of switchers, the better the retailer’s bargaining alternatives); and 
3.  how much it matters (the retailer captures the lion’s share of the profit)

 But we don’t want to lose sight of the point of studying bargaining theory: we use theory not only to show us where self-interest is likely to take us, but also to show us how to do better. In the next two sections, we show how horizontal and vertical merger can improve the bargaining position and lead to a bigger share of the proverbial pie.

 Horizontal Merger: 

 Imagine that Coke and Pepsi were to merge before the bargaining begins, and then bargain jointly. No longer would the retailer be able to use the threat of agreement with Coke to influence negotiation with Pepsi, and vice-versa. Instead, the post-merger profit would be evenly split:

r=$2.50, (c+p)=$2.50

which is bigger than the manufacturers’ pre-merger profit of $2=$1+$1. Intuitively, the merger eliminates competition between the manufacturers which, if significant, may lead to a challenge from the competition agencies.

 Vertical Integration:

 Now imagine that the retailer buys Pepsi (sometimes called “vertical integration” or “vertical merger”), and then bargains with Coke. Intuitively, the acquisition of Pepsi is profitable for the retailer because it improves its outside option in negotiations with Coke (from $2 to $4). As a consequence, the merged retailer will earn $4 more than Coke (r=p+4) for a total post-merger profit split:

r =$4.50, p=$0.50.

This is profitable because the post-merger profit is bigger than the Retailer and Pepsi pre-merger profit ($4=$3+$1).

If you think of Coke as an independent brand like Calvin Klein, and Pepsi as a private label brand, like Kirkland Signature for Costco, we can see that having a captive private label put the retailer in a better negotiating position. If its private label brand is a good substitute for the independent brand, then the retailer can negotiate better deals with the independent brand because if it fails to reach agreement, it will capture much of the profit with its private label brand.

Who pays a tax? Spaniards seem confused

Very funny post over at Marginal Revolution documenting the political unrest about who should pay a tax? When their Supreme Court ruled that borrowers instead of banks should pay a mortgage tax, it resulted in street protests:

Alberto Garzón, head of the United Left coalition, went even further: “Private banks are thieves, they are the main enemy of democracy and they are responsible for gutting our economies. A majority of the Supreme Court sides with them, ratifying that justice has a price and that the system is rotten and spent,” he tweeted.

Of course, as every economics student knows, a tax drives a wedge between what a seller receives and what a buyer pays, regardless of who nominally pays the tax. In other words, the protest in Spain reflects ignorance, and I blame economists (including myself) for not being able to communicate this better.

Anyone who knows Mr Garzón, please send him this video from MR University on exactly this topic:  who pays a tax?  It is part of a great collection of short videos designed to teach principles of Microeconomics.

How Prediction Markets Work

The above graph shows the prices of contracts traded on the Iowa futures market (legally sanctioned for educational purposes).  The contracts pay out a dollar if the event occurs, so the prices can be interpreted as the probability of the underlying event.

The graph above, taken on Tuesday 2pm of Election day shows that a contract that pays out if the Democrats control the House and the Republicans control Senate is trading at about $0.70 (plotted in black), indicating a 70% chance that this will occur.

A contract that pays out if the Republicans control both House and the Senate is trading at about $0.17 (plotted in red), indicating a 17% chance that this will occur.

Prediction markets are being used by private companies, like Best Buy:

TagTrade accurately predicted the delay or on-time schedule of major initiatives including new services, IT initiatives, and store openings. Additionally, the Best Buy TagTrade market proved to be more accurate than traditional forecasts, and in some cases 5% more accurate in predicting sales forecasts, such as media sales during the quarter.

Prediction markets claim to be more accurate than other forecasting methodologies (like polls), and are best used for:

...forecasts used for New Product Introductions (NPI) and longer-term capacity requirements planning.

Google's ad auctions under attack by the European Commission

Hal Varian explains how Google's ad auctions work in this video:  Each advertiser is ranked with a quality score which is multiplied by their bid to get their bid rank.  Because it is a second-price auction, a winning advertiser has only to outbid the second highest ranked advertiser.  In other words, the higher quality score of the advertiser, the less they have to pay to win.

When Google's algorithms downgraded the quality of some European comparison shopping sites (pejoratively called "click farms"), the European Commission sued Google, claiming that it changed the algorithms to favor its own comparison shopping sites, essentially "abusing its dominant position in search."  The downgrade made it more costly for the European sites to win ad auctions. 

Google argued that the quality downgrades simply reflected the lower quality of the European comparison shopping sites because it was not possible to purchase an item on them.  Rather, the European sites just sent users to another site on which they could buy an item:

“We believe the European Commission’s online shopping decision underestimates the value of those kinds of fast and easy connections. While some comparison shopping sites naturally want Google to show them more prominently, our data show that people usually prefer links that take them directly to the products they want, not to websites where they have to repeat their searches.”

HBO Channels taken off Dish Network

The Los Angeles Times reports that HBO and DISH are at a bargaining impasse, and blames the impasse on HBO's merger with AT&T, which owns a competitor to DISH, DirecTV:

HBO, which boasts such premium programming as “Game of Thrones,” “Silicon Valley” and “Last Week Tonight With John Oliver,” has long maintained amicable relations with its distribution partners because it relies on them to help market its channels.   

But now HBO has a new corporate owner — AT&T — which also owns Dish’s biggest competitor, DirecTV. The dispute centers on how much Dish Network Corp. will pay to carry HBO and Cinemax. The blackout affects about 2.5 million of the 13 million Dish customers, including those who subscribe to Sling TV, and creates a public relations nightmare for AT&T.

In the language of Chapter 16, it is the alternatives to agreement that determine the terms of agreement.  If HBO fails to reach agreement with Dish, some Dish subscribers will switch to DirecTV, and this changes the profit caluclus of the merged firm (HBO+DirecTV).  Now that has a better outside alternative to reaching agreement with Dish, i.e., distributing HBO through its own captive distributor, it can command a better deal with Dish.

Dish is resisting giving the merged firm a bigger share of its profit pie, and is hoping that it can bring pressure to bear on HBO to accept the old smaller slice of the profit pie. 

Taylor Swift does Revenue Management

When you price to fill a venue with a fixed capacity, there are two mistakes you can make:

• Type I error: You can price too low, and have excess demand
• Type II error:  You can price too high, and have empty seats

An optimal strategy would choose a price that sets expected demand to capacity, but "shaded" high or low, depending on the relative size expected costs of over and under pricing.  In other words, if the expected costs of under pricing are bigger than the expected costs of over pricing, then price a little higher than the target price where capacity equals expected demand.

Reducing uncertainty, means that you can more accurately price to match demand to capacity (you shade less).  Some middling economists have written on how hotel mergers reduce uncertainty, and allow the merged hotel to price more accurately.  With fewer over pricing errors, occupancy goes up. 

Kalnins, Arturs and Froeb, Luke M. and Tschantz, Steven T., Mergers Increase Output When Firms Compete by Managing Revenue. Vanderbilt Law and Economics Research Paper No. 10-27. Available at SSRN: https://ssrn.com/abstract=1670278 or http://dx.doi.org/10.2139/ssrn.1670278

Now, it appears that Taylor Swift is trying to fill venues using "dynamic pricing":

If you went on Ticketmaster in January and pulled up a third-row seat for Taylor Swift‘s June 2nd show at Chicago’s Soldier Field, it would have cost you $995. But if you looked up the same seat three months later, the price would have been $595. That’s because Swift has adopted “dynamic pricing,” where concert tickets – like airline seats – shift prices constantly in adjusting to market demand. It’s a move intended to squeeze out the secondary-ticket market – but it’s also left many fans confused as they’re asked to pay hundreds of dollars more than face value. “Basically, Ticketmaster is operating as StubHub,” says one concert-business source.

The problem, of course, is that by dynamic pricing, concert goers have an incentive to "game" the dynamic pricing, by waiting until the last minute to book seats.  

Why are prices going up?

WSJ reports that prices are going up because costs are increasing and income is increasing, increasing comsumers' willingness to pay:

Businesses including Coke and big U.S. airlines have said their higher prices aren’t denting demand. 

“The economy is healthy,” Delta Air Lines Inc. Chief Executive Ed Bastian said in September. “To the extent oil prices were to continue to rise, we expect to be able to pass along the cost of that.”

Stossel on price-gouging

Platforms as Market-makers

There are two different ways of looking at markets: metaphorically and as "platforms."  Metaphorically, markets are ways of characterizing transactions between a group of buyers and sellers for a given product, in a geographic location, and during a specific time.  There is no literal marketplace, but prices and quantities behave as if there were.  

Platforms are literal markets that are under the control of a market maker, like Uber, who can set different prices to buyers (riders) and sellers (drivers).  For example, suppose that there were seven sellers with marginal costs {$1,$2,$3,$4,$5,$6,$7} and seven buyers with values={$7,$6,$5,$4,$3,$2,$1}.  Uber makes money on the spread between what it receives from riders and what it pays to drivers.  With these supply and demand curves, we show Uber’s profit calculus below.

Margin	Quantity	Profit
(7-1)=6	1	6
(6-2)=4	2	8
(5-3)=2	3	6
(4-4)=0	4	0

·       If Uber paid $1 and sold at $7, it would sell one ride for a profit of $6. 

·       If Uber paid $2 and sold at $6, it would sell two rides for a profit of $8. 

·       If Uber paid $3 and sold at $5, it would sell three rides for a profit of $6. 

·       If Uber paid $4 and sold at $4, it would sell four rides for a profit of $0. 

The profit maximizing prices are in red above.  The table also shows how the competitive equilibrium (a single price) is actually a special case of the platform with zero margin. 

This raises an interesting question, “is it ever profitable to bring an extra driver into the market?”  In this simple example, the answer is “no.”

However, the answer changes if bringing more drivers into the market increases the rider’s value for rides by reducing the waiting time.    For example, suppose that an extra unmatched driver into the market reduces waiting time by enough so that it increases the willingness to pay by every rider by $2, i.e., with values={$9,$8,$7,$6,$5,$4,$3}.  This would change the profit calculus as follows:

Margin	Drivers	Riders	Profit
(9-2)=7	2	1	7
(8-3)=5	3	2	10
(7-4)=3	4	3	9
(6-5)=1	5	4	4

·       If Uber paid $2 and sold at $9, it would sell one ride for a profit of $7. 

·       If Uber paid $3 and sold at $8, it would sell two rides for a profit of $10. 

·       If Uber paid $4 and sold at $7, it would sell three rides for a profit of $9. 

·       If Uber paid $5 and sold at $6, it would sell four rides for a profit of $4. 

With an extra unmatched driver, the margin that Uber can charge increases by $2, which is enough to offset the cost of bringing an extra driver into the market.  In fact, these kind of “network effects” work in both directions.  Just as bringing more drivers into the market reduces the waiting time for riders (increasing demand), so too does bringing more riders onto the platform make it easier for drivers to find nearby riders (increasing supply).