Friday, December 7, 2018

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 years, 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 diminished producer profit.

Wednesday, November 21, 2018

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.

Tuesday, November 13, 2018

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...

Sunday, November 11, 2018

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.