Andrew Leigh

My Wryside economics segment on Life Matters this morning discussed Eli Berman’s work on the economics of terrorism. If you’d like to listen to it, you can find it here.

Today, an anonymous guest post (from someone with an ANU connection) ponders airline pricing.

A recent article in the SMH reports that from February 1, Air France and KLM will begin charging obese passengers 75% of the cost of a second seat if they cannot fit into one seat. This story re-ignited an interesting question I’ve had regarding airline tickets. I have always been perplexed by the issue of pricing after a trip to the Middle East.

After spending far too much money on carpets in the souks, I arrived at the check-in counter at the airport to be told that my ticket only allowed 25kg of baggage and that my recent acquisitions had sent me 5kg over. If I wanted to take them with me, I had to pay the surcharge. The exact amount escapes me, but I was told it was to cover the cost of transporting the extra weight.

What puzzled me about the explanation was that standing at the next check-in counter was a sizable chap (possibly around 110kg) with 25kg of luggage. He wasn’t charged excess since his luggage was on the allowable limit, but if the price of a ticket represents the cost of transporting weight; your weight, surely his net impact on the overall weight of the plane is far greater? (I’m an average male, 175cm tall and weighing around 78kg). With my luggage, my total weight on the plane was 108kg, much less than the 145kg of my fellow passenger.

Simple flight dynamics says that the heavier the plane, the more fuel it will use to fly. Presumably, most other costs of operating an aircraft are fixed (salaries for crew, catering, landing and docking levies, lease payments on the aircraft), which leaves fuel. If the largest variable cost of running a plane is determined by the fuel used to transport the overall weight (plane, passengers and luggage) of the aircraft at takeoff, surely the current pricing mechanism for airline tickets is economically inefficient?

Wouldn’t it be far more efficient to charge people based on their total weight impact on the plane (i.e. body weight plus baggage)? That way, slim travellers with little luggage do not subsidise heavier people with large amount of baggage.

Any theories as to whether the suggestion of our anonymous poster could – well – fly?

(xposted @ Core Economics)

New Yorker writer John Cassidy has posted on his website a 1996 interview with the late Paul Samuelson. My favourite snippet concerns the use of mathematics in economics.

I asked Samuelson whether mathematics was now too important in economics.

Rather than answering the question directly, he talked about a lecture he attended in the nineteen-thirties by Lionel Robbins, a well-known professor at the London School of Economics. “Lionel Robbins gave an address saying this math stuff is just a passing fad. I was all of twenty-eight, but I thought, ‘Poor fellow, he just doesn’t realize that he’s missing the train.’ That was just a bad understanding of the dynamics of the profession. Math is a problem for everybody in the profession and it has been for years. We all say, math should be used just up to the point that I have used it, and no more…I always say to our graduate students when they are leaving: ‘As a graduate student at a top-notch university, you tend to lose touch with reality. You have been engaged in puzzle solving and learning a new language. When you emerge, you may tend to think you have been asleep for several years.’ The paradox is that the best people in practical terms are the Jim Tobins, the Bob Solows—the guys who are awfully good at the technical stuff as well.” Samuelson also brought up his colleague Modigliani, whose parking space he may have been occupying, noting “he has done more for Italy than pizza,” and the prevalence of technically adept M.I.T. graduates in the Clinton administration. (They included Lawrence Summers, Joseph Stiglitz, and Laura Tyson.)

“Like herpes, math is here to stay,” he said. “It takes strong math to defeat misleading math. For example, ordinary least squares”—a standard statistical method—“is misleading. It takes more mathematics than ordinary least squares to understand three-stage least squares, co-integration, or unit roots, all of which are improvements on ordinary least squares. But it does lead to a communication problem. The number of people who can communicate effectively, like Paul Krugman, is very small.

As anyone who has read SuperFreakonomics would’ve seen, Steven Levitt (along with and John List) appears to be on a quest to reach into the chest of laboratory experiments and rip out its beating heart. Their latest two papers are below (gated links, sorry).

What Happens in the Field Stays in the Field: Exploring Whether Professionals Play Minimax in Laboratory Experiments 
Steven Levitt, John List and David Reiley
The minimax argument represents game theory in its most elegant form: simple but with stark predictions. Although some of these predictions have been met with reasonable success in the field, experimental data have generally not provided results close to the theoretical predictions. In a striking study, Palacios-Huerta and Volij (2007) present evidence that potentially resolves this puzzle: both amateur and professional soccer players play nearly exact minimax strategies in laboratory experiments. In this paper, we establish important bounds on these results by examining the behavior of four distinct subject pools: college students, bridge professionals, world-class poker players, who have vast experience with high-stakes randomization in card games, and American professional soccer players. In contrast to Palacios-Huerta and Volijs results, we find little evidence that real-world experience transfers to the lab in these games–indeed, similar to previous experimental results, all four subject pools provide choices that are generally not close to minimax predictions. We use two additional pieces of evidence to explore why professionals do not perform well in the lab: (1) complementary experimental treatments that pit professionals against preprogrammed computers, and (2) post-experiment questionnaires. The most likely explanation is that these professionals are unable to transfer their skills at randomization from the familiar context of the field to the unfamiliar context of the lab.

Checkmate: Exploring Backward Induction Among Chess Players
Steven Levitt, John List and Sally Sadoff
Although backward induction is a cornerstone of game theory, most laboratory experiments have found that agents are not able to successfully backward induct. Much of this evidence, however, is generated using the Centipede game, which is ill-suited for testing the theory. In this study, we analyze the play of world class chess players both in the centipede game and in another class of games – Race to 100 games – that are pure tests of backward induction. We find that world class chess players behave like student subjects in the centipede game, virtually never playing the backward induction equilibrium In the race to 100 games, in contrast, we find that many chess players properly backward induct. Consistent with our claim that the Centipede game is not a useful test of backward induction, we find no systematic within-subject relationship between choices in the centipede game and performance in pure backward induction games.

This follows on from List’s famous 2007 JPE paper (On the Interpretation of Giving in Dictator Games), which found that a simple modification to the dictator game (allowing the player to take money as well as share it) drastically altered the results. As List stated in the conclusion to that paper:

A recent surge of research in economics uses the laboratory as a tool to measure preferences. One stylized fact from this literature is that a majority of agents in standard dictator games pass a portion of their funds to an anonymous agent, and the amount is nontrivial—roughly 20 percent of the endowment. Utility theories that invoke social preferences have been forwarded to explain such data patterns. One puzzling feature of everyday life, however, is that even though scores of students around the world have outwardly exhibited their preferences for equality in laboratory experiments by sending anonymous cash gifts to anonymous souls (in some cases not even knowing that such a soul actually exists), why is it rare to find such data patterns in the extra-lab world?

My own take on this literature is that for experimental economists, there is probably more to be learned from economics games that don’t use computers. If you want to learn about teams, put people around a table. If you want to learn about trust, let them speak to their partner. If you want to learn about giving, put the cash in their hands first. The less that the game resembles real life, the less we are likely to be able to learn from it.