Category: Teaching

Human Organs, Behavioral Economics, and Insurance Mandates

Like the minimum wage and rent control, the market for human organs is a classic topic when teaching the basics of supply and demand. Organ markets are largely outlawed and, as a result, the demand for organs greatly outstrips the supply. For example, according to some estimates, as many as 4,000 people in the United States die each year while waiting for donor kidneys (some of which could, in principle, come from healthy donors).

As Dick Thaler notes in the New York Times today, the usual economist solution to this problem – allowing the buying and selling of human organs – is a political non-starter. Many people find the idea “repugnant,” as economist Alvin Roth has put it.

One solution, which Roth helped pioneer, is to create organ swaps rather than sales. Suppose, for example, that my wife needs a kidney and that I am willing to donate, but am not a match. And at the same time, a woman wants to donate a kidney to her sick brother, but also isn’t a match. That seems like a dead end (so to speak), but if I am a match for her brother, and she is a match for my wife, then we can arrange a swap – my kidney for hers. Two lives get saved, and there’s nothing repugnant about it.

Over time, this basic idea has expanded to include “daisy chains” of donations involving numerous donors and recipients (for a nice description see this recent article in Wall Street Journal).

Thaler considers another way to address the problem of organ supply (from individuals who become brain dead, not those who are healthy)  using the insights of behavioral economics:

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Netflix Boosts Prize Economics

By at least one metric – the number of people who have mentioned it to me – my brief post about Netflix appears to be my most popular one so far.

The post linked to a remarkable slide deck about the corporate culture that Netflix has embraced in its quest for excellence. Most memorable line: “adequate performance gets a generous severance package.” If you haven’t seen it, I encourage you to click on over. It’s worth your time.

Yesterday’s award of the first Netflix prize highlights another strength of Netflix’s culture: it clearly does not suffer from “not-invented-here” syndrome. Indeed, quite the reverse. A few years ago, Netflix realized that it had reached its limit in trying to improve the accuracy of its movie recommendation system. Even though users may rate dozens (or more) movies, it turns out to be difficult to predict what other movies they will like.

So Netflix decided to outsource this problem in an ingenious way: it offered a $1 million prize to any person or team that could improve the recommendation algorithm by at least 10%. Stated that way, the problem seems deceptively easy. But it took nearly three years before the winner – a team led by AT&T Research engineers – took home the prize.

As recounted in Netflix’s press release, this marathon ended in a race to the wire:

“We had a bona fide race right to the very end,” said [CEO Reed] Hastings. “Teams that had previously battled it out independently joined forces to surpass the 10 percent barrier. New submissions arrived fast and furious in the closing hours and the competition had more twists and turns than ‘The Crying Game,’ ‘The Usual Suspects’ and all the ‘Bourne’ movies wrapped into one.”

…

Netflix said “BellKor’s Pragmatic Chaos” edged out a team called “The Ensemble,” another collaboration of former competitors, with the winning submission coming just 24 minutes before the conclusion of the nearly three-year-long contest. The competition was so close and the submissions so sophisticated that it took a team of external and internal judges several weeks to validate the winner after the contest closed on July 26.

Happily, the resulting algorithm won’t be exclusive to Netflix:

The contest’s rules require the winning team to publish its methods so that businesses in many fields can benefit from the work done. The winning submission and the previously hidden ratings used to score the contest will be published at the University of California Irvine Machine Learning Repository. The team licensed its work to Netflix and is free to license it to other companies.

On the first day of my microeconomics class, I told my students that economics is all about incentives. As an example, I used the famous prize for a way to measure longitude, which inspired the invention of the chronometer (i.e., a clock of sufficient precision to measure longitude). Next time around, I will mention the Netflix prize as well.

P.S. Not one to rest on its successes, Netflix has already announced plans for a second Netflix prize. This one aims to find a better way to recommend movies to people based on demographic data (e.g., where they live) rather than movie ratings.

Positive, Normative, and … ?

Am I the only one who feels unfulfilled by the standard distinction between positive and normative economics?

I am gearing up to return to the classroom next week, to teach microeconomics to incoming masters students at the Georgetown Public Policy Institute. Anyone who’s experienced the first day of micro class knows what’s coming. After introducing myself and talking about the wonders of economics (which is, indeed, fun, useful, and enlightening), I will launch into the great positive vs. normative distinction.

In brief:

  • Positive is the science side of economics: understanding and predicting the behavior of individuals, firms, markets, economies, etc. In short, the part of economics in which we try to be physicists (or, sometimes, biologists).
  • Normative is the side of economics where we make value judgments, identifying policies as good or bad. In short, the part of economics in which we try to be philosopher-kings.

Both styles of economics are important, particularly in a public policy program. And drawing a careful distinction is vital, not least because of the many people in Washington (both economists and non-economists) who try to dress up their value judgments as science.

I have one problem with this distinction, however: it overlooks a great deal of what economists actually do.

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