Elegant theories and headless clowns -- more bad tech reporting from the New York Times

The previously mentioned Paul Krugman piece on opera singer Jenny Lind  included a link to this NYT article "adapted from The Price of Everything: Solving the Mystery of Why We Pay What We Do, by Eduardo Porter, an editorial writer for The New York Times." Krugman was criticizing the reliance on simple economic stories that don't fit the facts. Porter was telling one.

Baseball aficionados might conclude that all of this points to some pernicious new trend in the market for top players. But this is not specific to baseball, or even to sport. Consider the market for pop music. In 1982, the top 1 percent of pop stars, in terms of pay, raked in 26 percent of concert ticket revenue. In 2003, that top percentage of stars — names like Justin Timberlake, Christina Aguilera or 50 Cent — was taking 56 percent of the concert pie.
...

But broader forces are also at play. Nearly 30 years ago, Sherwin Rosen, an economist from the University of Chicago, proposed an elegant theory to explain the general pattern. In an article entitled “The Economics of Superstars,” he argued that technological changes would allow the best performers in a given field to serve a bigger market and thus reap a greater share of its revenue. But this would also reduce the spoils available to the less gifted in the business.

The reasoning fits smoothly into the income dynamics of the music industry, which has been shaken by many technological disruptions since the 1980s. First, MTV put music on television. Then Napster took it to the Internet. Apple allowed fans to buy single songs and take them with them. Each of these breakthroughs allowed the very top acts to reach a larger fan base, and thus command a larger audience and a bigger share of concert revenue.

Putting aside the fact that, as Krugman pointed out, we have examples of superstar musicians that predate both recording and broadcasting, this paragraph is still stunningly incomplete and comically ill-informed.

The 1980s cutoff is arbitrary and misleading. The 1880's would make more sense, though it really wasn't until the 1890s that things really took off and it has been a fairly steady stream of technological innovations since then.

Here's a brief, roughly chronological view of some of the highlights:

Disc records

Amplification

Radio

Optical sound tracks on film

Stereo

FM

LPs

HiFi

Television (which brought with it everything from Hit Parade to American Bandstand, Sullivan, Midnight Special and countless shows like this)

Cassettes

CDs

Recordable  CDs

Affordable digital audio editing

And then, of course, a whole family of internet-based innovations.

The past 125 years has been one long stream of "technological disruptions" for the music industry, but most of the innovation over the past couple of decades has mainly broadened the market by increasing selection and lowering production costs. In terms of "allow[ing] the very top acts to reach a larger fan base, and thus command a larger audience and a bigger share of concert revenue," at least for the North American and European audience, the top acts have been near saturation since the Sixties. (Check out the ratings for Elvis or the Beatles on Sullivan.)

By looking at the past thirty years of advances and ignoring the previous ninety, Porter gives us a blatant example of headless clown causal reasoning, arguing that x explains the difference in A and B because x is present in A while ignoring the fact that x is also present in B. Data journalism has fully embraced the idea that two numbers briefly moving in sync constitutes a causal argument.



The phrase "elegant theory" should have set off the red flags and warning lights. Elegance in these books pretty much always means "simplistic and unrealistic." The theories are aesthetically and emotionally appealing but they just barely fit the data in their examples and they usually fall apart completely when taken out on the road.

As previously mentioned, this goes back to what George Polya called (in a quote I really need to dig up) thinking like a mathematician . Polya suggested that the default setting of most people when presented with a rule is to look for examples while the default setting of mathematicians and scientists was to look for exceptions. Mathematical ideas get a tremendous amount of press these days but very few of the people covering them think like mathematicians.