One of the examples I use in the book to illustrate the distorting effect of limited "shelf space" in traditional markets is Hollywood box office revenues. The American megaplex theater network has only has enough screens to show about 120 films per year. Meanwhile, there are about 13,000 films shown in film festivals each year. So only a tiny fraction of the movies made get enough theatrical distribution to register any sort of significant box office revenues.
How much bigger would the movie industry be if it didn't have this distribution bottleneck suppressing measured demand for niche film? I get that question all the time, for various markets, and usually I can only guess at the answer. But now Kalevi Kilkki, Principal Scientist at Nokia Siemens Networks, has actually done the math. Building on the work in his earlier paper on this subject, he finds that for movies, the "latent demand" for films that don't get adequate distribution is 60%-70% as big as the existing industry.
In other words, if we had a more efficient delivery system for theatrical films so the carrying capacity of the theatrical network were increased many-fold (using something like digital distribution and projection, which takes most of the cost out of distribution and adds ultimate flexibility over where the films run and how long), box office revenues could theoretically be 60-70% larger than they are today.
In practice, there are other economic barriers to giving all 13,000 films their day on the screen, such as the cost of marketing, the theaters and the screens themselves. But we do have another distribution system that does ahve unlimited capacity and the ability to play on an infinite number of screens. That's Netflix, which currently offers 80,000 DVD titles, and as I've shown before its rentals do follow the powerlaw model very neatly.
Here's how Kilkki comes up with his estimate: If you rank films by their box office revenues and plot the results on a log-log scale and you get the following. The dotted blue line is the real data from 2006; the solid blue line is what the powerlaw/long tail model would predict, and the red line represents curve fit of the real-world data.
Once you have equations to describe both the predicted demand and the measured demand, you can figure out how much one differs from the other. The answer varies from country to country, but the US gap is the largest. If you're into powerlaw math, you can read more in his research note. It's a clever model he's come up with and you'll find that it can be applied to markets of all sorts. This one's for you, math geeks!