The economic policy of data caps

It is the one year anniversary of the Open Internet Advisory Committee (as noted earlier). Today the committee issued a report of its work over the last year. You can access it here. Today’s post discusses the report about data Caps, which was written by the Economic Impacts working group.

I am a member of the committee and the Economic Impacts Working Group, and I like the work we did. I chair the group. “Chair” is a misleading title for what I really do, which is take notes of the groups’ discussions and transcribe them. Every now and again, I do a little more. As one of the members without any stakes in the outcome, occasionally I offer a synthesis or compromise between distinct views.

The report aims to analyze data caps in the context of the Open Internet Report and Order. The Open Internet Report and Order discusses usage-based pricing (UBP), but does not expressly mention data caps except by implication in that data caps can be considered a form of UBP. The Order left open the possibility of many experiments in business models and pricing.

Moreover, the Internet had evolved over time, and the Order anticipated that the Internet would continue to evolve in unexpected ways. The Order set up the advisory group to consider whether aspects of the Order remain consistent in its effects on the Internet as the Internet evolves, and it is in that spirit that this conversation was undertaken.

Differentiated Platforms

Differentiation is a standard concept for analyzing competition. It describes a common situation, where one firm develops the ability to serve one type of customer in a market—say, buyers who will pay a lot to save time—while a competing firm serves another—say, budget-conscious buyers who are patient.

Differentiation can describe common competitive behavior in technology markets. A chip firm might develop particular attributes—say, faster, energy-hungry electronics for a particular purpose—while their rival might specialize in slower chips that use little energy. This differentiation can earn each firm loyalty from buyers with different preferences.

That motivates today’s question: Can platforms differentiate? Platforms have played an increasingly important role in technology markets in the last decade—in mobile devices, in web services, you name it. A mix of standards composes a platform, complementing many other firms who build services upon the standards.

At first blush the answer appears to be yes. Think of attributes associated with common platforms, such as Windows, Android, Linux, Facebook, or the iPhone. These platforms differ from one another in the marketplace and set themselves apart from near rivals, in ways that earn the loyalty of particular users.

That first impression makes it worth a deeper look. There is more here than meets the eye, and smart firms shape their strategies with subtle thoughtfulness.

Managing Complements

Most software companies fall short of perfection, and that’s just the way it goes. It’s routine for most software firms to slap beta on the product and ship.

Most companies aren’t Apple, however. In case you missed it (and if you did, where have you been?), Apple released imperfect mapping software for the latest iPhone. The Washington Monument ended up on the wrong side of the street. Bus routes didn’t appear in many major cities. Mere glitches for most firms, but for Apple it was embarrassing. The firm has not quite learned how to do beta.

Lost in the ensuing brouhaha was a fundamental economic question. To wit: is it best to own a newly invented and complementary piece of software that works with lots of other software? Even Siri doesn’t have an obvious answer to that question. Let’s consider it.

The Prevailing View

Talk to the management at leading technology firms in the same market, and the similarities in opinions are striking. Most hold roughly the same set of opinions, beliefs, and ideas about how specific actions lead to successful business outcomes. For lack of a better phrase, I call this the “prevailing view.”

The prevailing view is an important aspect of every market. It can persist for a long time, and it can change, sometimes slowly and other times quickly. In common speech, momentous changes define the divide between one era and the next.

Where does the prevailing view come from, and how does it shape economic outcomes? That is this column’s topic.

Does Google get a good ROI in KC?

What in the world is Google doing with its high speed network in Kansas City? Does Goggle expect the revenue to exceed the costs of building the network? Dave Burstein has some numbers to illuminate the question.

Dave Burstein is a communications junky for the major communications junky. He covers many topics in communications markets, and he obsesses over finding the true facts, not merely the sound bites. Last week he provided an outline of the basic economic parameters behind Google’s investment in high speed broadband in Kansas City, and his notes make for interesting reading about the project.

I would guess that most readers of this space are not junkies. I know how to read what Burstein writes. The purpose of this post is to provide a translation.

A Big Payoff

Google and Apple are two of the most profitable companies on the globe today. They seem to share little in common except that achievement. They took very different paths to the stratosphere.

Google, after all, is less than a decade and a half old, a child of the web with a successful approach to advertising, built around a search engine and many services to enhance the user’s experience. Apple is more than twice as old. Its original product, personal computers, makes up a fraction of its sales today, while its future profitability lies with a mix of software in iTunes and new hardware introduced in the last decade—namely, phones, tablets, and portable music devices.

What economic insight emerges from setting these two firms next to one another? A brief discussion of both of their businesses will reveal something trite and something deep. The trite part is this: Some settings produce lots of market value, and some firms capture large parts of that value, but those rarely happen together. The deep part forms the key insight today: these examples are fabulously profitable because they are unique.

The grocery scanner and barcode economy

Think about the world of bar codes and scanners. What was life like before their invention? This post offers an appreciation for this staple of modern retail life.

Give the barcode its due. The widespread deployment of barcodes and scanners reduces the costs of keeping accurate and timely inventories. It happened quietly in the last few decades and had numerous consequences.

Think about it. The number of products on the shelf of a typical retail store has increased by tens of thousands. The accuracy of cashiers has increased tremendously because the cashiers do not have to pause to read the price tag. Firms keep better inventory so the frequency of stock-outs — missing items — also has declined.

More to the point, all of that happened because somebody took the time to develop the bar code. Somebody made effort to get everyone in the industry to invent the equipment to take advantage of barcodes.

Among the influential people in that effort was a fellow named Alan Haberman. He passed away last week.

I never knew the man, so I cannot wax eloquent about his life. But I know something about bar codes, as well as the economics of value built around such symbols. Modern life could not exist without them. That is why this post is not a eulogy. It is an appreciation.

It would be an exaggeration to say that barcodes set me on my life’s intellectual path, but they were an influential example when I was a fledgeling and impressionable scholar. The bar code was one of the three canonical examples of the new era unfolding before us in the 1980s, a world of new standardization and increased interoperability. (VCRs and PCs were the other two). Those three examples, as well as a few others, did motivate my interest in the economics of this phenomenon. As readers of this space know, I have stayed here because new examples arise all the time, and in such diverse areas as WiFi, travel intermediaries, the MP3 player, smart phone, and in many places online.

Alright, maybe I am (a little) nuts, but read on.

In appreciation to Haberman’s life’s work, this is an opportunity to wax on a bit about the joys of the scanner economy. Once you begin to recognize the economics of bar codes, you realize that these economics are everywhere.  I hope you find this interesting, illuminating, and a little amusing.

Digital Dark Matter

Astrophysicists draw on the term “dark matter” to describe the unseen parts of the universe. Many symptoms, such as the rotational speed of galaxies and gravitational effects, indicate the presence of dark matter. Yet, our present science lacks the appropriate concepts and tools for measuring directly what we only see indirectly today.

Economists need a similar label for some important building blocks of the digital economy that we do not measure using standard tools. Many indirect symptoms indicate their growth and importance. Many labels have been proposed—invisible infrastructure and private provision of public goods, for example. These labels capture a grain of truth, and, yet, miss something, too.

Let’s just call it “digital dark matter” and review what we know.

An Economic Perspective on Revolutionary US Inventions

American Heritage marked the twenty fifth anniversary of its magazine, Invention and Technology, with an engaging theme. The editors picked their top twenty five revolutionary inventions in the United States.

Before reviewing the top 25, let’s admit to the obvious. Editors compile lists with full awareness that no list will be definitive. Almost by definition, any educated reader will disagree with someone else’s assessment of the twenty five most revolutionary inventions.

Rather, lists are a contrivance, a device to generate a thoughtful discussion about a topic.

Well, so be it. The editors at Invention and Technology succeeded with me. I would like to offer some alternatives to their list, using an economic sensibility. Economics provides a perspective that differs from a technologists’ perspective, emphasizing new modes of production and improvements in human welfare, namely, the economic impact of invention.

More to the point, this essay will stress what is “missing ” from the list compiled by Invention and Technology. Among the missing are hybrid corn, McDonalds, the cellular phone, the commercial Internet, the assembly line, fast drying paint, the IBM System 360, the birth control pill, and PCR.

In addition, this essay ends with observations about the drawbacks to conceiving of invention too narrowly. I end with several honorable mentions for institutional inventions found uniquely in the US. These were performed at the FDA, within venture capital, and by the traitorous eight (pictured on the right).

I hope you will react to this essay. In other words, you should feel free to disagree, and respond with your own nominees for revolutionary inventions.

Why large firms can’t innovate: Don’t generalize

Recently Robert Scoble published a provocative essay about why Google has difficulty innovating. His answer was provocative, but unbalanced. His answer stressed that Google, like any large firm, has a problem with innovation linked to small teams. The post raises many interesting points, and I recommend reading it.

Perhaps the best feature of the post is Scoble’s incessant piling on of stories. He brings in a few Microsoft stories to bolster the notion that Google’s problems resemble the problems found at other large software firms who had a history of innovation but lost it.

I will admit that it suckered me in. Comparing Google to Microsoft is a bit of a team sport in high tech blogging these days. I have engaged in it myself in these very pages. That does not make the observation valid, however.

More to the point, Scoble’s observations do not generalize. Large firms have difficulty innovating in some contexts. This should come as no surprise, since, after all, firms are built for some sets of problems and not all problems. Hence, no firm is good at everything. Google is large, so it faces challenges in some contexts. What else is new?

(By similar reasoning, by the way, small firms have a hard time innovating in some contexts too. Nobody ever gets exercised over that. But when a large firm faces problems….oh, what hand wringing!)

So here is the big point. While it is very interesting to hear about one rich firm’s problems competing with start-ups, that observation, by itself, does not give anything like a complete picture. This post will consider in what sense Scoble’s observations were incomplete.

Why care about incomplete analysis? Perhaps you will want to read this before you go and short all your Google stock.