Book Club: The Code Economy, Chs. 6-7: Learning Curves

Welcome back to EvX’s Book Club: The Code Economy, by Philip Auerswald. Today’s entry is going to be quick, because summer has started and I don’t have much time. Ch 6 is titled Information, and can perhaps be best summarized:

The challenge is to build a reliable economy with less reliable people. In this way, the economy is an information processing organism. …

When I assert that economics must properly be understood as a branch of information theory, I am reffing to the centrality of the communication problem that exists whenever one person attempts to share know-how with another. I am referring, in other words, to the centrality of code.

Auerswald goes on to sketch some relevant background on “code” as a branch of economics:

The economics taught in undergraduate courses is a great example of history being written by the victors. Because the methodologies of neoclassical economics experienced numerous triumphs in the middle of the twentieth century, the study of the distribution of resources within the economy–choice rather than code and, to a lesser extent, consumption rather than  production–came to be taught as the totality of economics. However, the reality is that choice and code have always coexisted, not only in the economy itself but in the history of economic thought.

And, an aside, but interesting:

Indeed, from 1807 to the time Jevons was born, the volume of shipping flowing through the port of Liverpool more than doubled.

However:

And yet, while both the city’s population and worker’s wages increased steadily, a severe economic rift occurred that separated the haves from the have-nots. As wealthy Liverpudlians moved away from the docks to newly fashionable districts on the edges of the city, those left behind in the center of town faced miserable conditions.From 1830 through 1850, life expectancy actually decreased in Liverpool proper from an already miserable 32 years to a shocking 25 years.

(You should see what happened to life expectancies in Ireland around that time.)

A reliable economy built with less reliable people is one in which individuals have very little autonomy, because autonomy means unreliable people messing things up.

Thereafter, a series of economists, Herbert Simon foremost among them, put the challenges of gathering, sharing, and processing economically relevant information at the center of their work.

Taken together and combined with foundational insights from other fields–notably evolutionary biology and molecular biology–the contributions of these economists constitute a distinct domain of inquiry within economics. These contributions have focused on people as producers and on the algorithms that drive the development of the economy.

This domain of inquiry is Code Economics.

I am rather in love with taking the evolutionary model and applying it to other fields, like the spread of ideas (memes) or the growth of cities, companies, economies, or whole countries. That is kind of what we do, here at EvolutionistX.

Chapter 7 is titled Learning: The Dividend of Doing. It begins with an amusing tale about Julia Child, who did not even learn to cook until her mid to late thirties, and then became a rather famous chef/cookbook writer. (Cooking recipes are one of Auerswald’s favored examples of “code” in action.)

Next Auerswald discusses Francis Walker, first president of the American Economic Association. Walker disagreed with the “wage fund theory” and with Jevons’s simplifying assumption that firms can be modeled as simply hiring workers until it cannot make any more money by hiring more workers than by investing in more capital.

Jevons’s formulation pushes production algorithms–how businesses are actually being run–into the background and tradeoffs between labor and capital to the foreground. But as Walker notes:

“We have the phenomenon in every community and in every trade, in whatever state of the market,” Walker observes, “of some employers realizing no profits at all, while other are making fair profits; others, again, large profits; others, still, colossal profits. Side by side, in the same business, with equal command of capital, with equal opportunities, one man is gradually sinking a fortune, while another is doubling or trebling his accumulations.”

The relevant economic data, when it finally became available, confirmed Walker’s belief about the distribution of profits, yet the difference between the high-profit and low-profit firms does not appear to hinge primarily on the question of how much labor should be substituted for capital and vice versa.

Walker argued that more profitable entrepreneurs are that way because they are able to solve a difficult problem more effectively than other entrepreneurs. … three core mechanisms for the advance of code: learning, evolution, and the layering of complexity though the development of platforms.

Moreover:

…in the empirical economics of production, few discoveries have been more universal or significant than that of the firm-level learning curve. As economist James Bessen notes, “Developing the knowledge and skills needed to implement new technologies on a large scale is a difficult social problem that takes a long time to resolve… A new technology typically requires more than an invention in order to be designed, built, installed, operated, and maintained. Initially, much of this new technical knowledge develops slowly because it is learned through experience, not in the classroom.”

Those of you who are familiar with business economics probably find learning curves and firm growth curves boring and old-hat, but they’re new and quite fascinating to me. Auerswald has an interesting story about the development of airplanes and a challenge to develop cheaper, two-seat planes during the Depression–could a plane be built for under a $1,000? $700?

(Make sure to read the footnote on the speed of production of “Liberty Ships.”)

The rest of the chapter discusses the importance of proper firm management for maximizing efficiency and profits. Now, I have an instinctual aversion to managers, due to my perception that they tend to be parasitic on their workers or at least in competition with them for resources/effort, but I can admit that a well-run company is likely more profitable than a badly run one. Whether it is more pleasant for the workers is another matter, as the folks working in Amazon’s warehouses can tell you.

So why are some countries rich and others poor?

Whereas dominant variants of the neoclassical production model emphasize categories such as public knowledge and organization, which can be copied and implemented at zero cost, code economics suggests that such categories are unlikely to be significantly relevant in the practical work of creating the business entities that drive the progress of human society. This is because code at the level of a single company–what I term a “production algorithm”–includes firm-specific components. Producers far from dominant production clusters must learn to produce through a costly process of trial and error. Market-driven advances in production recipes, from which venture with proprietary value can be created, require a tenacious will to experiment, to learn, and to document carefully the results of that learning. Heterogeneity among managers… is thus central to understanding observed differences between regions and nations. …

Management and the development of technical standards combined to enable not just machines but organizations to be interoperable and collaborative. Companies thus could become far bigger and supply chains far more complex than every before.

As someone who actually likes shopping at Ikea, I guess I should thank a manager somewhere.

Auerswald points out that if communication of production algorithms and company methods were perfect and costless, then learning curves wouldn’t exist:

All of these examples underscore the following point, core to code economics: The imperfection of communication is not a theory. It is a ubiquitous and inescapable physical reality.

That’s all for now, but how are you enjoying the book? Do you have any thoughts on these chapters? I enjoyed them quite a bit–especially the part about Intel and the graphs of the distribution of management scores by country. What do you think of France and the UK’s rather lower “management” scores than the US and Germany?

Join us next week for Ch. 8: Evolution–should be exciting!

One thought on “Book Club: The Code Economy, Chs. 6-7: Learning Curves

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s