Russ Roberts talks with Ed Leamer about inequality, among other things. It’s a great conversation. One of my favorite parts is when they mull on a possible scenario in which 1 person is a coder and 99 people are barbers. It simply is not possible to re-train the barbers to be productive as coders. Supply and demand being what they are, the coder makes a lot more money than the barbers.
Another determinant of inequality is that the barber’s capital equipment–the electric shaver and the chair–can only serve one customer at a time. The coder’s capital equipment–the server and the Internet connection–can reach the whole world. I think in the talk they use the metaphor of the forklift vs. the microphone.
Highly recommended, although not on the topics that I thought earned Leamer the Nobel Prize.
Life above and below the API – now the barbers work for Great Cuts and the computer assigns them customers so they don’t set their own schedules or have enduring relationships. There aren’t many people above the API at Great Cuts.
Bloomberg, six years ago, You can’t teach a coal miner to code
Biden, Learn to Code.
Understood that a community with 1 coder and 99 barbers may have less equal outcomes than one with 100 barbers. But, which one is better off? Some people look at the inequality of outcomes between the 1 coder and 99 barbers, the equality of outcomes among the 100 barbers, and seem to draw the conclusion that a community of 100 barbers would have the incomes of 100 coders. That last conclusion does not follow.
You can compare jobs along these three dimensions:
Tangible vs. Intangible Output, “Reproducible”
Labor vs. Capital Bottleneck for Productivity, “Augmentable”
Proximate vs. Distant, “Outsourceable”
Tangibles can’t be copied, can’t be scaled to any level at very low marginal cost.
Labor bottlenecks are “Baumol Jobs”. The thing is produced and/or consumed at human speed.
Proximate jobs need to be done (at least some of the time) “face to face” – in close proximity, to other people involved in production or exchange: team members, supervisors, customers, clients, suppliers, etc. “Economies of agglomeration” could be split into labor-focused (cities) or related-outputs-focused (business clusters, maybe Krugman’s “Geography and Trade”). This is the human-based focus.
So a barber produces a tangible output, with a labor bottleneck, that has to be done close to customers. (0,0,0) Maids prepping rooms at hotels (0,0,0). Note that until fully autonomous AI is here, uber drivers and pilots flying planes are also (0,0,0). Surgeons and trial lawyers too, (0,0,0)
A coal miner produces a tangible output, with a capital bottleneck. He has to work at the mine close to a few other support staff, but it’s not the tight “economic spiderweb” which needs to be done in an urban context, the output can be shipped anywhere, and competes with similar output being shipped from elsewhere. (0,1,0.8) Manufacturing is like coal-mining with mines that can be moved. (0,1,1)
A writer of novels produced an intangible output, limited by human speed, but which can be done anywhere (e.g., the archetypal “remote cabin”, or Paris). (1,0,1). An automated writing bot would be (1,1,1).
“Coder” is complicated, but it’s obviously intangible, and the tools that help them build faster are relatively cheap, so the limit isn’t capital but human speed in thinking and coding, and we can observe that a lot of them apparently need to agglomerate and pay a fortune in order to do so. (1,0,0.5)
My explanation for pre-crisis economic trends says that in countries with expensive labor, the outsourceable jobs will be outsourced, and the augmentable and intangible sectors won’t be able to hire much of the labor force, since they don’t need much labor. More and more of the jobs will have a (X,0,0) character, “Handle-Baumol Jobs”.
There is some inequality among the (0,0,0) for ordinary reasons of skill scarcity and supply and demand (e.g., Surgeon vs. Maid). It’s mostly still in the “order of magnitude” level of difference, correcting for taxes, benefits, housing costs, etc.
There is a ton of inequality the closer one gets to (1,0,0), because high scalability tends toward winner-take-all / “average is over”, dynamics, and winning some tournaments is a lottery ticket that can pay off in three commas.
I found the observation on “intellectual services” to be very interesting. It is the first time I’ve seen the change in higher ed so succinctly laid out. Education is still necessary, but it will definitely have to change from what most modern education has become. Learning to solve problems, not presented in the book(s), is increasingly important. Applying what you learned in one are to a new area, new field is rewarded.
I started out as an engineering major, the first year ENG 101/102 was basically high school physics concurrent with Calculus. But I’ve come to realize the content was the vehicle. The class was constant problem solving. Developing the skills to take problem, break it down into pieces that could be analyzed. Later, those skills were applied to more complex topics, but disciplining your intellect was still an underlying part of the instruction. Other courses outside of the “hard” sciences were more about knowing the body of knowledge.
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“But, I want to go to the other end of the spectrum, which is intellectual services. It used to be, if you wave your Bachelor’s degree, you’re going to get a great job. When I graduated from college, it was a sure thing that you’d get a great job. And, in college, you’d basically learned artificial intelligence, meaning, you carried out the instructions that the faculty member gave you. You memorized the lectures, and you were tested on your memory in the exams. That’s what a computer does. It basically memorizes what you tell it to do.
“But now, with a computer doing all those mundane, repetitive intellectual tasks, if you’re expecting to do well in the job market, you have to bring, you have to have real education. Real education means to solve problems that the faculty who teach don’t really know how to solve.
“And that takes talent as well as education.
“So, my view is we’ve got to change education from a kind of a big Xerox machine where the lectures are memorized and then tested, into one which is more experienced-based to prepare a workforce for the reality of the 20th century. You’ve got to recognize that just because you had an experience with, say, issues in accounting, doesn’t mean that you have the ability to innovate and take care of customers who have problems that cannot be coded.”