Book Club: The Code Economy, Chapter 11: Education and Death

Welcome back to EvX’s book club. Today we’re reading Chapter 11 of The Code Economy, Education.

…since the 1970s, the economically fortunate among us have been those who made the “go to college” choice. This group has seen its income row rapidly and its share of the aggregate wealth increase sharply. Those without a college education have watched their income stagnate and their share of the aggregate wealth decline. …

Middle-age white men without a college degree have been beset by sharply rising death rates–a phenomenon that contrasts starkly with middle-age Latino and African American men, and with trends in nearly every other country in the world.

It turns out that I have a lot of graphs on this subject. There’s a strong correlation between “white death” and “Trump support.”

White vs. non-white Americans

American whites vs. other first world nations


But “white men” doesn’t tell the complete story, as death rates for women have been increasing at about the same rate. The Great White Death seems to be as much a female phenomenon as a male one–men just started out with higher death rates in the first place.

Many of these are deaths of despair–suicide, directly or through simply giving up on living. Many involve drugs or alcohol. And many are due to diseases, like cancer and diabetes, that used to hit later in life.

We might at first think the change is just an artifact of more people going to college–perhaps there was always a sub-set of people who died young, but in the days before most people went to college, nothing distinguished them particularly from their peers. Today, with more people going to college, perhaps the destined-to-die are disproportionately concentrated among folks who didn’t make it to college. However, if this were true, we’d expect death rates to hold steady for whites overall–and they have not.

Whatever is affecting lower-class whites, it’s real.

Auerswald then discusses the “Permanent income hypothesis”, developed by Milton Friedman: Children and young adults devote their time to education, (even going into debt,) which allows us to get a better job in mid-life. When we get a job, we stop going to school and start saving for retirement. Then we retire.

The permanent income hypothesis is built into the very structure of our society, from Public Schools that serve students between the ages of 5 and 18, to Pell Grants for college students, to Social Security benefits that kick in at 65. The assumption, more or less, is that a one-time investment in education early in life will pay off for the rest of one’s life–a payout of such returns to scale that it is even sensible for students and parents to take out tremendous debt to pay for that education.

But this is dependent on that education actually paying off–and that is dependent on the skills people learn during their educations being in demand and sufficient for their jobs for the next 40 years.

The system falls apart if technology advances and thus job requirements change faster than once every 40 years. We are now looking at a world where people’s investments in education can be obsolete by the time they graduate, much less by the time they retire.

Right now, people are trying to make up for the decreasing returns to education (a highschool degree does not get you the same job today as it did in 1950) by investing more money and time into the single-use system–encouraging toddlers to go to school on the one end and poor students to take out more debt for college on the other.

This is probably a mistake, given the time-dependent nature of the problem.

The obvious solution is to change how we think of education and work. Instead of a single, one-time investment, education will have to continue after people begin working, probably in bursts. Companies will continually need to re-train workers in new technology and innovations. Education cannot be just a single investment, but a life-long process.

But that is hard to do if people are already in debt from all of the college they just paid for.

Auerswald then discusses some fascinating work by Bessen on how the industrial revolution affected incomes and production among textile workers:

… while a handloom weaver in 1800 required nearly forty minutes to weave a yard of coarse cloth using a single loom, a weaver in 1902 could do the same work operating eighteen Nothrop looms in less than a minute, on average. This striking point relates to the relative importance of the accumulation of capital to the advance of code: “Of the roughly thirty-nine-minute reduction in labor time per yard, capital accumulation due to the changing cost of capital relative to wages accounted for just 2 percent of the reduction; invention accounted for 73 percent of the reduction; and 25 percent of the time saving came from greater skill and effort of the weavers.” … “the role of capital accumulation was minimal, counter to the conventional wisdom.”

Then Auerswald proclaims:

What was the role of formal education in this process? Essentially nonexistent.


New technologies are simply too new for anyone to learn about them in school. Flexible thinkers who learn fast (generalists) thus benefit from new technologies and are crucial for their early development. Once a technology matures, however, it becomes codified into platforms and standards that can be taught, at which point demand for generalists declines and demand for workers with educational training in the specific field rises.

For Bessen, formal education and basic research are not the keys to the development of economies that they are often represented a being. What drives the development of economies is learning by doing and the advance of code–processes that are driven at least as much by non-expert tinkering as by formal research and instruction.

Make sure to read the endnotes to this chapter; several of them are very interesting. For example, #3 begins:

“Typically, new technologies demand that a large number of variables be properly controlled. Henry Bessemer’s simple principle of refining molten iron with a blast of oxygen work properly only at the right temperatures, in the right size vessel, with the right sort of vessel refractory lining, the right volume and temperature of air, and the right ores…” Furthermore, the products of these factories were really one that, in the United States, previously had been created at home, not by craftsmen…

#8 states:

“Early-stage technologies–those with relatively little standardized knowledge–tend to be used at a smaller scale; activity is localized; personal training and direct knowledge sharing are important, and labor markets do not compensate workers for their new skills. Mature technologies–with greater standardized knowledge–operate at large scale and globally, market permitting; formalized training and knowledge are more common; and robust labor markets encourage workers to develop their own skills.” … The intensity of of interactions that occur in cities is also important in this phase: “During the early stages, when formalized instruction is limited, person-to-person exchange is especially important for spreading knowledge.”

This reminds me of a post on Bruce Charlton’s blog about “Head Girl Syndrome“:

The ideal Head Girl is an all-rounder: performs extremely well in all school subjects and has a very high Grade Point Average. She is excellent at sports, Captaining all the major teams. She is also pretty, popular, sociable and well-behaved.

The Head Girl will probably be a big success in life…

But the Head Girl is not, cannot be, a creative genius.


Modern society is run by Head Girls, of both sexes, hence there is no place for the creative genius.

Modern Colleges aim at recruiting Head Girls, so do universities, so does science, so do the arts, so does the mass media, so does the legal profession, so does medicine, so does the military…

And in doing so, they filter-out and exclude creative genius.

Creative geniuses invent new technologies; head girls oversee the implementation and running of code. Systems that run on code can run very smoothly and do many things well, but they are bad at handling creative geniuses, as many a genius will inform you of their public school experience.

How different stages in the adoption of new technology and its codification into platforms translates into wages over time is a subject I’d like to see more of.

Auerswald then turns to the perennial problem of what happens when not only do the jobs change, they entirely disappear due to increasing robotification:

Indeed, many of the frontier business models shaping the economy today are based on enabling a sharp reduction in the number of people required to perform existing tasks.

One possibility Auerswald envisions is a kind of return to the personalized markets of yesteryear, when before massive industrial giants like Walmart sprang up. Via internet-based platforms like Uber or AirBNB, individuals can connect directly with people who’d like to buy their goods or services.

Since services make up more than 84% of the US economy and an increasingly comparable percentage in coutnries elsewhere, this is a big deal.

It’s easy to imagine this future in which we are all like some sort of digital Amish, continually networked via our phones to engage in small transactions like sewing a pair of trousers for a neighbor, mowing a lawn, selling a few dozen tacos, or driving people to the airport during a few spare hours on a Friday afternoon. It’s also easy to imagine how Walmart might still have massive economies of scale over individuals and the whole system might fail miserably.

However, if we take the entrepreneurial perspective, such enterprises are intriguing. Uber and Airbnb work by essentially “unlocking” latent assets–time when people’s cars or homes were sitting around unused. Anyone who can find other, similar latent assets and figure out how to unlock them could become similarly successful.

I’ve got an underutilized asset: rural poor. People in cities are easy to hire and easy to direct toward educational opportunities. Kids growing up in rural areas are often out of the communications loop (the internet doesn’t work terribly well in many rural areas) and have to drive a long way to job interviews.

In general, it’s tough to network large rural areas in the same ways that cities get networked.

On the matter of why peer-to-peer networks have emerged in certain industries, Auerswald makes a claim that I feel compelled to contradict:

The peer-to-peer business models in local transportation, hospitality, food service, and the rental of consumer goods were the first to emerge, not because they are the most important for the economy but because these are industries with relatively low regulatory complexity.

No no no!

Food trucks emerged because heavy regulations on restaurants (eg, fire code, disability access, landscaping,) have cut significantly into profits for restaurants housed in actual buildings.

Uber emerged because the cost of a cab medallion–that is, a license to drive a cab–hit 1.3 MILLION DOLLARS in NYC. It’s a lucrative industry that people were being kept out of.

In contrast, there has been little peer-to-peer business innovation in healthcare, energy, and education–three industries that comprise more than a quarter of the US GDP–where regulatory complexity is relatively high.

Again, no.

There is a ton of competition in healthcare; just look up naturopaths and chiropractors. Sure, most of them are quacks, but they’re definitely out there, competing with regular doctors for patients. (Midwives appear to be actually pretty effective at what they do and significantly cheaper than standard ob-gyns.)

The difficulty with peer-to-peer healthcare isn’t regulation but knowledge and equipment. Most Americans own a car and know how to drive, and therefore can join Uber. Most Americans do not know how to do heart surgery and do not have the proper equipment to do it with. With training I might be able to set a bone, but I don’t own an x-ray machine. And you definitely don’t want me manufacturing my own medications. I’m not even good at making soup.

Education has tons of peer-to-peer innovation. I homeschool my children. Sometimes grandma and grandpa teach the children. Many homeschoolers join consortia that offer group classes, often taught by a knowledgeable parent or hired tutor. Even people who aren’t homeschooling their kids often hire tutors, through organizations like Wyzant or afterschool test-prep centers like Kumon. One of my acquaintances makes her living primarily by skype-tutoring Koreans in English.

And that’s not even counting private schools.

Yes, if you want to set up a formal “school,” you will encounter a lot of regulation. But if you just want to teach stuff, there’s nothing stopping you except your ability to find students who’ll pay you to learn it.

Now, energy is interesting. Here Auerswsald might be correct. I have trouble imagining people setting up their own hydroelectric dams without getting into trouble with the EPA (not to mention everyone downstream.)

But what if I set up my own windmill in my backyard? Can I connect it to the electric grid and sell energy to my neighbors on a windy day? A quick search brings up WindExchange, which says, very directly:

Owners of wind turbines interconnected directly to the transmission or distribution grid, or that produce more power than the host consumes, can sell wind power as well as other generation attributes.

So, maybe you can’t set up your own nuclear reactor, and maybe the EPA has a thing about not disturbing fish, but it looks like you can sell wind and solar energy back to the grid.

I find this a rather exciting thought.

Ultimately, while Auerswald does return to and address the need to radically change how we think about education and the education-job-retirement lifepath, he doesn’t return to the increasing white death rate. Why are white death rates increasing faster than other death rates, and will transition to the “gig economy” further accelerate this trend? Or was the past simply anomalous for having low white death rates, or could these death rates be driven by something independent of the economy itself?

Now, it’s getting late, so that’s enough for tonight, but what are your thoughts? How do you think this new economy–and educational landscape–will play out?


Book Club: Code Economy, Ch. 10: In which I am Confused

Welcome back to EvX’s Book Club. Today we start the third (and final) part of Auerswald’s The Code Economy: The Human Advantage.

Chapter 10: Complementarity discuses bifurcation, a concept Auerswald mentions frequently throughout the book. He has a graph of the process of bifurcation, whereby the development of new code (ie, technology), leads to the creation of a new “platform” on the one hand, and new human work on the other. With each bifurcation, we move away from the corner of the graph marked “simplicity” and “autonomy,” and toward the corner marked “complexity” and “interdependence.” It looks remarkably like a graph I made about energy inputs vs outputs at different complexity levels, based on a memory of a graph I saw in a textbook some years ago.

There are some crucial differences between our two graphs, but I think they nonetheless related–and possibly trying to express the same thing.

Auerswald argues that as code becomes platform, it doesn’t steal jobs, but becomes the new base upon which people work. The Industrial Revolution eliminated the majority of farm laborers via automation, but simultaneously provided new jobs for them, in factories. Today, the internet is the “platform” where jobs are being created, not in building the internet, but via businesses like Uber that couldn’t exist without the internet.

Auerswald’s graph (not mine) is one of the few places in the book where he comes close to examining the problem of intelligence. It is difficult to see what unintelligent people are going to do in a world that is rapidly becoming more complicated.

On the other hand people who didn’t have access to all sorts of resources now do, due to internet-based platforms–people in the third world, for example, who never bought land-line telephones because their country couldn’t afford to build the infrastructure to support them, are snapping up mobile and smartphones at an extraordinary rate:

And overwhelming majorities in almost every nation surveyed report owning some form of mobile device, even if they are not considered “smartphones.”

And just like Auerswald’s learning curves from the last chapter, technological spread is speeding up. It took the landline telephone 64 years to go from 0% to 40% of the US market. Mobile phones took only 20 years to accomplish the same feat, and smartphones did it in about 10. (source.)

There are now more mobile phones in the developing world than the first world, and people aren’t just buying just buying these phones to chat. People who can’t afford to open bank accounts now use their smarphones as “mobile wallets”:

According to the GSMA, an industry group for the mobile communications business, there are now 79 mobile money systems globally, mostly in Africa and Asia. Two-thirds of them have been launched since 2009.

To date, the most successful example is M-Pesa, which Vodafone launched in Kenya in 2007. A little over three years later, the service has 13.5 million users, who are expected to send 20 percent of the country’s GDP through the system this year. “We proved at Vodafone that if you get the proposition right, the scale-up is massive,” says Nick Hughes, M-Pesa’s inventor.

But let’s get back to Auerswald. Chapter 10 contains a very interesting description of the development of the development of the Swiss Watch industry. Of course, today, most people don’t go out of their way to buy watches, since their smartphones have clocks built into them. Have smartphones put the Swiss out of business? Not quite, says Auerswald:

Switzerland… today produces fewer than 5 percent of the timepieces manufactured for export globally. In 2014, Switzerland exported 29 million watches, as compaed to China’ 669 million… But what of value? … Swiss watch exports were worth 24.3 billion in 2014, nearly five times as much as all Chinese watches combined.

Aside from the previously mentioned bifurcation of human and machine labor, Auerswald suggests that automation bifurcates products into cheap and expensive ones. He claims that movies, visual art services (ie, copying and digitization of art vs. fine art,) and music have also undergone bifurcation, not extinction, due to new technology.

In each instance, disruptive advances in code followed a consistent and predictable pattern: the creation of a new high-volume, low-price option creates a new market for the low-volume, high-price option. Every time this happens, the new value created through improved code forces a bifurcation of markets, and of work.


He then discusses a watch-making startup located in Detroit, which I feel completely and totally misses the point of whatever economic lessons we can draw from Detroit.

Detroit is, at least currently, a lesson in how people fail to deal with increasing complexity, much less bifurcation.

Even that word–bifurcation–contains a problem: what happens to the middle? A huge mass of people at the bottom, making and consuming cheap products, and a small class at the top, making and consuming expensive products–well I will honor the demonstrated preferences of everyone involved for stuff, of whatever price, but what about the middle?

Is this how the middle class dies?

But if the poor become rich enough… does it matter?

Because work is fundamentally algorithmic, it is capable of almost limitless diversification though both combinatorial and incremental change. The algorithms of work become, fairly literally, the DNA of the economy. …

As Geoff Moore puts it, “Digital innovation is reengineering our manufacturing-based product-centric economy to improve quality, reduce cost, expand markets, … It is doing so, however, largely at the expense of traditional middle class jobs. This class of work is bifurcating into elite professions that are highly compensated but outside the skillset of the target population and commoditizing workloads for which the wages fall well below the target level.”

It is easy to take the long view and say, “Hey, the agricultural revolution didn’t result in massive unemployment among hunter-gatherers; the bronze and iron ages didn’t result in unemployed flint-knappers starving in the streets, so we’ll probably survive the singularity, too,” and equally easy to take the short view and say, “screw the singularity, I need a job that pays the bills now.”

Auerswald then discusses the possibilities for using big data and mobile/wearable computers to bring down healthcare costs. I am also in the middle of a Big Data reading binge, and my general impression of health care is that there is a ton of data out there (and more being collected every day,) but it is unwieldy and disorganized and doctors are too busy to use most of it and patients don’t have access to it. and if someone can amass, organize, and sort that data in useful ways, some very useful discoveries could be made.

Then we get to the graph that I didn’t understand,”Trends in Nonroutine Task Input, 1960 to 1998,” which is a bad sign for my future employment options in this new economy.

My main question is what is meant by “nonroutine manual” tasks, and since these were the occupations with the biggest effect shown on the graph, why aren’t they mentioned in the abstract?:

We contend that computer capital (1) substitutes for a limited and well-defined set of human activities, those involving routine (repetitive) cognitive and manual tasks; and (2) complements activities involving non-routine problem solving and interactive tasks. …Computerization is associated with declining relative industry demand for routine manual and cognitive tasks and increased relative demand for non-routine cognitive tasks.

Yes, but what about the non-routine manual? What is that, and why did it disappear first? And does this graph account for increased offshoring of manufacturing jobs to China?

If you ask me, it looks like there are three different events recorded in the graph, not just one. First, from 1960 onward, “non-routine manual” jobs plummet. Second, from 1960 through 1970, “routine cognitive” and “routine manual” jobs increase faster than “non-routine analytic” and “non-routine interactive.” Third, from 1980 onward, the routine jobs head downward while the analytic and interactive jobs become more common.

*Downloads the PDF and begins to read* Here’s the explanation of non-routine manual:

Both optical recognition of objects in a visual field and bipedal locomotion across an uneven surface appear to require enormously sophisticated algorithms, the one in optics and the other in mechanics, which are currently poorly understood by cognitive science (Pinker, 1997). These same problems explain the earlier mentioned inability of computers to perform the tasks of long haul truckers.

In this paper we refer to such tasks requiring visual and manual skills as ‘non-routine manual activities.’

This does not resolve the question.

Discussion from the paper:

Trends in routine task input, both cognitive and manual, also follow a striking pattern. During the  1960s, both forms of input increased due to a combination of between- and within-industry shifts. In the 1970s, however, within-industry input of both tasks declined, with the rate of decline accelerating.

As distinct from the other four task measures, we observe steady within- and between-industry shifts against non-routine manual tasks for the entire four decades of our sample. Since our conceptual framework indicates that non-routine manual tasks are largely orthogonal to computerization, we view
this pattern as neither supportive nor at odds with our model.

Now, it’s 4 am and the world is swimming a bit, but I think “we aren’t predicting any particular effect on non-routine manual tasks” should have been stated up front in the thesis portion. Sticking it in here feels like ad-hoc explaining away of a discrepancy. “Well, all of the other non-routine tasks went up, but this one didn’t, so, well, it doesn’t count because they’re hard to computerize.”

Anyway, the paper is 62 pages long, including the tables and charts, and I’m not reading it all or second-guessing their math at this hour, but I feel like there is something circular in all of this–“We already know that jobs involving routine labor like manufacturing are down, so we made a models saying they decreased as a percent of jobs because of computers and automation, looked through jobs data, and low and behold, found that they had decreased. Confusingly, though, we also found that non-routine manual jobs decreased during this time period, even though they don’t lend themselves to automation and computerization.”

I also searched in the document and could find no instance of the words “offshor-” “China” “export” or “outsource.”

Also, the graph Auerswald uses and the corresponding graph in the paper have some significant differences, especially the “routine cognitive” line. Maybe the authors updated their graph with more data, or Auerswald was trying to make the graph clearer. I don’t know.

Whatever is up with this paper, I think we may provisionally accept its data–fewer factory workers, more lawyers–without necessarily accepting its model.

The day after I wrote this, I happened to be reading Davidowitz’s Everybody Lies: Big Data, New Data, and What the Internet Can Tell us about who we Really Are, which has a discussion of the best places to raise children.

Talking about Chetty’s data, Davidowitz writes:

The question asked: what is the chance that a person with parents in the bottom 20 percent of the income distribution reaches the top 20 percent of the income distribution? …

So what is it about part of the United States where there is high income mobility? What makes some places better at equaling the playing field, of allowing a poor kid to have a pretty good life? Areas that spend more on education provide a better chance to poor kids. Places with more religious people and lower crime do better. Places with more black people do worse. Interestingly, this has an effect on not just the black kids but on the white kids living there as well.

Here is Chetty’s map of upward mobility (or the lack thereof) by county. Given how closely it matches a map of “African Americans” + “Native Americans” I have my reservations about the value of Chetty’s research on the bottom end (is anyone really shocked to discover that black kids enjoy little upward mobility?) but it still has some comparative value.

Davidowitz then discusses Chetty’s analysis of where people live the longest:

Interestingly, for the wealthiest Americans, life expectancy is hardly affected by where you live. …

For the poorest Americans, life expectancy varies tremendously…. living in the right place can add five years to a poor person’s life expectancy. …

religion, environment, and health insurance–do not correlate with longer life spans for the poor. The variable that does matter, according to Chetty and the others who worked on this study? How many rich people live in a city. More rich people in a city means the poor there live longer. Poor people in New York City, for example, live longer than poor people in Detroit.

Davidowitz suggests that maybe this happens because the poor learn better habits from the rich. I suspect the answer is simpler–here are a few possibilities:

1. The rich are effectively stopping the poor from doing self-destructive things, whether positively, eg, funding cultural that poor people go to rather than turn to drugs or crime out of boredom, or negatively, eg, funding police forces that discourage life-shortening crime.

2. The rich fund/support projects that improve general health, like cleaner water systems or better hospitals.

3. The effect is basically just a measurement error that doesn’t account for rich people driving up land prices. The “poor” of New York would be wealthier if they had Detroit rents.

(In general, I think Davidowitz is stronger when looking for correlations in the data than when suggesting explanations for it.)

Now contrast this with Davidowitz’s own study on where top achievers grow up:

I was curious where the most successful Americans come from, so one day I decided to download Wikipedia. …

[After some narrowing for practical reasons] Roughly 2,058 American-born baby boomers were deemed notable enough to warrant a Wikipedia entry. About 30 percent made it through achievements in art or entertainment, 29 percent through sports, 9 percent via politics, and 3 percent in academia or science.

And this is why we are doomed.

The first striking fact I noticed in the data was the enormous geographic variation in the likelihood of becoming a big success …

Roughly one in 1,209 baby boomers born in California reached Wikipedia. Only one in 4,496 baby boomers born in West Virginia did. … Roughly one in 748 baby boomers born in Suffolk County, MA, here Boston is located, made it to Wikipedia. In some counties, the success rate was twenty times lower. …

I closely examined the top counties. It turns out that nearly all of them fit into one of two categories.

First, and this surprised me, many of these counties contained a sizable college town. …

I don’t know why that would surprise anyone. But this was interesting:

Of fewer than 13,000 boomers born in Macon County, Alabama, fifteen made it to Wikipedia–or one in 852. Every single one of them is black. Fourteen of them were from the town of Tuskegee, home of Tuskegee University, a historically black college founded by Booker . Washington. The list included judges, writers, and scientists. In fact, a black child born in Tuskegee had the same probability of becoming a notable in a field outside of ports as a white child born in some of the highest-scoring, majority-white college towns.

The other factor that correlates with the production of notables?

A big city.

Being born in born in San Francisco County, Los Angeles County, or New York City all offered among the highest probabilities of making it to Wikipedia. …

Suburban counties, unless they contained major college towns, performed far worse than their urban counterparts.

A third factor that correlates with success is the proportion of immigrants in a county, though I am skeptical of this finding because I’ve never gotten the impression that the southern border of Texas produces a lot of famous people.

Migrant farm laborers aside, though, America’s immigrant population tends to be pretty well selected overall and thus produces lots of high-achievers. (Steve Jobs, for example, was the son of a Syrian immigrant; Thomas Edison was the son of a Canadian refugee.)

The variable that didn’t predict notability:

One I found more than a little surprising was how much money a state spends on education. In states with similar percentages of its residents living in urban areas, education spending did not correlate with rates of producing notable writers, artists, or business leaders.

Of course, this is probably because 1. districts increase spending when students do poorly in school, and 2. because rich people in urban send their kids to private schools.


It is interesting to compare my Wikipedia study to one of Chetty’s team’s studies discussed earlier. Recall that Chetty’s team was trying to figure out what areas are good at allowing people to reach the upper middle class. My study was trying to figure out what areas are good at allowing people to reach fame. The results are strikingly different.

Spending a lot on education help kids reach the upper middle class. It does little to help them become a notable writer, artist, or business leader. Many of these huge successes hated school. Some dropped out.

Some, like Mark Zuckerberg, went to private school.

New York City, Chetty’s team found, is not a particularly good place to raise a child if you want to ensure he reaches the upper middle class. it is a great place, my study found, if you want to give him a chance at fame.

A couple of methodological notes:

Note that Chetty’s data not only looked at where people were born, but also at mobility–poor people who moved from the Deep South to the Midwest were also more likely to become upper middle class, and poor people who moved from the Midwest to NYC were also more likely to stay poor.

Davidowitz’s data only looks at where people were born; he does not answer whether moving to NYC makes you more likely to become famous. He also doesn’t discuss who is becoming notable–are cities engines to make the children of already successful people becoming even more successful, or are they places where even the poor have a shot at being famous?

I reject Davidowitz’s conclusions (which impute causation where there is only correlation) and substitute my own:

Cities are acceleration platforms for code. Code creates bifurcation. Bifurcation creates winners and losers while obliterating the middle.

This is not necessarily a problem if your alternatives are worse–if your choice is between poverty in NYC or poverty in Detroit, you may be better off in NYC. If your choice is between poverty in Mexico and poverty in California, you may choose California.

But if your choice is between a good chance of being middle class in Salt Lake City verses a high chance of being poor and an extremely small chance of being rich in NYC, you are probably a lot better off packing your bags and heading to Utah.

But if cities are important drivers of innovation (especially in science, to which we owe thanks for things like electricity and refrigerated food shipping,) then Auerswald has already provided us with a potential solution to their runaway effects on the poor: Henry George’s land value tax. As George accounts, one day, while overlooking San Francisco:

I asked a passing teamster, for want of something better to say, what land was worth there. He pointed to some cows grazing so far off that they looked like mice, and said, “I don’t know exactly, but there is a man over there who will sell some land for a thousand dollars an acre.” Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.[28]

Alternatively, higher taxes on fortunes like Zuckerberg’s and Bezos’s might accomplish the same thing.

Book Club: Code Economy: Economics as Information Theory

If the suggestion… that the economy is “alive” seems fanciful or far-fetched, it is much less so if we consider the alternative: that it is dead.

Welcome back to your regularly scheduled discussion of Auerswald’s The Code Economy: a Forty-Thousand-Year History. Today we are discussing Chapter 9: Platforms, but feel free to jump into the discussion even if you haven’t read the book.

I loved this chapter.

We can safely answer that the economy–or the sum total of human provisioning, consumptive, productive and social activities–is neither “alive” nor, exactly, “non-living.”

The economy has a structure, yes. (So does a crystal.) It requires energy, like a plant, sponge, or macaque. It creates waste. But like a beehive, it is not “conscious;” voters struggle to make any kind of coherent policies.

Can economies reproduce themselves, like a beehive sending out swarms to found new hives? Yes, though it is difficult in a world where most of the sensible human niches have already been filled.

Auerswald notes that his use of the word “code” throughout the book is not (just) because of its modern sense in the coding of computer programs, but because of its use in the structure of DNA–we are literally built from the instructions in our genetic “code,” and society is, on top of that, layers and layers of more code, for everything from “how to raise vegetables” to “how to build an iPhone” to, yes, Angry Birds.

Indeed, as I have insisted throughout, this is more than an analogy: the introduction of production recipes into economics is… exactly what the introduction of DNA i to molecular biology. it is the essential first step toward a transformation of economics into a branch of information theory.

I don’t have much to say about information theory because I haven’t studied information theory, beyond once reading a problem about a couple of people named Alice and Bob who were trying to send messages to each other, but I did read Viktor Mayer-Schönberger and, Kenneth Cukier‘s Big Data: A revolution that will transform how we live, work, and think a couple of weeks ago. It doesn’t rise to the level of “OMG this was great you must read it,” but if you’re interested in the subject, it’s a good introduction and pairs nicely with The Code Economy, as many of the developments in “big data” are relevant to recent developments in code. It’s also helpful in understanding why on earth anyone sees anything of value in companies like Facebook and LinkedIn, which will be coming up soon.

You know, we know that bees live in a hive, but do bees know? (No, not for any meaningful definition of “knowing.”) But imagine being a bee, and slowly working out that you live in a hive, and that the hive “behaves” in certain ways that you can model, just like you can model the behavior of an individual bee…


Economics has a lot to say about how to optimize the level of inputs to get output, but what about the actual process of tuning inputs into outputs? … In the Wonderful World of Widgets that i standard economic, ingredients combine to make a final product, but the recipe by which the ingredients actually become the product is nowhere explicitly represented.

After some papers on the NK model and th shift in organizational demands from pre-industrial economic production to post-industrial large-scale production by mega firms, (or in the case of communism, by whole states,) Auerswald concludes that

…the economy manages increasing complexity by “hard-wiring” solution into standards, which in turn define platforms.

Original Morse Telegraph machine, circa 1835

This is an important insight. Electricity was once a new technology, whose mathematical rules were explored by cutting-edge scientists. Electrical appliances and the grid to deliver the electricity they run on were developed by folks like Edison and Tesla.

But today, the electrical grid reaches nearly every house in America. You don’t have to understand electricity at all to plug in your toaster. You don’t have to be Thomas Edison to lay electrical lines. You just have to follow instructions.

Electricity + electrical appliances replaced many jobs people used to do, like candle making or pony express delivery man, but electricity has not resulted in an overall loss of jobs. Rather, far more jobs now exist that depend on the electrical grid (or “platform”) than were eliminated.

(However, one of the difficulties or problems with codifying things into platforms is then, systems have difficulty handling other, perfectly valid methods of doing things. Early codification may lock-in certain ways of doing things that are actually suboptimal, like how our computer keyboard layout is intentionally difficult to use not because of anything to do with computers, but because typewriters in the 1800s jammed if people typed on them too quickly. Today, we would be better off with a more sensible keyboard layout, but the old one persists because too many systems use it.)

The Industrial Revolution was a time of first technological development, and then encoding into platforms of many varieties–transportation networks of water and rail; electrical, sewer, and fresh water grids; the large-scale production of antibiotics and vaccines; and even the codification of governments.

The English, a nation of a couple thousand years or so, are governed under a system known as “Common Law,” which is just all of the legal precedents and traditions built up over that time that have come into customary use.

When America was founded, it didn’t have a thousand years of experience to draw on because, well, it had just been founded, but it did have a thousand years of cultural memory of England’s government. English Common Law was codified as the base of the American legal system.

The Articles of Confederation, famous only for not working very well, were the fledgling country’s first attempt at codifying how the government should operate. They are typically described as failing because they allocated insufficient power to the federal government, but I propose a more nuanced take: the Articles laid out insufficient code for dealing with nation-level problems. The Constitution solved these problems and instituted the basic “platform” on which the rest of the government is built. Today, whether we want to ratify a treaty or change the speed limit on i-405, we don’t have to re-derive the entire decisions-making structure from scratch; legitimacy (for better or for worse) is already built into the system.

Since the days of the American and French revolutions, new countries have typically had “constitutions,” not because Common Law is bad, but because there is no need to re-derive from scratch successful governing platforms–they can just be copied from other countries, just as one firm can copy another firms organizational structure.

Continuing with Auerswald and the march of time:

Ask yourself what the greatest inventions were over the past 150 years: Penicillin? The transistor? Electrical power? Each of these has been transformative, but equally compelling candidates include universal time, container shipping, the TCP/IP protocols underlying the Internet, and the GSM and CDMA standards that underlie mobile telephony. These are the technologies that make global trade possible by making code developed in one place interoperable with code developed in another. Standards reduce barriers among people…

Auerswald, as a code enthusiast, doesn’t devote much space to the downsides of code. Clearly, code can make life easier, by reducing the number of cognitive tasks required to get a job done. Let’s take the matter of household management. If a husband and wife both adhere to “traditional gender norms,” such as an expectation that the wife will take care of internal household chores like cooking and vacuuming, and the husband will take care of external chores, like mowing the lawn, taking out the trash, and pumping gas, neither spouse has to ever discuss “who is going to do this job” or wonder “hey, did that job get done?”

Following an established code thus aids efficiency and appears to decrease marital stress (there are studies on this,) but this does not mean that the code itself is optimal. Perhaps men make better dish-washers than women. Or for a couple with a disabled partner, perhaps all of the jobs would be better performed by reversing the roles.

Technological change also encourages code change:

The replacement of manual push-mowers with gas-powered mowers makes mowing the lawn easier for women, so perhaps this task would be better performed by housewives. (Even the Amish have adopted milking machines on the grounds that by pumping the milk away from the cow for you, the machines enable women to participate equally in the milking–a task that previously involved picking up and carrying around 90 lb milkjugs.)

But re-writing the entire code is work and involves a learning curve as both parties sort out and get used to new expectations. (See my previous thread on “emotional labor” and its relation to gender norms.) So even if you think the old code isn’t fair or optimal, it still might be easier than trying to make a new code–and this extends to far more human relations than just marriage.

And then you get cases where the new technology is incompatible with the old code. Take, for example, the relationship between transportation, weights and measures, and the French Revolution.

A country in which there is no efficient way to get from Point A to Point B has no need for a standardized set of weights and measures, as people in Community A will never encounter or contend with whatever system they are using over in Community B. Even if a king wanted to create a standard system, he would have difficulty enforcing it. Instead, each community tends to evolve a system that works well for its own needs. A community that grows bananas, for example, will come up with measures suitable to bananas, like the “bunch,” a community that deals in grain will invent the “bushel,” and a community that enumerates no goods, like the Piraha, will not bother with large quantities quantities.

(Diamonds are measured in “carats,” which have nothing to do with the orange vegetable, but instead are derived from the seeds of the carob tree, which apparently are small enough to be weighed against small stones.)

Since the French paid taxes, there was some demand for standardized weights and measures within each province–if your taxes are “one bushel of grain,” you want to make sure “bushel” is well defined so the local lord doesn’t suddenly define this year’s bushel as twice as big as last year’s bushel–and likewise, the lord doesn’t want this year’s bushel to be defined as half the size as last year’s.

But as roads improved and trade increased, people became concerned with making sure that a bushel of grain sold in Paris was the same as a bushel purchased in Nice, or that 5 carats of diamonds in Bordeaux was still 5 carats when you reached Cognac.

But the established local power of the local nobility made it very hard to change whatever measures people were using in each individual place. That is, the existing code made it hard to change to a more efficient code, probably because local lords were concerned the new measures would result in fewer taxes, and the local peasants concerned it would result in higher taxes.

Thus it was only with the decapitation of the Ancien Regime and wiping away of the privileges and prerogatives of the nobility that Revolutionary France established, as one of its few lasting reforms, a universal system of weights and measures that has come down to us today as the metric or SI system.

Now, speaking as an American who has been trained in both Metric and Imperial units, using multiple systems can be annoying, but is rarely deadly. On the scale of sub-optimal ideas, humans have invented far worse.

Quoting Richard Rhodes, The Making of the Atomic Bomb:

“The end result of the complex organization that was the efficient software of the Great War was the manufacture of corpses.

This essentially industrial operation was fantasized by the generals as a “strategy of attrition.” The British tried to kill Germans, the Germans tried to kill British and French and so on, a “strategy” so familiar by now that it almost sounds normal. It was not normal in Europe before 1914 and no one in authority expected it to evolve, despite the pioneering lessons of the American Civil War. Once the trenches were in place, the long grave already dug (John Masefield’s bitterly ironic phrase), then the war stalemated and death-making overwhelmed any rational response.

“The war machine,” concludes Elliot, “rooted in law, organization, production, movement, science, technical ingenuity, with its product of six thousand deaths a day over a period of 1,500 days, was the permanent and realistic factor, impervious to fantasy, only slightly altered by human variation.”

No human institution, Elliot stresses, was sufficiently strong to resist the death machine. A new mechanism, the tank, ended the stalemate.”

Russian Troops waiting for death

On the Eastern Front, the Death Machine was defeated by the Russian Revolution, as the canon fodder decided it didn’t want to be turned into corpses anymore.

I find World War I more interesting than WWII because it makes far less sense. The combatants in WWII had something resembling sensible goals, some chance of achieving their goals, and attempted to protect the lives of their own people. WWI, by contrast, has no such underlying logic, yet it happened anyway–proof that seemingly logical people can engage in the ultimate illogic, even as it reduces whole countries to nothing but death machines.

Why did some countries revolt against the cruel code of war, and others not? Perhaps an important factor is the perceived legitimacy of the government itself (though regular food shipments are probably just as critical.) Getting back to information theory, democracy itself is a kind of blockchain for establishing political legitimacy, (more on this in a couple of chapters) which may account for why some countries perceived their leadership as more legitimate, and other countries suddenly discovered, as information about other people’s opinions became easier to obtain, that the government enjoyed very little legitimacy.

But I am speculating, and have gotten totally off-topic (Auerswald was just discussing the establishment of TCP/IP protocols and other similar standards that aid international trade, not WWI!)

Returning to Auerswald, he cites a brilliant quote from Alfred North Whitehead

“Civilization advances by extending the number of operations we can perform without thinking about them.”

As we were saying, while sub-optimal (or suicidal) code can and does destroy human societies, good code can substantially increase human well-being.

The discovery and refinement of new inventions, technologies, production recipes, etc., involves a steep learning curve as people first figure out how to make the thing work and to source and put together all of the parts necessary to build it, (eg, the invention of the automobile in the late 1800s and early 1900s,) but once the technology spreads, it simply becomes part of the expected infrastructure of everyday life (eg, the building of interstate highways and gas stations, allowing people to drive cars all around the US,) a “platform” on which other, future innovations build. Post-1950, most automobile-driven innovation was located not in refinements to the engines or brakes, but in things you can do with vehicles, like long-distance shipping.

Interesting things happen to income as code becomes platforms, but I haven’t worked out all of the details.

Continuing with Auerswald:

Note that, in code economics, a given country’s level of “development” is not sensibly measured by the total monetary value of all goods and services it produces…. Rather, the development of a country consists of … it capacity to execute more complex code. …

Less-developed countries that lack the code to produce complex products will import them, and they will export simpler intermediate products and raw materials in order to pay for the required imports.

By creating and adhering to widely-observed “standards,” increasing numbers of countries (and people) are able to share inventions, code, and development.

Of the drivers of beneficial trade, international standards are at once among the most important and the least appreciated. … From the invention of bills of exchange in the Middle Ages … to the creation of twenty-first-century communications protocols, innovations in standards have lowered the cost and enhanced the value of exchange across distance. …

For entrepreneurs in developing countries, demonstrated conformity with international standards… is a universally recognized mark of organizational capacity that substantially eases entry into global production and distribution networks.

In other words, you are more likely to order steel from a foreign factory if you have some confidence that you will actually receive the variety you ordered, and the factory can signal that it knows what it is doing and will actually deliver the specified steel by adhering to international standards.

On the other hand, I think this can degenerate into a reliance on the appearance of doing things properly, which partially explains the Elizabeth Holmes affair. Holmes sounded like she knew what she was doing–she knew how to sound like she was running a successful startup because she’d been raised in the Silicon Valley startup culture. Meanwhile, the people investing in Holmes’s business didn’t know anything about blood testing (Holmes’s supposed invention tested blood)–they could only judge whether the company sounded like it was a real business.

Auerswald then has a fascinating section comparing each subsequent “platform” that builds on the previous “platform” to trophic levels in the environment. The development of each level allows for the development of another, more complex level above it–the top platform becomes the space where newest code is developed.

If goods and services are built on platforms, one atop the other, then it follows that learning at higher levels of the system should be faster than learning at lower levels, for the simple reason that leaning at higher levels benefits from incremental learning all the way down.

There are two “layers” of learning. Raw material extraction shows high volatility around a a gradually increasing trend, aka slow learning. By contrast, the delivery of services over existing infrastructure, like roads or wireless networks, show exponential growth, aka fast learning.

In other words, the more levels of code you already have established and standardized into platforms, the faster learning goes–the basic idea behind the singularity.


That’s all for today. See you next week!

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.


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.


…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!

Book Club: The Industrial Revolution and its Discontents, Code Economy, ch. 5

1. The Industrial Revolution and its consequences have been a disaster for the human race. They have greatly increased the life-expectancy of those of us who live in “advanced” countries, but they have destabilized society, have made life unfulfilling, have subjected human beings to indignities, have led to widespread psychological suffering (in the Third World to physical suffering as well) and have inflicted severe damage on the natural world. The continued development of technology will worsen the situation. It will certainly subject human beings to greater indignities and inflict greater damage on the natural world, it will probably lead to greater social disruption and psychological suffering, and it may lead to increased physical suffering even in “advanced” countries. –Kaczynski, Industrial Society and Its Future

The quest to find and keep a “job for life”–stable, predictable work that pays enough to live on, is reachable by available transportation, and lends a sense of meaning to their daily lives–runs though every interview transcript, from those who are unemployed to those who have “made it” to steady jobs like firefighting or nursing. Traditional blue-collar work–whether as a factory worker or a police officer–has become increasingly scarce and competitive, destroyed by a technologically advanced and global capitalism that prioritizes labor market “flexibility”… Consequently, the post-industrial generation is forced to continuously grapple with flux and contingency, bending and adapting to the demands of they labor market until they feel that they are about to break. –Silva, Coming up Short: Working-Class Adulthood in an Age of Uncertainty

The historical record confirms that the realities of the ongoing processes of mechanization and industrialization, as noted early on by Lord Byron, were very different from the picture adherents to the wage fund theory held in their heads. While the long-term impact of the Industrial revolution had on the health and well-being of the English population was strongly positive, the first half of the nineteenth century was indeed a time of exceptional hardship for English workers. In a study covering the years 1770-1815, Stephen Nicholas and Richard Steckel report “falling heights of urban-and rural-born males after 1780 and a delayed growth spurt for 13- to 23-year old boys,” as well as a fall in the English workers’ height relative to that of Irish convicts. By the 1830s, the life expectancy of anyone born in Liverpool and Manchester was less than 30 years–as low as had been experienced in England since the Black Death of 1348. –Auerswald, The Code Economy

On the other hand:

Chapter 5 of The Code Economy, Substitution, explores the development of economic theories about the effects of industrialization and general attempts at improving the lives of the working poor.

… John Barton, a Quaker, published a pamphlet in 1817 titled, Observations on the Circumstances Which Influence the Condition of the Laboring Classes of Society. … Barton began by targeting the Malthusian assumption that population grows in response to increasing wages. … He began by noting that there was no a priori reason to believe that labor and capital were perfect complements, as classical economists implicitly assumed. The more sensible assumption was that, as wages increased, manufacturers and farmers alike would tend to substitute animals or machines for human labor. Rather than increasing the birth rate, the higher wages brought on by the introduction of new machinery would increase intergenerational differences in income and thus delay child-bearing. Contrary to the Malthusian line of argument, this is exactly what happened.

There’s an end note that expands on this (you do read the end notes, right?) Quoting Barton, 1817:

A rise of wages then does not always increase population… For every rise of wages tends to decrease the effectual demand for labor… Suppose that by a general agreement among farmers the rate of agricultural wage were raised from 12 shillings to 24 shillings per week–I cannot imagine any circumstance calculated more effectually to discourage marriage. For it would immediately become a a most important object to cultivate with as few hands as possible; wherever the use of machinery, or employment of horses could be substituted for manual labor, it would be done; and a considerable portion of existing laborers would be out of work.

This is the “raising the minimum wage will put people out of work” theory. Barton also points out that when people do manage to get these higher-paid jobs, they will tend to be older, more experienced laborers rather than young folks looking to marry and start a family.

A quick perusal of minimum wage vs. unemployment rate graphs reveals some that are good evidence against minimum wages, and some that are good evidence in favor of them. Here’s a link to a study that found no effects of minimum wage differences on employment. The American minimum wage data is confounded by things like “DC is a shithole.” DC has the highest minimum wage in the country and the highest unemployment rate, but Hawaii also has a very high minimum wage and the lowest unemployment rate. In general, local minimum wages probably reflect local cost of living/cost of living reflects wages. If we adjust for inflation, minimum wage in the US peaked around 1968 and was generally high throughout the 60s and 70s, but has fallen since then. Based on conversations with my parents, I gather the 60s and 70s were a good time to be a worker, when unskilled labor could pretty easily get a job and support a family; unemployment rates do not seem to have fallen markedly since then, despite lower real wages. A quick glance at a map of minimum wages by country reveals that countries with higher minimum wage tend to be nicer countries that people actually want to live in, but the relationship is not absolute.

We might say that this contradicts Barton, but why have American wages stagnated or gone down since the 60s?

1. Automation

2. Emergence of other economic competitors as Europe and Japan recovered from WWII

3. Related: Outsourcing to cheaper workers in China

4. Labor market growth due to entry of women, immigrants, and Boomers generally

Except for 2, that sounds a lot like what Barton said would happen. Wages go up => people move where the good jobs are => labor market expands => wages go down. If labor cannot move, then capitalists can either move the businesses to the labor or invest in machines to replace the labor.

On the other hand, the standard of living is clearly higher today than it was in 1900, even if wages, like molecules diffusing through the air, tend to even out over time. Why?

First, obviously, we learned to extract more energy from sources like oil, coal, and nuclei. A loom hooked up (via the electrical grid) to an electric turbine can make a lot more cloth per hour than a mere human working with shuttles and thread.

Second, we have gotten better at using the energy we extract–Auerswald would call this “code.”

Standards of living may thus have more to do with available resources (including energy) and our ability to use those resources (both the ‘code” we have developed and our own inherent ability to interact with and use that code,) than with the head-scratching entropy of minimum wages.

Auerswald discusses the evolution of David Ricardo’s economic ideas:

By incorporating the potential for substitution between capital and labor, Ricardo led the field of economics in rejecting the wage fund theory, along with its Dickensian implications for policy. He accepted the notion the introduction of new machinery would result in the displacement of workers. The upshot was that the workers were still assumed to be doomed, but the reason was now substitution of machines for labor, not scarcity of a Malthusian variety.

Enter Henry George, with a radically different perspective:

“Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.” …

George asserted that increasing population density, (not, as Malthus claimed, population decline) was the source of increased prosperity in human societies: “Wealth is greatest where population is densest… the production of wealth to a given amount of labor increases as population increases.”  The frequent interactions among people in densely populated cities accelerates the emergence and evolution of code. However, while population growth and increased density naturally bring increased prosperity, they also, just as naturally, bring increasing inequality and poverty. Why? Because the fruits of labor are inevitably gathered by the owners of land.

In other words, increasing wages => increasing rents and the workers are right back where they started while the landlords are sitting pretty.

In sharp contrast with Karl Marx, … George stated that “the antagonism of interest is not between labor and capital… but is in reality between labor and capital on the one side and the land ownership on the other.” The implication of his analysis was as simple as it was powerful: to avoid concentrating wealth in the hands of the few, it was the government’s responsibility to eliminate all taxes on capital and laborers, the productive elements of the economy, and to replace those taxes with a single tax on land.

Note: not a flat tax on land, but a tax relative to the land’s sale value.

I was glad to see Henry George in the book because I enjoy George’s theories and they are under-discussed, especially relative to Marxism. You will find massive online communities of Marxists despite the absolute evidence that Marxism is a death machine, but relatively few enthusiastic Georgists. One of the things I rather appreciate about Georgism is its simplicity; the complication of the tax code is its own, additional burden on capitalists and workers alike. Almost any simplified tax code, no matter how “unfair,” would probably improve maters a great deal.

But there’s more, because this is a dense chapter. Auerswald notes that the increasing complexity of code (ie, productivity) has lead to steadily increasing standards of living over the past two centuries, at least after the Industrial Revolution’s initial cataclysm.

Quoting economist Paul Douglas, some years later:

“The increased use of mechanical appliances in offices has tended to lower the skill required. An old-fashioned bookkeeper, for instance, had to write a good hand, he had to be able to multiply and divide with absolute accuracy. Today his place is taken by a girl who  operates a book-keeping machine, and it has taken her a few weeks at mot to become a skilled bookkeeper.” In other words, the introduction of machinery displaced skilled workers for the very same reason it enhanced human capabilities: it allowed a worker with relatively rudimentary training to perform tasks that previously required a skilled worker.

…”Another way of looking at it, is this: Where formerly the skill used in bookkeeping was exercised by the bookkeeper, today that skill is exercised by the factory employees who utilize it to manufacture a machine which can do the job of keeping books, when operated by someone of skill far below that of the former bookkeeper. And because of this transfer of skill form the office to the factory, the rewards of skill are likewise transferred to the wage-earner at the plant.”

This is a vitally important pint… The essence of this insight is that introducing more powerful machines into the workplace does more than simply encode  into the machine the skills or capabilities that previously resided only in humans; it also shifts the burden of skill from one domain of work to another. … A comparable shift in recent decades has been from the skill of manufacturing computing machines (think IBM or Dell in their heydays) to that of creating improved instructions for computing machines’ the result has been a relative growth in programmers’ wages. The underlying process is the same. Improvements in technology will predictably reduce demand for the skills held by some workers, but they also will enhance the capabilities of other workers and shift the requirements of skill from one domain of work to the other.”

The problem with this is that the average person puts in 15-20 years of schooling (plus $$$) in order to become skilled at a job, only to suddenly have that job disappear due to accelerating technological change/improvement, and then some asshole one comes and tells them they should just “learn to code” spend another two to four years unemployed and paying for the privilege of learning another job and don’t see how fucking dispiriting this is to the already struggling.

The struggle for society is recognizing that even as standards of living may be generally rising, some people may absolutely be struggling with an economic system that offers much less certainty and stability than our ancestors enjoyed.

A final word from Auerswald:

… work divides or “bifurcates” as code advances in a predictable and repeatable way. The bifurcation of work in a critical mechanism by which the advance of code yields improvements in human well-being at the same time as it increases human reliance on code.

Which came first, the City or the Code? (book club Code Economy Ch 2)

The Code of Hammurabi

Writing, which is itself a form of code, enable humans to communicate code. Cities grow as code evolves. –Auerswald

Welcome to The Code Economy: A Forty-Thousand Year History, by Philip E. Auerswald. Chapter Two: Code looks at two epochal developments in human history: writing and cities.

One of the earliest pieces of writing we have uncovered is the Sumerian Hymn to Ninkasi, Goddess of Beer, which contains, yes, a recipe for making beer (translation by Miguel Civil):

Your father is Enki, Lord Nidimmud,
Your mother is Ninti, the queen of the sacred lake.
Ninkasi, your father is Enki, Lord Nidimmud,
Your mother is Ninti, the queen of the sacred lake.

You are the one who handles the dough [and] with a big shovel,
Mixing in a pit, the bappir with sweet aromatics,
Ninkasi, you are the one who handles the dough [and] with a big shovel,
Mixing in a pit, the bappir with [date] – honey,

You are the one who bakes the bappir in the big oven,
Puts in order the piles of hulled grains,
Ninkasi, you are the one who bakes the bappir in the big oven,
Puts in order the piles of hulled grains,

You are the one who waters the malt set on the ground,
The noble dogs keep away even the potentates,
Ninkasi, you are the one who waters the malt set on the ground,
The noble dogs keep away even the potentates,

You are the one who soaks the malt in a jar,
The waves rise, the waves fall.
Ninkasi, you are the one who soaks the malt in a jar,
The waves rise, the waves fall.

You are the one who spreads the cooked mash on large reed mats,
Coolness overcomes,
Ninkasi, you are the one who spreads the cooked mash on large reed mats,
Coolness overcomes,

You are the one who holds with both hands the great sweet wort,
Brewing [it] with honey [and] wine
(You the sweet wort to the vessel)
Ninkasi, (…)(You the sweet wort to the vessel)

The filtering vat, which makes a pleasant sound,
You place appropriately on a large collector vat.
Ninkasi, the filtering vat, which makes a pleasant sound,
You place appropriately on a large collector vat.

When you pour out the filtered beer of the collector vat,
It is [like] the onrush of Tigris and Euphrates.
Ninkasi, you are the one who pours out the filtered beer of the collector vat,
It is [like] the onrush of Tigris and Euphrates.

Sumerian tablet recording the allocation of beer

You guys requested beer or wine with your books, so here you go.

The hymn contains two layers of code–first, there is the code which allows each symbol or character to stand for a particular sound, which let the author write down the recipe and you, thousands of years later, decode and read the recipe; and second, there is the recipe itself, a code for producing beer.

The recipe’s code likely far predates the hymn itself, as humans had begun brewing beer at least a couple thousand years earlier.

Writing and cities go hand in hand; it is difficult to imagine managing the day-to-day need to import food (and water) for thousands of people without some ability to encode information. As cities grow larger, complexity grows: one man in the woods may relieve himself behind a tree; thousands of people packed into a square mile cannot.

Each solved problem, once routinized, becomes its own layer of code, building up as the city itself expands; a city of thousands or millions of people cannot solve each person’s problems anew each day.

Gobekli Tepe, Turkey

But which came first, the city or the alphabet? Did the growth of cities spur innovations that improved agricultural output, or did agricultural innovations spur the growth of cities?

For example, settlement and construction appear to have gotten underway at Jericho (one of the world’s oldest inhabited cities) around 9 or 10,000 BC and at the mysterious Gobekli Tepe site began around 7-9,000 BC, before agriculture emerged in the region.

Writing developed a fair bit later, developing from clay shapes to shapes impressed in clay between 8,000 and 4,000 BC.

Amphitheater, Norte Chico, Peru

Others of the world’s earliest civilizations had either no or very little writing. The Norte Chico civilization of Peru, for example; by the time the Spaniards arrived, the Inca had an accounting system based on the quipu, a kind of string abacus, but appear to have not yet developed a true writing system, despite their palaces, cities, roads, emperor, and tax collectors. (Here is my previous post on Norte Chico.)

The Great Bath of Mohenjo-daro

The extensive Indus Valley civilization had some form of symbolic encoding, but few of their inscriptions are longer than 4 or 5 characters–the longest inscription found so far is 26 symbols, spread over three different sides of an object. Not exactly an epic–but the Indus Valley Civilization was nevertheless quite large and impressive, supporting perhaps 5 million people. (Previous post on the Indus Valley.)

Auerswald documents some of the ways cities appear to drive innovation–and to “live”:

The Santa Fe team found that cities are like biological organisms when it comes to “metabolic” urban processes that are analogous to nutrient supply and waste removal–transportation, for example, ha a branching structure much like veins or bronchi–but that cities differ fundamentally from biological organisms when it comes to indicators reflecting the creation and transmission of code. measuring the size of cities based on population and on the urban “metabolism” using metrics such as wages, GDP, electric power and gasoline consumption, and total  road surface, the team found a systematic relationship between city size and indicators of the supply of “nutrients” and waste removal… However, while metabolic indicators do not keep pace with the size of cities as they grow, indicators relating to the creation and transmission of code increase at a greater rate than city size. … In short, the creation of ideas accelerates with city growth, whereas the cost of new infrastructure is minimized.

This intriguing macro-level departure from the inverse relationships that hold for organisms ends up risking more questions about the evolution of cities than it answers: What mechanism enables larger cities to produce disproportionately more innovation and wealth than smaller cities?

Data Economy has a fascinating article in a similar vein: Street Smarts: The Rise of the Learning City:

The city as a brain

An amalgam of terms that have been used for parallel conceptions of the Smart City among them cyberville, digital city, electronic communities, flexicity, information city, intelligent city, knowledge-based city, MESH city, telecity, teletopia, ubiquitous city, wired city.

However the one I would like to propose, with population movement in mind, is The Learning City.

The term is based on a combination of two theories The Ego City and The Flynn Effect.

In 2009 Neurobiologist Mark Changizi from the Rensselaer Polytechnic Institute released a paper entitled Ego City: Cities Are Organized Like Human Brain.

Changizi sees strikingly real similarities between the brain and a city.

The central idea being that they organise and evolve similarly due to the need for efficiency.

As brains grow more complex from one species to the next, they change in structure and organisation in order to achieve the right level of reciprocity.

This is analogous to the widening of streets in cities.

The research team found mutual “scaling laws” for brains and cities.

For example, as the surface area of a brain or city grows, the number of connectors (neurons or highways) increased at a similar rate for each.

Likewise, a bigger city needs more highway exits in the same proportion as a bigger brain needs more synapses connecting neurons.

“The brain is like a city.

Cities develop and grow bigger and may get problems with roads and infrastructure, which is similar to what happens to our brains when we get older”, notes Håkan Fischer, Professor of Biological Psychology at the Department of Psychology at Stockholm University.

 The learning city

This is curious when taken in the context of The Flynn Effect.

Intelligence Researcher James Flynn found that every decade without fail the human population scored higher on IQ tests.

An average increase of 3 points per decade.

His thesis suggests that the more information we as humans have to absorb and compute leads to an increase in IQ.

In this instance the increased information is data collected within the city.

As cities gain more data they adapt and in turn get smarter.

Human brains faced with a busier world filled with more information brings about an increase in IQ from generation to generation.

As people migrant to cities creating a more complex environment for the city it to must gather this data, learn and raise its Smart City IQ.

This is The Learning City.

On the other hand, the data Auerswald cites–from the “Santa Fe Team”–only looks at cities from the US, China, the EU, and Germany. How would this data look if it incorporated other megacities, like Manila, Philippines (the world’s densest city); Sao Paolo, Brazil; Bombay, India; Caracas, Venezuela; Karachi, Pakistan; or Jakarta, Indonesia? Of the world’s ten biggest cities, only two–Seoul, #1, and Tokyo, #10–are in the first world. (#9 Shanghai, is well on its way.)

#2 Sao Paolo might be more energy efficient than villages in the Brazilian hinterland (or it may not, as such towns may not even have electricity,) but does it produce more innovation than #11 New York City? (No American city made the top 10 by population.)

If cities are drivers of innovation, why are so many of the biggest in the third world? Perhaps third world countries offer their citizens so little that they experience a form of extreme brain drain, with everyone who can fleeing to the most productive regions. Or perhaps these cities are simply on their way–in a century, maybe Sao Paolo will be the world’s next Shanghai.

The city, by definition, is civilization–but does the city itself spur innovation? And are cities, themselves, living things?

Geoffrey West has some interesting things to say on this theme:

“How come it is very hard to kill a city? You can drop an atom bomb on a city, and 30 years later, it’s surviving.”

Here’s a transcript of the talk.

Hiroshima in 1945:

Hiroshima today:

Hiroshima montage

Detroit, 1905:

Belle Isle Park, Detroit, 1905–h/t Photos of Detroit’s Golden Age

Detroit today:

Detroit Book Depository

“Bombs don’t destroy cities; people destroy cities.”

Book Club: The Code Economy ch. 1

Greetings! Grab a cup of coffee and pull up a chair. Tea is also good. Today we’re diving into chapter one of Philip Auerswald’s The Code Economy, “Jobs: Divide and Coordinate.”

I wish this chapter had been much longer; we speed through almost 2.5 million years of cognitive evolution in a couple of pages.

The earliest hominins had about the same short-term memory as a modern-day chimpanzee, which is to say they could keep track of only two operations at a time. … Our methods for creating tools gradually became more sophisticated, until we were using the tools we created to produce other tools in a repetitive and predictable manner. These processes for creating stone tools were among humanity’s first production algorithms-that is, the earliest code. They appeared almost simultaneously in human communities in most part of the world around 40,000 BC.


…[E.O.] Wilson refers to this phenomenon more broadly as the discovery of eusocial behavior… Wilson situates the date far earlier in human history than I do here. I chose 50,000 years [ago] because my focus is on the economy. it is clear that an epochal change in society occurred roughly 10,000 years BCE, when humans invented agriculture in six parts of the world simultaneously. The fact of this simultaneity directly suggests the advance of code represented by the invention of agriculture was part of a forward movement of code that started much earlier.

What do you think? Does the simultaneous advent of behavioral modernity–or eusociality–in far-flung human groups roughly 50,000 years ago, followed by the simultaneous advent of agriculture in several far-flung groups about 10,000 years ago speak to the existence of some universal, underlying process? Why did so many different groups of people develop similar patterns of life and technology around the same time, despite some of them being highly isolated? Was society simply inevitable?

The caption on the photo is similarly interesting:

Demand on Short-Term Working Memory in the Production of an Obsidian Axe [from Read and van der Leeuw, 2015] … We can relate the concepts invoked in the prodcution of stone tools to the number of dimensions involved and thereby to the size of short-term workign memory (STWM) required for the prodction of the kind of stone tools that exemplify each stage in hominin evolution. …

Just hitting the end of a pebble once to create one edge, as in the simplest tools, they calculate requires holding three items in the working memory. Removing several flakes to create a longer edge (a line), takes STWM 4; working an entire side takes STWM 5; and working both sides of the stone in preparation for knapping flakes from the third requires both an ability to think about the pebble’s shape in three dimensions and STWM 7.

(The Wikipedia article on Lithic Reduction has a lovely animation of the technique.)

It took about 2 million years to proceed from the simplest tools (working memory: 3) to the most complex (working memory: 7.) Since the Neolithic, our working memory hasn’t improved–most of us are still limited to a mere 7 items in our working memory, just enough to remember a phone number if you already know the area code.

All of our advances since the Neolithic, Auerswald argues, haven’t been due to an increase in STWM, but our ability to build complexity externally: through code. And it was this invention of code that really made society take off.

By about 10,000 BCE, humans had formed the first villages… Villages were the precursors of modern-day business firms in that they were durable association built around routines. … the advance of code at the village level through the creation of new technological combinations set into motion the evolution from simplicity to complexity that has resulted in the modern economy.

It was in the village, then, that code began to evolve.

What do you think? Are Read and van der Leeuw just retroactively fitting numbers 3-7 to the tools, or do they really show an advance in working memory? Is the village really the source of most code evolution? And who do you think is more correct, Herbert Spencer or Thomas Malthus?

Auerswald then forward to 1557, with the first use of the word “job” (spelled “jobbe,” most likely from “gobbe,” or lump.)

The advent of the “jobbe” a a lump of work was to the evolution of modern society something like what the first single-celled organism was to the evolution of life.


The “jobbe” contrasted with the obligation to perform labor continuously and without clearly defined roles–slavery, serfdom, indentured servitude, or even apprenticeship–as had been the norm throughout human history.

Did the Black Death help create the modern “job market” by inspiring Parliament to pass the Statute of Laborers?

I am reminded here of a passage from Gulick’s Evolution of the Japanese, Social and Psychic, (published in 1903):

The idea of making a bargain when two persons entered upon some particular piece of work, the one as employer, the other as employed, was entirely repugnant to the older generation, since it was assumed that their relations as inferior and superior should determine their financial relations; the superior would do what was right, and the inferior should accept what the superior might give without a question or a murmur. Among the samurai, where the arrangement is between equals, bargaining or making fixed and fast terms which will hold to the end, and which may be carried to the courts in case of differences, was a thing practically unknown in the older civilization. Everything of a business nature was left to honor, and was carried on in mutual confidence.

“A few illustrations of this spirit of confidence from my own experience may not be without interest. On first coming to Japan, I found it usual for a Japanese who wished to take a jinrikisha to call the runner and take the ride without making any bargain, giving him at the end what seemed right. And the men generally accepted the payment without question. I have found that recently, unless there is some definite understanding arrived at before the ride, there is apt to be some disagreement, the runner presuming on the hold he has, by virtue of work done, to get more than is customary. This is especially true in case the rider is a foreigner. Another set of examples in which astonishing simplicity and confidence were manifested was in the employment of evangelists. I have known several instances in which a full correspondence with an evangelist with regard to his employment was carried on, and the settlement finally concluded, and the man set to work without a word said about money matters. It need hardly be said that no foreigner took part in that correspondence. …

“This confidence and trustfulness were the product of a civilization resting on communalistic feudalism; the people were kept as children in dependence on their feudal lord; they had to accept what he said and did; they were accustomed to that order of things from the beginning and had no other thought; on the whole too, without doubt, they received regular and kindly treatment. Furthermore, there was no redress for the peasant in case of harshness; it was always the wise policy, therefore, for him to accept whatever was given without even the appearance of dissatisfaction. This spirit was connected with the dominance of the military class. Simple trustfulness was, therefore, chiefly that of the non-military classes.

“Since the overthrow of communal feudalism and the establishment of an individualistic social order, necessitating personal ownership of property, and the universal use of money, trustful confidence is rapidly passing away.

We still identify ourselves with our profession–“I am a doctor” or “I am a paleontologist”–but much less so than in the days when “Smith” wasn’t a name.

Auerswald progresses to the modern day:

In the past two hundred years, the complexity of human economic organization has  increased by orders of magnitude. Death rates began to fall rapidly in the middle of the nineteenth century, due to a combination of increased agricultural output, improved hygiene, and the beginning of better medical practices–all different dimensions of the advance of code…. Greater numbers of people living in greater density than ever before accelerated the advance of code.

Sounds great, but:

By the twentieth century, the continued advance of code necessitated the creation of government bureaucracies and large corporations that employed vast numbers of people. These organizations executed code of sufficient complexity that it was beyond the capacity of any single individual to master.

I’ve often wondered if the explosion of communist disasters at the beginning of the 20th century occurred because we could imagine a kind of nation-wide code for production and consumption and we had the power to implement it, but we didn’t actually have the capabilities and tools necessary to make it work.

We can imagine Utopia, but we cannot reach it.

Auerswald delineates two broad categories of “epochal change” as a result of the code-explosion of the past two centuries: First, our capabilities grew. Second:

“we have, to an increasing degree, ceded to other people–and to code itself–authority and autonomy, which for millennia we had kept unto ourselves and our immediate tribal groups as uncodified cultural norms.”

Before the “job”, before even the “trade,” people lived and worked far more at their own discretion. Hoeing fields or gathering yams might be long and tedious work, but at least you didn’t have to pee in a bottle because Amazon didn’t give you time for bathroom breaks.

Every time voters demand that politicians “bring back the jobs” or politicians promise to create them, we are implicitly stating that the vast majority of people are no longer capable of making their own jobs. (At least, not jobs that afford a modern lifestyle.) The Appalachians lived in utter poverty (the vast majority of people before 1900 lived in what we would now call utter poverty), but they did not depend on anyone else to create “jobs” for them; they cleared their own land, planted their own corn, hunted their own hogs, and provided for their own needs.

Today’s humans are (probably not less intelligent nor innately capable than the average Appalachian of 1900, but the economy (and our standards of living) are much more complex. The average person no longer has the capacity to drive job growth in such a complicated system, but the solution isn’t necessarily for everyone to become smarter. After all, large, complicated organizations need hundreds of employees who are not out founding their own companies.

But this, in turn, means all of those employees–and even the companies themselves–are dependent on forces far outside their control, like Chinese monetary policy or the American electoral cycle. And this, in turn, raises demand for some kind of centralized, planned system to protect the workers from economic hardship and ensure that everyone enjoys a minimum standard of living.

Microstates suggest themselves as a way to speed the evolution of economic code by increasing the total number of organisms in the ecosystem.

With eusociality, man already became a political (that is, polis) animal around 10,000 or 40,000 or perhaps 100,000 years ago, largely unable to subsist on his own, absent the tribe. We do not seem to regret this ill-remembered transition very much, but what about the current one? Is the job-man somehow less human, less complete than the tradesman? Do we feel that something essential to the human spirit has been lost in defining and routinizing our daily tasks down to the minute, forcing men to bend to the timetables of factories and international corporations? Or have we, through the benefits of civilization (mostly health improvements) gained something far richer?

Book Club: The Code [Robot] Economy (pt. 2)

Welcome to EvX’s book club. Today we’re discussing Philip Auerswald’s The Code Economy, Introduction.

I’ve been discussing the robot economy for years (though not necessarily via the blog.) What happens when robots take over most of the productive jobs? Most humans were once involved in directly producing the necessities of human life–food, clothing, and shelter, but mostly food. Today, machines have eliminated most food and garment production jobs. One tractor easily plows many more acres in a day than a horse or mule team did in the 1800s, allowing one man to produce as much food as dozens (or hundreds) once did.

What happened to those ex-farmers? Most of us are employed in new professions that didn’t exist (eg, computer specialist) or barely existed (health care), but there are always those who can’t find employment–and unemployment isn’t evenly distributed.

Black unemployment rate

Since 1948, the overall employment rate has rarely exceeded 7.5%; the rate for whites has been slightly lower. By contrast, the black unemployment rate has rarely dipped below 10% (since 1972, the best data I have.) The black unemployment rate has only gone below 7.5 three times–for one month in 1999, one month in 2000, and since mid-2017. 6.6% in April, 2018 is the all-time low for black unemployment. (The white record, 3.0%, was set in the ’60s.)

(As Auerswald points out, “unemployment” was a virtually unknown concept in the Medieval economy, where social station automatically dictated most people’s jobs for life.)

Now I know the books are cooked and “unemployment” figures are kept artificially low by shunting many of the unemployed into the ranks of the officially “disabled,” who aren’t counted in the statistics, but no matter how you count the numbers, blacks struggle to find jobs at the same rates as whites–a problem they didn’t face in the pre-industrial, agricultural economy (though that economy caused suffering in its own way.)

A quick glance at measures of black and white educational attainment explains most of the employment gap–blacks graduate from school at lower rates, are less likely to earn a college degree, and overall have worse SAT/ACT scores. In an increasingly “post-industrial,” knowledge-based economy where most unskilled labor can be performed by robots, what happens to unskilled humans?

What happens when all of the McDonald’s employees have been replaced by robots and computers? When even the advice given by lawyers and accountants can be more cheaply delivered by an app on your smartphone? What if society, eventually, doesn’t need humans to perform most jobs?

Will most people simply be unemployed, ruled over by the robot-owning elite and the lucky few who program the robots? Will new forms of work we haven’t even begun to dream of emerge? Will we adopt some form of universal basic income, or descend into neo-feudalism? Will we have a permanent underclass of people with no hope of success in the current economy, either despairing at their inability to live successful lives or living slothfully off the efforts of others?

Here lies the crux of Auerswald’s thesis. He provides four possible arguments for how the “advance of code” (ie, the accumulation of technological knowledge and innovation,) could turn out for humans.

The Rifkin View:

  1. The power of code is growing at an exponential rate.
  2. Code nearly perfectly substitutes for human capabilities.
  3. Therefore the (relative) power of human capabilities is shrinking at an exponential rate.

If so, we should be deeply worried.

The Kurzweil View:

  1. The power of code is growing at an exponential rate.
  2. Code nearly perfectly complements human capabilities.
  3. Therefore the (absolute) power of human capabilities is growing at an exponential rate.

If so, we may look forward to the cyborg singularity

The Auerswald View:

  1. The power of code is growing at an exponential rate [at least we all agree on something.]
  2. Code only partially substitutes for human capabilities.
  3. Therefore the (relative) power of human capabilities is shrinking at an exponential rate in those categories of work that can be performed by computers, but not in others.

Auerswald notes:

In other words, where Kurzweil talks about an impeding code-induced Singularity, the reality looks much more like one code-induced bifurcation–the division of labor between humans and machines–after another.

The answer to the question, “Is there anything that humans can do better than digital computers?” turns out to be fairly simple: humans are better at being human.


1. Creating and improving code is a key part of what we human beings do. It’s how we invent the future by building on the past.

2. The evolution of the economy is driven by the advance of code. Understanding this advance is therefore fundamental to economics, and to much of human history.

3. When we create and advance code we don’t just invent new toys, we produce new forms of meaning, new experiences, and new ways of making our way in the world.

What do you think?

Come read “The Code Economy: A 40,00 Year History” with us

I don’t think the publishers got their money’s worth on cover design

EvX’s Book Club is reading Philip Auerswald’s The Code Economy: A 40,000 Year History looks at how everything humans produce, from stone tools to cities to cryptocurrencies like bitcoin, requires the creation, transmission, and performance of “code,”  and explores the notion that human societies–and thus civilization–is built on a mountain of of encoded processes.

I loved this book and am re-reading it, so I would like to invite you to come read it, too.

Discussion of Chapter 1 Jobs: Divide and Coordinate, will begin on May 23 and last as long as we want it to.

Here’s Amazon’s blurb about the book:

What do Stone Age axes, Toll House cookies, and Burning Man have in common? They are all examples of code in action.

What is “code”? Code is the DNA of human civilization as it has evolved from Neolithic simplicity to modern complexity. It is the “how” of progress. It is how ideas become things, how ingredients become cookies. It is how cities are created and how industries develop.

In a sweeping narrative that takes readers from the invention of the alphabet to the advent of the Blockchain, Philip Auerswald argues that the advance of code is the key driver of human history. Over the span of centuries, each major stage in the advance of code has brought a shift in the structure of society that has challenged human beings to reinvent not only how we work but who we are.

We are in another of those stages now. The Code Economy explains how the advance of code is once again fundamentally altering the nature of work and the human experience. Auerswald provides a timely investigation of value creation in the contemporary economy-and an indispensable guide to our economic future.

Come read “In the Shadow of Man” with me

jane-van-lawick-goodall-in-the-shadow-of-man-book-coverJane Goodall’sIn the Shadow of Man” was first published in 1971, and apparently revolutionized the entire field of primatology and our understanding of our nearest evolutionary cousins. From Amazon:

Her adventure began when the famous anthropologist Dr. Louis Leakey suggested that a long-term study of chimpanzees in the wild might shed light on the behavior of our closest living relatives. … For months the project seemed hopeless; out in the forest from dawn until dark, she had but fleeting glimpses of frightened animals. But gradually she won their trust and was able to record previously unknown behavior, such as the use—and even the making— of tools, until then believed to be an exclusive skill of man. As she came to know the chimps as individuals, she began to understand their complicated social hierarchy and observed many extraordinary behaviors, which have forever changed our understanding of the profound connection between humans and chimpanzees.

It has good reviews, so I’m optimistic about using it for our next Anthropology Friday series, in about a month. (Though I am somewhat skeptical about this supposed first observed non-human tool-making, given that beavers and otters have long been observed.)

Does anyone want to read along with me? I can post discussion questions and make it a regular “book club” affair. (I guess “What counts as ‘tool making?’ should be our first question.) ETA: I promise to avoid really dumb questions, like “Explain how Jane got to know the chimpanzees,” or “Why does the author include an introduction?”

Oh, and it’s fine to post thoughts/responses even if you disliked the book or didn’t read it all the way through. Or if you really hate the book, suggest a different one for next time.