Tag Archives: Automation

Automation As Colonization Wave

Our automation data analysis found a few surprising results. We found that labor demand is inversely correlated with education. As if, when facing a labor shortage for a particular kind of worker, employers respond in part by lowering education requirements. And even though more automation directly lowers demand for a job, it seems that labor demand changes, relative to labor supply changes, becomes a smaller factor for jobs where automaton rises more.

But the most interesting surprise, I think, is that while, over the last twenty years, we’ve seen no noticeable change in the factors that predict which jobs get more automated, we have seen job features change to become more suitable to automation. On average jobs have moved by about a third of a standard deviation, relative to the distribution of job automation across jobs. This is actually quite a lot. Why do jobs change this way?

Consider the example of a wave of human colonization moving over a big land area. Instead of all the land becoming colonized more densely at same rate everywhere, what you instead see is new colonization happening much more near old colonization. In the U.S., dense concentrations started in the east and slowly spread to the west. There was little point in clearing land to grow stuff if there weren’t enough other folks nearby to which to sell your crops, and from which to buy supplies.

If you looked at any particular plot of land and asked what factors predict if it will be colonized soon, you might see those factors stay pretty constant over time. But many of those factors would depend on what other land nearby had been colonized recently. In a spatial colonization wave, there can be growth without much change in the underlying tech. Instead, the key dynamic can be that there are big time delays to allow an initial tech potential to become realized via spreading across a large landscape. A colonization wave can be growth without much tech change.

Now think about the space of job tasks as a similar sort of landscape. Two tasks are adjacent to other tasks when the same person tends to do both, when info or objects are passed from one to the other, when they take place close in place and time, and when their details gain from being coordinated. The ease of automating each task depends on how regular and standardized are its inputs, how easy it is to formalize the info on which key choices depend, how easy it is to evaluate and judge outputs, and how simple, stable, and mild are the physical environments in which this task is done.

When the tasks near a particular task get more automated, those tasks tend more to happen in a more controlled stable environment, the relevant info tends to be more formalized, and related info and objects get simpler, more standardized, and more reliably available. And this all tends to make it easier to automate such tasks. Much like how land is easier to colonize when nearby land is more colonized.

Among the job features that predict automation in our analysis, the strongest is: Pace Determined By Speed Of Equipment. This feature clearly fits my story here; it says you coordinate your task closely with a task done by a machine. Many others fit as well; here is more from our paper:

Pace Determined By Speed Of Equipment picks out jobs that coordinate closely with machinery, while Importance of Repeating Same Tasks picks out jobs with many similar and independent small tasks. Variety picks out an opposite case of dissimilar tasks. The job features Wear Common Safety Equipment and Indoors Environmentally Controlled pick out tasks done in calm stable environments, where machines function better, while Hearing Sensitivity picks out less suitable complex subtle environments. In jobs with frequent Letters and Memos, such memos tend to be short and standardized. Jobs with more Advancement are “results oriented”, with more clearly measurable results. Simple machines tend to be bad at Thinking Creatively, Innovation and Mathematics. Physical Proximity picks out jobs done close to humans, usually because of needed human interactions, which tend to be complex, and where active machines could risk hurting them.

We have long been experiencing a wave of automation passing across the space of job tasks. Some of this increase in automation has been due to falling computer tech costs, improving algorithms and tools, etc. But much of it may simply be the general potential of this tech being realized via a slow steady process with a long delay: the automation of tasks near other recently automated tasks, slowly spreading across the landscape of tasks.

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Automation: So Far, Business As Usual

Since at least 2013, many have claimed that we are entering a big automation revolution, and so should soon expect to see large trend-deviating increases in job automation levels, in related job losses, and in patterns of which jobs are more automated.

For example, in the October 15 Democratic debate between 12 U.S. presidential candidates, 6 of them addressed automation concerns introduced via this moderator’s statement:

According to a recent study, about a quarter of American jobs could be lost to automation in just the next ten years.

Most revolutions do not appear suddenly or fully-formed, but instead grow from precursor trends. Thus we might hope to test this claim of an automation revolution via a broad study of recent automation.

My coauthor Keller Scholl and I have just released such a study. We use data on 1505 expert reports regarding the degree of automation of 832 U.S. job types over the period 1999-2019, and similar reports on 153 other job features, to try to address these questions:

  1. Is automation predicted by two features suggested by basic theory: pay and employment?
  2. Do expert judgements on which particular jobs are vulnerable to future automation predict which jobs were how automated in the recent past?
  3. How well can we predict each job’s recent degree of automation from all available features?
  4. Have the predictors of job automation changed noticeably over the last two decades?
  5. On average, how much have levels of job automation changed in the last two decades?
  6. Do changes in job automation over the last two decades predict changes in pay or employment for those jobs?
  7. Do other features, when interacted with automation, predict changes in pay or employment?

Bottom line: we see no signs of an automation revolution. From our paper‘s conclusion:

We find that both wages and employment predict automation in the direction predicted by simple theory. We also find that expert judgements on which jobs are more vulnerable to future automation predict which jobs have been how automated recently. Controlling for such factors, education does not seem to predict automation.

However, aside perhaps from education, these factors no longer help predict automation when we add (interpolated extensions of) the top 25 O*NET variables, which together predict over half the variance in reported automation. The strongest O*NET predictor is Pace Determined By Speed Of Equipment and most predictors seem understandable in terms of traditional mechanical styles of job automation.

We see no significant change over our time period in the average reported automation levels, or in which factors best predict those levels. However, we can’t exclude the possibility of drifting standards in expert reports; if so, automation may have increased greatly during this period. The main change that we can see is that job factors have become significantly more suitable for automation, by enough to raise automation by roughly one third of a standard deviation.

Changes in pay and employment tend to predict each other, suggesting that labor market changes tend more to be demand instead of supply changes. These changes seem weaker when automation increases. Changes in job automation do not predict changes in pay or employment; the only significant term out of six suggests that employment increases with more automation. Falling labor demand correlates with rising job education levels.

None of these results seem to offer much support for claims that we are in the midst of a trend-deviating revolution in levels of job automation, related job losses, or in the factors that predict job automation. If such a revolution has begun, it has not yet noticeably influenced this sort of data, though continued tracking of such data may later reveal such a revolution. Our results also offer little support for claims that a trend-deviating increase in automation would be accompanied by large net declines in pay or employment. Instead, we estimate that more automation mainly predicts weaker demand, relative to supply, fluctuations in labor markets.

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Wealth, Not Robots, Makes Us Lazy

Tyler Cowen adapts his forthcoming book, Average Is Over, into a NYT essay:

Self-driving vehicles threaten to send truck drivers to the unemployment office. … There are even computers that can grade essay exams with reasonable accuracy. … Who will prosper and who won’t in this new kind of machine economy?

Who will do well? THE CONSCIENTIOUS … PEOPLE WHO LISTEN TO COMPUTERS Your smartphone will record data on your life and, when asked, will tell you what to do. … PEOPLE WITH A MARKETING TOUCH … MOTIVATORS … Managers who are motivators of first-rate talent will see their earnings continue to rise.

Who will be most likely to suffer from this technological revolution? PEOPLE WITH DELICATE FEELINGS Computing and software will make it easier to measure performance and productivity. It will be harder to gloss over our failings and maintain self-deception. … PEOPLE UNLUCKY IN HEALTH CARE … PEOPLE WHO DON’T NEED MONEY … people who are bright, culturally literate, Internet-savvy and far from committed to the idea of hard work directed toward earning a good middle-class living. … have the incomes of the lower middle class and the cultural habits of the wealthy or upper middle class. … POLITICAL RADICALS: … We’re … aging rapidly, and that tends to make society more peaceful, less violent and less extreme in all directions. (more)

Tyler has the pundit style of trying to make everything seem as if it turns on today’s fashionable worries. Since one of today’s fashionable worry is automation, Tyler talks as if that caused all these trends. But in fact, while most of these trends are real, they have much more to do with increasing wealth than increasing automation.

Because we are richer, we are healthier and live longer. Older folks are less inclined to political radicalism, and those unluckily in health look all the worse compared to the healthy. Rich folks are also more inclined to be lazy and arrogant, as the threat of starvation fades into non-existence. So among the rich there are bigger gains to retaining strong inclinations to work hard even when rich, and an ability to similarly motivate others. There are also bigger gains among the rich to listening to others, especially about the quality of your work, and resisting the lazy inclination toward arrogance.

I disagree that it is getting easier to measure performance and productivity overall, or that smart phones will give useful life advice anytime soon. Yes smartphones and other machines may measure your heart rate and keystrokes more easily, but as we work in larger and more coordinated organizations, it gets harder not easier to measure individual performance. For example, when video games were made by small teams, individual contributions were much easier to discern than now when hundreds work together.

Someday robots will make a huge difference. It is important to foresee and prepare for that eventuality. But it will only get harder to take that distant prospect seriously when pundits keep crying wolf and blaming automation for trends caused by other things.

Added 3 Sept: I’d say most long-term trends can be understood reasonably well as due to increasing wealth and lifespans. These include less monogamy, religion, work ethic, and violence. See my forager vs. farmer story.

Instapundit and Marginal Revolution linked here.

Added 27 Sep: Maytt Yglesias agrees that tech isn’t the explanation:

In Stagnation, Cowen reviewed the previous generation and concluded that despite substantial progress and catch-up in poor countries that the median household in rich countries had suffered stagnant living standards thanks to a slowdown in technological progress. In Average, Cowen looks ahead at the next generation and concludes that despite substantial progress and catch-up in poor countries, the median household in rich countries will suffer stagnant living standards thanks to a speedup in technological progress.

Curious. Which is to say that while each book offers a brilliant exposition of income stagnation and how it intersects with the technological progress of its era, read in conjunction it’s clear that in neither period was the stagnation actually caused by the pace of technological change.

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