Meta-post on Robots and Jobs

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

I don’t know that I have anything particularly original to say on the worry that robots will soon replace humans in many more tasks, and what implications this has for wages, living conditions, income distribution, the introduction of the Matrix or Skynet, or anything else. So here I’ve just collected a few relevant pieces of information from about the inter-tubes that are useful in thinking about the issue.

Let’s start with some data on “routine” jobs and what is happening to them. Cortes, Jaimovich, Nekarda, and Siu have a recent voxeu post (and associated paper) on the flows into routine versus non-routine work. In the 1980’s, about 1/3 of American workers did “routine” work, now this number is only about 1/4. Routine work tended (and tends) to be middle-waged; it pays pretty well. What the authors find is that the decline in the share of people doing these middle-wage routine jobs is due to slower flows *in* to those jobs, but not due to faster flows *out*. That is, routine workers were not necessarily getting let go more rapidly, but companies were simply not hiring new routine workers.

Unsurprisingly, people with more education were better able to adapt to this. Higher education meant a higher likelihood of shifting into non-routine “cognitive” tasks, which also is a move up the wage scale (upper-middle wages, say). Perhaps more surprising is that women have been more likely, holding education constant, to move into these cognitive tasks. It is low education males who represent the group that is failing to get routine middle-wage jobs. To the extent that these lower-educated males get work, it tends to be in “brawn” jobs, low-wage manual work.

This last fact is somewhat odd in the context of the robot-overlord thesis. Robots/computers are really good at doing routine tasks, but so far have not replaced manual labor. If there was a group that should have a lot to worry about, I’d think it would be low-education males, who could well be replaced as robots become more robust to doing heavy manual labor. One thought I have is that this indicates that manual work (think landscaping) is not as low-skill as routine tasks like data entry. I think there is more cognitive processing that is going on in these jobs than we tend to give them credit for (where to dig, how deep, should I move this plant over a little, what if I hit a root?, does this shrub look right over here, etc.. ), and that their wages are low simply because the supply of people who can do those jobs is so large.

Brad DeLong took on the topic by considering Peter Thiel‘s comments in the Financial Times. Thiel is relatively optimistic about the arrival of robots – he uses the computer/human mix at Paypal to detect fraud as the example of how smarter machines or robots will benefit workers. Brad worries that Thiel is making a basic error. Yes, machines relieve us of drab, boring, repetitive work. But whether workers benefit from that (as opposed to the owners of the machines) depends not on the average productivity of that worker, but on the productivity of the marginal worker who is not employed. That is, if I can be replaced at Paypal by an unemployed worker who has no other options, then my own wage will be low, regardless of how productive I am. By replacing human workers in some jobs, robots/machines drive up the supply of humans in all the remaining jobs, which lowers wages.

To keep wages high for workers, we will need to increase demand for human-specific skills. What are those? Brad likes to list 6 different types of tasks, and leaves humans with persuasion, motivation, and innovation as things that will be left for humans to do. Is there sufficient demand for those skills to keep wages elevated? I don’t know.

David Autor has a recent working paper that is relatively optimistic about robots/machines. He thinks there is more complementarity between machines and humans than we think, so it echoes Thiel’s optimism. Much of Autor’s optimism stems from what he calls “Polyani’s Paradox”, which is essentially that we are incapable of explaining in full what we know. And if we cannot fully explain exactly what we know how to do (whether that is identifying a face in a crowd, or making scrambled eggs, writing an economics paper, or building a piece of furniture) then we cannot possibly program a machine to do it either. The big limit of machines, for Autor, is that they have no tacit knowledge. Everything must be precisely specified for them to work. There is no “feel” to their actions, so to speak. As long as there are tasks like that, robots cannot replace us, and it will require humans – in conjunction with machines, maybe – to actually do lots of work. Construction workers are his example.

But I am a little wary of that example. Yes, construction workers today work with a massive array of sophisticated machines, and they serve as the guidance systems for those machines, and without construction workers nothing would get done. But that’s a statement about average product, not marginal product. The wage of those workers could still fall because better machines could make *anyone* capable of working at a construction site, and the marginal product of any given worker is very low. Further, adding better or more construction machines can reduce the number of construction workers necessary, which again only floods the market with more workers, lowering the marginal product.

Autor gets interviewed in this video from Ryan Avent at the Economist. It’s a fairly good introduction to the ideas involved with robots replacing workers.

Lower Skill Demand in the 21st Century

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

Having just posted something on de-skilling in the Industrial Revolution, I saw this post by Nick Bunker regarding the skills gap (or perceived skills gap) as an explanation for the current low employment rate in the U.S. currently. He links to a paper from last year by Paul Beaudry, David Green, and Benjamin Sand on the reversal in the demand for skills since 2000.
Beaudry et al 2013 figure 13
The Beaudry, Green, Sand (BGS) paper has the same kind of stylized fact, only over a much shorter time frame, as the paper on the Industrial Revolution by de Pleijt and Weisdorf I posted about. Basically, the level of skilled worker employment is falling. For BGS, this begins around the year 2000, and you can see the essential point they are making in their figure 13. Here you see that the employment share for “high-skilled” workers (managers, technical workers, professionals) peaked right around 2000, and since then has been falling. The drop since 2008 really seems to just be continuing the pre-2008 trend rather than a response to the financial crisis. In contrast, the trend for low-skilled service and labor employees is strongly positive (their figure 15), and despite the dip in 2008, this doesn’t seem to have reversed the general upward trend of the last few decades. So we appear to have a “de-skilling” going on in the U.S. since 2000.
Beaudry etal 2013 figure 15

BGS propose a theory for why this might be the case. In their model, the IT wave of the 1990’s required high-skilled workers to get the IT capital installed – put in the servers, write the underlying code, adapt existing business practices to new IT, put things on websites, etc. etc.. [Quick aside: Before I got my Ph.D. and inhabited this dark corner of the internet, I was one of those IT workers. One of my clients was United Airlines, and I worked on incorporating e-tickets into their back-office accounting system. It was as boring as it sounds, and hence here I am.]

Now that this IT capital is installed, we don’t need nearly as many high-skilled workers, as we’re down to maintenance work. [Example: the team of people I worked with at United are now all doing other things because you only need to program the accounting system for e-tickets once.] So according to BGS we now have an “overhang” of skilled workers with college degrees who aren’t really needed. They are pushing down the job food chain into jobs that would normally have gone to medium-skilled non-college-educated workers, which in turn forces those people down into low-skilled service or labor jobs. People with very low skills/education are pushed out of the labor force entirely or forced to work for less because there is an abundance of that kind of labor.

This has to be bad, right? It is, certainly, for everyone who has been caught out with “too many” skills for their jobs. Lots of people were investing in college educations in order to be those high-skilled technicians and managers, and now they can’t find those kind of jobs. They have to take less skill-intense positions, for which there is more competition, and hence they probably won’t be earning as much relative to the loans they took out to go to college. The people at the very bottom end of the job food chain are really out of luck because they are being replaced entirely.

But. But as we go forward, new workers who get added to the workforce can do so without acquiring as many costly skills. In short, they could get away with skipping college, or do it at a cheaper place, or get a 2-year rather than a 4-year degree. If the de-skilling trend continues (robots!), then it isn’t necessarily true that *new* workers are necessarily worse off. They may face a market that demands more low-skilled than high-skilled workers, but they would also need to invest far less in order to be hired. Imagine not needing to go into $40,000 in student debt just to get a job. They may well be better off without the debt and with the lower-skill job. [Before someone gets all huffy in the comments, yes, I’m include my over-educated academic self in that bucket. If I was just 10 years old now, maybe I would end up with less formal education, a lower skill job, and do economic growth as a hobby. Who knows?] There are two responses to technical change. Raise output, or lower inputs. Since 2000 we’ve apparently been choosing the latter strategy, and that might be how we continue.

So, whether de-skilling is bad depends on perspective. From the perspective of existing workers with skill, yes it is bad. From the perspective of new workers who are making their choices about skill, no it is not. Which makes it no different from any other technology change. Was it obvious that the invention of the automobile was bad? From the perspective of trained farriers, yes. From the perspective of young people who could choose to become a machinist rather than a farrier, no. The change to autos presumably was skill-demanding (although, I don’t know, I never shoed a horse before), but that doesn’t change the fact that some people lose and some people win. We’ve lived through thirty years of increased demand for college educated people, with an attendant increase in their wages relative to the less educated. Is it necessarily bad that this trend reverses?

What about those people at the very bottom of the skill distribution? They’re getting shafted by this de-skilling. Yes. But they were getting shafted before 2000 by the skill-biased technical change favoring college-educated workers. De-skilling suggests that maybe how we help those at the bottom of the distribution should change. Maybe de-skilling means we need to rethink whether college prep as the point of education. Maybe the point should be on building marketable skills, not building college-applications? Nowhere is it written that technological change and economic growth must always and forever increase the demand for college-educated employees, so it may be time to adapt.

Does de-skilling mean that labor is going to “lose” compared to capital, or that de-skilling is a cause of increasing concentration of wealth and income? Maybe. To answer that you need to know about the elasticity of substitution between labor and capital. If it is big, then de-skilling could be the symptom of capital being substituted for labor in production, which in turn is going to lead to a lower share of national income for labor. If the elasticity is small, then de-skilling would eventually lead to an increase in the share of national income going to labor. My gut reaction is that the elasticity is relatively big, especially over longer time periods, and so if de-skilling were to continue, labor probably keeps earning a smaller share of national income. That’s an entirely different discussion to have about inequality and distribution, which takes you down the Piketty rabbit-hole.