Markets, Institutions, and Underpants

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

The title of this post is my proposed re-naming of Sven Beckert’s Empire of Cotton: A Global History. Grabs the attention, right?

The short recommendation is that you should read this book if you are interested in economic history and growth.

The long recommendation is that Beckert’s is an entry in the “global history” genre, this time using cotton production, processing, and trade as the framing device. But it is not just another version of Salt: A World History, with a new commodity plugged in. Beckert actually has a larger point to make about how a “market” in a commodity is something that is created by people, sometimes explicitly and sometimes not. In that sense, this book is better at explaining how institutions shape economies than most books that are specifically about institutions.

A key component of the story is the recognition that the global market for cotton was created prior to the Industrial Revolution, as part of what Beckert somewhat awkwardly calls “war capitalism”. De Gama and Columbus created direct links between Europe, South Asia, Africa, and North America. Europeans then used a superior ability to coordinate firepower and capital to ship goods between these nodes. Cotton from India was sent to Africa for slaves or South-east Asia for spices. The slaves were sent from Africa to North America, the spices to Europe. One could refer to there being “markets” for these things, but only in the sense that Europeans were trading claims on these various people or goods amongst themselves.

Beckert separates the institutions of modern capitalism, which governed the intra-European trade, from the institutions of war capitalism, which governed European trade with non-Europeans. The former developed along the idealized lines of protected property rights, secure contracts, and so forth. The latter was about coercion and expropriation. The Europeans played “cooperate” with each other, so to speak, while playing “deviate” with the rest of the world. In Liverpool the English cotton brokers developed standards of quality, separated physical location in a warehouse from nominal ownership, and created futures contracts. In the American South planters enslaved millions in order to fulfill those contracts.

The consequences of the global market in cotton were far-reaching. The cotton factory, all spindles and chimneys, becomes the epitome of the Industrial Revolution. Beckert’s implied story about innovation in this industry is Allen-like. The major costs of cotton trade were in spinning and weaving, not in growing. So innovation occurs in Britain where those costs are particularly high. But cotton also has far more scope for innovation in processing than the other major crops. It may be natural that cotton production was innovated on. There just isn’t much innovation to do on sugar once it is refined. What are you going to do, make clothes out of it? This isn’t the book to use in an argument about factor prices versus the enlightenment in generating the IR.

The more interesting question that looms over Beckert’s book is whether slavery, or the coercion of labor in any form, was necessary for the growth of the cotton trade and Industrial Revolution. Here you have to be careful about wording. Necessary? No. It was certainly possible that the global cotton trade could have evolved in a different way, perhaps with India and Egypt remaining major exporters and the American South a patchwork of small-holding cotton farmers. But did slavery and the coercion of labor accelerate the development of the global cotton trade and likely the Industrial Revolution? The answer seems to be yes. Ceteris paribus, slavery and coercion made the IR happen sooner rather than later. I think that’s what Beckert would argue. I am leaning towards agreement with him, but I need some more information before I would come down hard one way or the other.

Probably the most compelling thing I learned reading the book is about the layers of institutions that exist within economies. Beckert makes clear that there is no such thing as “English institutions” (or any other) that are constant across all transactions. Institutions are a characteristic of two entities (states, people, firms) and any given pair of entities will have its own set of institutions. So Liverpool and New Orleans cotton brokers had one set of institutions, Liverpool and Manchester brokers had another, while Liverpool and Bombay brokers a third. In some cases those institutions are “good”, fostering cooperation and trust, while others are “bad”, involving coercion. As is typical, institutions are really central to studying growth, but measuring or quantifying institutions without being extremely specific about the exact parties involved is probably hopeless.

Techno-neutrality

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

I’ve had a few posts in the past few months (here and here) about the consequences of mechanization for the future of work. In short, what will we do when the robots take our jobs?

I wouldn’t call myself a techno-optimist. I don’t think the arrival of robots necessarily makes everything better. But I do not buy the strong techno-pessimism that comes up in many places. Richard Serlin has been a frequent commenter on this blog, and he generally has a gloomy take on where we are going to end up once the robots arrive. I’m not bringing up Richard to pick on him. He writes thoughtful comments on this subject (and lots of others), and it is those comments that pushed me to try and be more clear on why I’m “techno-neutral”.

The economy is more creative than we can imagine. The coming of robots to mechanize away our jobs is the latest in a long, long, long, history of technology replacing workers. And yet here we still are, working away. Timothy Taylor posted this great selection a few weeks ago. This is a quote from Time Magazine:

The rise in unemployment has raised some new alarms around an old scare word: automation. How much has the rapid spread of technological change contributed to the current high of 5,400,000 out of work? … While no one has yet sorted out the jobs lost because of the overall drop in business from those lost through automation and other technological changes, many a labor expert tends to put much of the blame on automation. … Dr. Russell Ackoff, a Case Institute expert on business problems, feels that automation is reaching into so many fields so fast that it has become “the nation’s second most important problem.” (First: peace.)
The number of jobs lost to more efficient machines is only part of the problem. What worries many job experts more is that automation may prevent the economy from creating enough new jobs. … Throughout industry, the trend has been to bigger production with a smaller work force. … Many of the losses in factory jobs have been countered by an increase in the service industries or in office jobs. But automation is beginning to move in and eliminate office jobs too. … In the past, new industries hired far more people than those they put out of business. But this is not true of many of today’s new industries. … Today’s new industries have comparatively few jobs for the unskilled or semiskilled, just the class of workers whose jobs are being eliminated by automation.

That quote is from 1961. This is almost word for word the argument you will get about robots and automation leading to mass unemployment in the future. 50 years ago we were just as worried about this kind of thing, and in those 50 years we do not have massive armies of unemployed workers wandering the streets. The employment/population ratio in 1961 was about 55%, and then it steadily rose until the late 90’s when it topped out at about 64%. Even after the Great Recession, the ratio is still 59% today, higher than it was in 1961.

This didn’t happen without disruption and dislocation. And the robots will cause similar dislocation and disruption. Luddites weren’t wrong about losing their jobs, they were just wrong about the economy losing jobs in aggregate. But I don’t see why next-generation robots are any different than industrial robots, mainframes, PC’s, tractors, mechanical looms, or any other of the ten million innovations made in history that replaced labor. We can handle this with some sympathy and try to smooth things out for those dislocated, or we can do what usually happens and let them hang out to dry. The robots aren’t the problem here, we are.

What exactly are those new jobs that will be created? If I knew, then I wouldn’t be writing this blog post, I’d be out starting a company. The fact that I cannot conceive of an innovation myself is not evidence that innovation has ceased. But I do believe in the law of large numbers, and somewhere among the 300-odd million Americans is someone who *is* thinking of a new kind of company with new kinds of jobs.

Robots change prices as well as wages. An argument for pessimism goes like this. People have subsistence requirements, meaning they have a wage floor below which they cannot survive. Robots will be able to replace humans in production and this will drive the wage below that subsistence requirement. Either no firm will hire workers at the subsistence wage or people who do work will not meet subsistence.

The problem with this argument is that it ignores the impact of robots on the price of that subsistence requirement. Subsistence requirements are in real terms (I need clothes and housing and food), not nominal terms (I need $2000 dollars). The “subsistence wage” is a a real wage, meaning it is the nominal wage divided by the price level of a subsistence basket of goods. Robots lowering marginal costs of production lowers the nominal human wage, but it also lowers the price of goods. It is not necessary or even obvious that real wages have to fall because of robots. History says that despite all of the labor-saving technological change that has gone on over the last few hundred years, real wages have risen as the lower costs outweigh the downward pressure on wages.

Who is going to buy what the robots produce? Call this the “Henry Ford” argument. If you are going to invest in opening up a factory staffed entirely by robots, then who precisely is supposed to buy that output? Ford raised wages at his highly mechanized (for the time) plants so that he had a ready-made market for his cars. The Henry Fords of robot factories are going to need a market for the stuff they build. Rich people are great, but diminishing marginal utility sets in pretty quick. That means robot owners either need to lower prices or raise wages for the people they do hire in order to generate a big enough market. Depending on the fixed costs involved in getting these proverbial robot factories up and running, robot owners may be a strong force for keeping wages high in the economy, just like Henry Ford was back in the day.

The wealthy are wealthy because they own productive assets. A tiny fraction of the value of those assets is due to the utility to the owner of the widgets they kick out. The majority of the value of those assets is due to the fact that you can *sell* that output for money and use that money to buy other widgets. Rockefeller wasn’t wealthy because he had a lot of oil. He was wealthy because he could sell it to other people. No other people, no wealth. Just barrel after barrel of useless black gunk.

The same holds for robot owners. Those robots and robot factories have value because you can sell them or the goods they make in the wider economy. And that means continuing to exchange with the non-wealthy. You cannot be wealthy in a vacuum. Bill Gates on an island with robots and a stack of 16 billion dollar bills is Gilligan with a lot of kindling.

Wealthy robot owners will do what wealthy (fill in capital stock here) owners have done for eons. They’ll trade access to the capital, or the goods it produces, to the non-wealthy in exchange for services, effort, flattery, and new ideas on what to do with that wealth.

Wealth concentration would be a problem with or without robots. The worry here is that because the wealthy will be the only ones able to build the robots and robot factories, they will control completely the production of goods and the demand for labor. That’s not a problem that arises with robots, that is a problem that arises with, well, settled agriculture 10,000 years ago. Wealth concentration makes owners both monopolists (market power selling goods) and monopsonists (market power buying labor), which is a bad combination. It gives them the ability to drive (real) wages down to minimum subsistence levels. This is bad, absolutely. But this was bad when (fill in example of a landed elite) did it in (fill in historical era here). This is bad in “company towns”. This is bad now, today. So if you want to argue against wealth concentration and the pernicious influence it has on wages, get started. Don’t wait for the robots, they’ve got nothing to do with it.

Again, be clear that in arguing against techno-pessimism I am not arguing that robots will generate a techno-utopia with ponies and rainbows. I just do not buy the dystopian view that somehow it’s all going to come crashing down around our ears because of the very particular innovations coming in the near future.

Technology and “Good Jobs”

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

I got a number of comments and e-mails regarding a recent post on technological change, jobs that produce goods, and “good jobs”. This is a follow up meant to clarify some points and solidify others.

1. The entire point of my post was to say that the exact tasks people do is unrelated to whether they have a “good job”. Working in manufacturing does not make a job “good”, and working in services does not make a job “bad”. Yes, “good” and “bad” are fuzzy terms.
2. I’m not denying that technology is replacing manufacturing jobs. It is. It will. We may well end up with robots making everything. If so, then you want to make sure that the jobs that people do have are “good jobs”.
3. Service jobs are not, even to a first approximation, poor people doing things for rich people. So no, we won’t run out of jobs because rich people can only get so many massages or restaurant meals. The vast majority of workers in the US for the last 60 years have been non-rich people doing service-like things for other non-rich people. [Teachers, cops, firemen, nurses, waiters, store clerks, everyone in HR, everyone in accounts payable, secretaries, receptionists, every computer programmer, truck drivers, warehouse workers, chefs, everyone who works on any TV show, record, or movie, claims adjusters, insurance agents, financial analysts, everyone at your local bank, your IT guys, everyone working in state or federal government, priests, librarians, florists, pizza delivery guys, photographers, personal trainers, dietitians, optometrists, dentists, physical therapists, veterinarians, security guards, dishwashers, hostesses, exterminators, HVAC workers, plumbers, electricians, roofers, rodeo clowns, pit bosses, morticians, barbers, day-care attendants, real estate brokers, airline pilots, car mechanics, flight attendants, taxi drivers, and yes, even used car salesmen. Just to give a few examples.] We are very good at finding things to do for each other. We’ll continue to be good at that
4. No, you cannot “work any day you want to”. Ask the day laborers that hang out at the Home Depot near my house how they are doing. Some days you pick the wrong parking lot. Some days it’s raining. Some days there just isn’t anyone with a job. The frictions and costs of working day-to-day are huge.
5. Personally, I think that the following characteristics are associated with “good jobs”. (A) Security/steadiness. As per #2, knowing that your job will be there next week/month/year is incredibly valuable. It allows you to undertake long-run commitments, like marriage, home-ownership, and schooling. (B) Family flexibility. You can deal with your life (i.e. all the crap you need to get your kids to) without the fear of being fired for it. (C) Pay/Benefits. Enough money to afford decent health insurance, or decent health insurance provided by the employer. In short, I think people want stability more than anything. The attraction of those mid-20th century union jobs for workers was that they had lock-it-down certainty about the future.
6. Yes, it is possible to make any kind of job a “good job”. I used the Costco/Wal-mart distinction as an example. Justin Wolfers and Jan Zilinsky just posted a piece containing further examples. In short, worker productivity is not a fixed value, and paying higher wages is associated with getting higher productivity from the same workers. Costco has a wage/benefit structure that encourages their workers to be productive. In return, Costco saves money from lower turnover. What Wolfers and Zilinsky show is that this works in a variety of settings.
7. The original post made it sound as if unions were the only way to generate the conditions of “good jobs”. That is not true, and not what I intended to say. Unions were one way to elicit those good conditions from employers, and manufacturing workers were particularly well placed to unionize and negotiate those conditions. But unions aren’t necessary for this. Costco isn’t unionized. [CORRECTION: About 15,000 Costco workers are part of the Teamsters. Roughly 174,000 total Costco workers. DV 1/20/15] We need companies to recognize the value of becoming a “good job” employer, but there are lots of ways to do that.

Job Quality is about Policies, not Technology

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

Nouriel Roubini posted an article titled “Where Will All the Workers Go?”. A few pulls:

“The risk is that robotics and automation will displace workers in blue-collar manufacturing jobs before the dust of the Third Industrial Revolution settles.”

“But, unless the proper policies to nurture job growth are put in place, it remains uncertain whether demand for labor will continue to grow as technology marches forward.”

“Even that may not be sufficient, in which case it will become necessary to provide permanent income support to those whose jobs are displaced by software and machines.”

The worry here is that technology will replace certain jobs (particularly goods-producing jobs) and that there will literally be nothing for those people to do. They will presumably exit the labor market completely and possibly need permanent income support.

Let’s quickly deal with the “lump of labor” fallacy sitting behind this. Technology reduces the demand for labor in some industries, so fewer workers are employed there. Which raises the supply of labor in all the other industries. For that supply shock to generate no other employment you have to assume that the $15 trillion dollar a year U.S. economy is so rigidly inflexible that it has a definitely fixed set of jobs that can be filled. That’s ridiculous.

To a rough approximation, just about the exact same number of people work in goods-producing industries in 2013 (19 million) as did in 1950 (17 million). And yet somehow the rest of us have figured out what to do with ourselves in the interim. Between 1950 and 2013 the U.S. economy expanded from 28 million service jobs to 117 million service jobs (All stats from the BLS). You think that somehow it won’t be able to figure out what to do with more workers that are displaced by technology? We’ve been creating new kinds of jobs for two hundred years.

So let’s ignore the phantom worry that tens of millions workers suddenly find themselves completely at a loss to find work. The economy is going to find something for these people to do. The question is what kind of jobs these will be.

Will they be “bad jobs”? McJobs at retail outlets, wearing a nametag? These aren’t “good jobs”, real jobs. Making “stuff” is a real job, not some made-up bullshit service job.

We can worry about the quality of jobs, but the mistake here is to confound “good jobs” with manufacturing or goods-producing jobs. Manufacturing jobs are not inherently “good jobs”. There is nothing magic about repetitively assembling parts together. You think the people at Foxconn have good jobs? There is no greater dignity to manufacturing than to providing a service. Cops produce no goods. Nurses produce no goods. Teachers produce no goods.

Manufacturing jobs were historically “good jobs” because they came with benefits that were not found in other industries. Those benefits – job security, health care, regular raises – have nothing to do with the dignity of “real work” and lots to do with manufacturing being an industry that is conducive to unionization. The same scale economies that make gigantic factories productive also make them relatively easy places to organize. They have lots of workers collected in a single place, with definitive safety issues to address, and an ownership that can be hurt deeply by shutting down the cash flow they need to pay off debt. To beat home the point, consider that what we consider “good” service jobs – teacher, cop – are also heavily unionized. Public employees, no less.

If you want people to get “good jobs” – particularly those displaced by technology – then work to reverse the loss of labor’s negotiating power relative to ownership. Raise minimum wages. Alleviate the difficulty in unionizing service workers.

You want to smooth the transition for people who are displaced, and help them move into new industries? Great. Let’s have a discussion about our optimal level of social insurance and support for training and education. But the sectors people leave or eventually enter are irrelevant to that.

You want to worry about downward wage pressure as the demand for labor falls? Great. Worry about that. See the above point about raising labor’s negotiating power relative to ownership.

Hoping or trying to recreate the employment structure of 1950 is stupid. We don’t need that many people to assemble stuff together any more because we are so freaking good at it now. The expansion of service employment isn’t some kind of historical mistake we need to reverse.

Any job can be a “good job” if the worker and employer can coordinate on a good equilibrium. Costco coordinates on a high-wage, high-benefit, high-effort, low-turnover equilibrium. Sam’s Club coordinates on a low-wage, low-benefit, low-effort, high-turnover equilibrium. Both companies make money, but one provides better jobs than the other. So as technology continues to displace workers, think about how to get *all* companies to coordinate on the “good” equilibrium rather than pining for lost days of manly steelworkers or making the silly presumption that we will literally run out of things to do.

The Slowdown in Reallocation in the U.S.

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

One of the components of productivity growth is reallocation. From one perspective, we can think about the reallocation of homogenous factors (labor, capital) from low-productivity firms to high-productivity firms, which includes low-productivity firms going out of business, and new firms getting started. A different perspective is to look more closely at the shuffling of heterogenous workers between (relatively) homogenous firms, with the idea being that workers may be more productive in one particular environment than in another (i.e. we want people good at doctoring to be doctors, not lawyers). Regardless of how exactly we think about reallocation, the more rapidly that we can shuffle factors into more productive uses, the better for aggregate productivity, and the higher will be GDP. However, evidence suggests that both types of reallocation have slowed down recently.

Foster, Grim, and Haltiwanger have a recent NBER working paper on the “cleansing effect of recessions”. This is the idea that in recessions, businesses fail. But it’s the really crappy, low-productivity businesses that fail, so we come out of the recession with higher productivity. The authors document that in recessions prior to the Great Recession, downturns tend to be “cleansing”. Job destruction rates rise appreciably, but job creation rates remain about the same. Unemployment occurs because it takes some time for those people whose jobs were destroyed to find newly created jobs. But the reallocation implied by this churn enhances productivity – workers are leaving low productivity jobs (generally) and then getting high productivity jobs (generally).

But the Great Recession was different. In the GR, job destruction rose by a little, but much less than in prior recessions. Job creation in the GR fell demonstrably, much more than in prior recessions. So again, we have unemployment as the people who have jobs destroyed are not able to pick up newly created jobs. But because of the pattern to job creation and destruction, there is little of the positive reallocation going on. People are not losing low productivity jobs, becoming unemployed, and then getting high productivity jobs. People are staying in low productivity jobs, and new high productivity jobs are not being created. So the GR is not “cleansing”. It is, in some ways, “sullying”. The GR is pinning people into *low* productivity jobs.

This holds for firm-level reallocation well. In recessions prior to the GR, low productivity firms tended to exit, and high productivity firms tended to grow in size. So again, we had productivity-enhancing recessions. But again, the GR is different. In the GR, the rate of firm exit for low productivity firms did not go up, and the growth rate of high-productivity firms did not rise. The GR is not “cleansing” on this metric either.

Why is the GR so different? The authors don’t offer an explanation, as their paper is just about documenting these changes. Perhaps the key is that a financial crash has distinctly different effects than a normal recession. A lack of financing means that new firms cannot start, and job creation falls, leading to lower reallocation effects. A “normal” recession doesn’t involve as sharp a contraction in financing, so new firms can take advantage of others going out of business to get themselves going. Just an idea, I have no evidence to back that up.

[An aside: For the record, there is no reason that we need to have a recession for this kind of reallocation to occur. Why don’t these crappy, low-productivity firms go out of business when unemployment is low? Why doesn’t the market identify these crappy firms and compete them out of business? So don’t take Foster, Grim, and Haltiwanger’s work as some kind of evidence that we “need” recessions. What we “need” is an efficient way to reallocate factors to high productivity firms without having to make those factors idle (i.e. unemployed) for extended periods of time in between.]

In a related piece of work Davis and Haltiwanger have a new NBER working paper that discusses changes in workers reallocations over the last few decades. They look at the rate at which workers turn over between jobs, and find that in general this rate has declined since 1980 to today. Some of this may be structural, in the sense that as the age structure and education breakdown of the workforce changes, there will be changes in reallocation rates. In general, reallocation rates go down as people age. 19-24 year olds cycle between jobs way faster than 55-65 year olds. Reallocation rates are also higher among high-school graduates than among college graduates. So as the workforce has aged and gotten more educated from 1980 to today, we’d expect some decline in job reallocation rates.

But what Davis and Haltiwanger find is that even after you account for these forces, reallocation rates for workers are declining. No matter which sub-group you look at (e.g. 25-40 year old women with college degrees) you find that reallocation rates are falling over time. So workers are flipping between jobs *less* today than they did in the early 1980s. Which is probably somewhat surprising, as my guess is that most people feel like jobs are more fleeting in duration these days, due to declines in unionization, etc.. etc..

The worry that Davis and Haltiwanger raise is that lower rates of reallocation lower productivity growth, as mentioned at the beginning of this post. So what has caused this decline in reallocation rates across jobs (or across firms as the first paper described)? From a pure accounting perspective, Davis and Haltiwanger gives us several explanations. First, reallocation rates within the Retail sector have declined, and since Retail started out with one of the highest rates of reallocation, this drags down the average for the economy. Second, more workers tend to be with older firms, which have less turnover than young firms. Last, the above-mentioned shift towards an older workforce that tends to shift jobs less than younger workers.

Fine, but what is the underlying explanation? Davis and Haltiwanger offer several possibilities. One is increased occupational licensing. In the 1950s, only about 5 % of workers needed a government (state or federal) license to do their job. In 2008, that is now 29%. So it can be incredibly hard to reallocate to a new job or sector of work if you have to fulfill some kind of licensing requirement (which could involve up to 2000 hours of training along with fees). Second is a decreased ability of firms to fire-at-will. Starting in the 1980s there were a series of court decisions that made it harder for firms to just fire someone, which makes it both less likely for people to leave jobs, and less likely for firms to hire new people. Both act to lower reallocation between jobs. Third is employer-provided health insurance, which generates some kind of “job lock” where people are unwilling to move jobs because they don’t want to lose, or create a gap in, coverage.

Last is the information revolution which may have had perverse effects on reallocation. We might expect that IT allows more efficient reallocation as people can look for jobs more easily (e.g. Monster.com, LinkedIn) and firms can cast a wider net for applicants. But IT also allows firms to screen much more effectively, as they have access to credit reports, criminal records, and the like, that would have been prohibitive to acquire in the past.

So we appear to have, on two fronts, declining dynamic reallocation in the U.S. This certainly contributes to a slowdown in productivity growth, and may perhaps be a better explanation than “running out of ideas from the IT revolution” that Gordon and Fernald talk about. The big worry is that, if it is regulation-creep, as Davis and Haltiwanger suspect, we don’t know if or when the slowdown in reallocation would end.

In summary, reading John Haltiwanger papers can make you have a bad day.

Age Structure, Experience, Productivity…… and France!

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

Miles Kimball posted a link to a relatively old Scott Sumner post that was discussing a Paul Krugman post from 2011. Which means I am only about 3 years behind, which is good, because I would have estimated I was about 5 years behind.

Anyway, Scott’s post deals with some facts about France. Namely, while GDP per capita in France is only roughly 70% of the U.S. level, GDP per hour worked is essentially equal to that in the U.S. French workers are just as productive per hour as U.S. workers, but just work fewer hours in aggregate.

There are generally two responses to this. The optimistic one: “The French have made a decision to spend their high productivity by taking more vacations and retiring earlier, leading to lower GDP per capita, but probably higher utility.” The pessimistic one: “The French labor system is so mucked up by taxes and regulations that despite being as productive per hour as the U.S., firms do not find it profitable, and workers do not find it desirable, to have more hours provided.”

It’s non-obvious which view is correct. Scott’s post makes two great points, though, about how to think about this. The first is one that I’m not going to deal with here. Comparing France to the U.S. is not an apples to apples comparison. The U.S. is better compared to the EU, or at least Western Europe, as a whole. French productivity looks much worse when compared to New England or the Mid-Atlantic as a region, and only looks good in comparison to the U.S. because the U.S. includes Mississippi and Alabama (which I will arbitrarily call the Sicily and Greece of Europe). It’s a great point.

The second idea that Scott talks about is whether we should be impressed by French output per hour being as high as the U.S. In France, the high youth unemployment rate and early retirement rate mean that the employed population is concentrated in the 30-55 age range. If this age range tends to be particularly productive compared to other age groups, then shouldn’t French output per hour be much higher than in the U.S., where we employ lots of sub-30 and over-55 workers?

Jim Feyrer has a paper from a few years back that looks precisely at the relationship of age structure and measures of productivity. What he finds is that the most productive group of workers are those aged 40-49. An 1% increase in the number of those workers (holding other age groups constant) is associated with about a 0.2% increase in productivity. Ages 50-plus imply lower productivity, but the statistical significance is low. Ages under 39, though, are significantly negative for productivity. Jim uses these relationships to partly explain the productivity slowdown in the US during the 1970s, when the Baby Boomers were filling up the labor force and were still under 40, meaning they were relatively low productivity.

But the results speak to this French question that Scott poses as well. By employing so few under 39-year-olds, France is essentially only using the very high productivity workers in the economy. Thus their GDP per hour is likely inflated by that fact, and their workers are not necessarily just as productive as those in the U.S. What you’d want is some kind of equivalent measure for the U.S. to make this concrete. What is the age-structure-adjusted GDP per hour worked in the U.S. and France? Based on Jim’s results, the U.S. would be ahead in that comparison.

This is related to the well-known result in labor economics that wages rise with labor market experience, but at a decreasing rate. That is, people’s wages always tend to rise with experience, but once you hit about 25-30 years of experience (meaning you are somewhere between 40-55 most likely, the increase gets close to zero. You can see a bunch of these wage/experience relationships in a paper by Lagakos, Moll, Porzio, and Qian, who compare the relationship across countries. One of the features of the data is that in rich countries (like France and the U.S.) the wage/experience relationship is really, really steep when experience is below 10 years. In other words, wages are particularly low for people who have little labor market experience, like young workers aged 18-25.

The U.S. tends to employ a lot more 18-25 year olds as a fraction of our labor force than France. Even prior to 2007, unemployment among those under 25 was roughly 20% in France, and only 10% in the U.S., see here. So the U.S. is employing far more workers that have not yet hit the sweet spot in labor market experience and their wages are very low. On the assumption that wages are some indication of how productive workers are, this means that the U.S. employs proportionately more low-productivity workers. So, again, France’s measured GDP per hour should really be higher than the U.S. level if in fact France and the U.S. have similar productivity levels.

Scott’s point is that we can’t take the equivalence between France’s and the U.S.’s GDP per hour at face value. This doesn’t necessarily mean that the pessimistic view noted above is correct. France could well be making some kind of optimal decision to take lots of leisure time and retirement. But that decision is not one made with the same “budget constraint” as the U.S. – France is very likely not as productive as the U.S.

If you do want to subscribe to the pessimistic viewpoint, then you could argue that not only have French regulations mucked up the labor market, but they have also given the statistical illusion of high productivity. Hence, France is in fact much worse off than the U.S. Even if they fixed their labor market, their GDP per capita would not reach U.S. levels.

Robots as Factor-Eliminating Technical Change

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

A really common thread running through the comments I’ve gotten on the blog involve the replacement of labor. This is tied into the question of the impact of robots/IT on labor market outcomes, and the stagnation of wages for lots of laborers. An intuition that a lot of people have is that robots are going to “replace” people, and this will mean that wages fall and more and more of output gets paid to the owners of the robots. Just today, I saw this figure (h/t to Brad DeLong) from the Center on Budget and Policy Priorities which shows wages for the 10th and 20th percentile workers in the U.S. being stagnant over the last 40 years.

The possible counter-arguments to this are that even with robots, we’ll just find new uses for human labor, and/or that robots will relieve us of the burden of working. We’ll enjoy high living standards without having to work at it, so why worry?

I’ll admit that my usual reaction is the “but we will just find new kinds of jobs for people” type. Even though capital goods like tractors and combines replaced a lot of human labor in agriculture, we now employ people in other industries, for example. But this assumes that labor is somehow still relevant somewhere in the economy, and maybe that isn’t true. So what does “factor-eliminating” technological change look like? As luck would have it, there’s a paper by Pietro Peretto and John Seater called …. “Factor-eliminating Technical Change“. Peretto and Seater focus on the dynamic implications of the model for endogenous growth, and whether factor-eliminating change can produce sustained growth in output per worker. They find that it can under certain circumstances. But the model they set is also a really useful tool for thinking about what the arrival of robots (or further IT innovations in general) may imply for wages and income distribution.

I’m going to ignore the dynamics that Peretto and Seater work through, and focus only on the firm-level decision they describe.

****If you want to skip technical stuff – jump down to the bottom of the post for the punchline****

Firms have a whole menu of available production functions to choose from. The firm-level functions all have the same structure, , and vary only in their value of . and are different factors of production (I’ll be more specific about how to interpret these later on). is a measure of total factor productivity.

The idea of having different production functions to choose from isn’t necessarily new, but the novelty comes when Peretto/Seater allow the firm to use more than one of those production functions at once. A firm that has some amount of and available will choose to do what? It depends on the amount of versus the amount of they have. If is really big compared to , then it makes sense to only use the maximum technology, so . This makes some sense. If you have lots of some factor , then it only makes sense to use a technology that uses this factor really intensely – .

On the other hand, if you have a lot of compared to , then what do you do? You do the opposite – kind of. With a lot of , you want to use a technology that uses this factor intensely, meaning the technology with . But, if you use only that technology, then your sits idle, useless. So you’ll run a -intense plant as well, and that requires a little of the factor to operate. So you’ll use two kinds of plants at once – a intense one and a intense one. You can see their paper for derivations, but in the end the production function when you have lots of is

where is a slurry of terms involving . What Peretto and Seater show is that over time, if firms can invest in higher levels of , then by necessity it will be the case that we have “lots” of compared to little , and we use this production function.

What’s so special about this production function? It’s linear in and , so their marginal products do not decline as you use more of them. More importantly, their marginal products do not rise as you acquire more of the other input. That is, the marginal product of is exactly , no matter how much we have.

What does this possibly have to do with robots, stagnant wages, and the labor market? Let represent labor inputs, and represent capital inputs. This linear production function means that as we acquire more capital (), this has no effect on the marginal product of labor (). If we have something resembling a competitive market for labor, then this implies that wages will be constant even as we acquire more capital.

That’s a big departure from the typical concept we have of production functions and wages. The typical model is more like Peretto and Seater’s case where is really big, and , a typical Cobb-Douglas. What’s true here is that as we get more , the marginal product of goes up. In other words, if we acquire more capital, then wages should rise as workers get more productive.

The Peretto/Seater setting says that, at some point, technology will progress to the point that wages stop rising with the capital stock. Wages can still go up with general total factor productivity, , sure, but just acquiring new capital will no longer raise wages.

While wages are stagnant, this doesn’t mean that output per worker is stagnant. Labor productivity () in this setting is

If capital per worker () is rising, then so is output per worker. But wages will remain constant. This implies that labor’s share of output is falling, as

With the ability to use multiple types of technologies, as capital is acquired labor’s share of output falls.

Okay, this Peretto/Seater model gives us an explanation for stagnant wages and a declining labor share in output. Why did I present this using for capital and for labor, not their traditional and ? This is mainly because the definition of what counts as “labor”, and what counts as “capital”, are not fixed. “Capital” might include human as well as physical capital, and so “labor” might mean just unskilled labor. And we definitely see that unskilled labor’s wage is stagnant, while college-educated wages have tended to rise.

***** Jump back in here if you skipped the technical stuff *****

The real point here is that whether technological change is good for labor or not depends on whether labor and capital (i.e. robots) are complements or substitutes. If they are complements (as in traditional conceptions of production functions), then adding robots will raise wages, and won’t necessarily lower labor’s share of output. If they are substistutes then adding robots will not raise wages, and will almost certainly lower labor’s share of output. The factor-eliminating model from Peretto and Seater says that firms will always invest in more capital-intense production functions and that this will inevitably make labor and capital substitutes. We happen to live in the period of time in which this shift to being substitutes is taking place. Or one could argue that it already has taken place, as we see those stagnant wages for unskilled workers, at least, from 1980 onwards.

What we should do about this is a different question. There is no equivalent mechanism or incentive here that would drive firms to make labor and capital complements again. From the firms perspective, having labor and capital as complements limits their flexibility, because they then depend on the other. They’d rather have the marginal product of robots and people independent of one other. So once we reach the robot stage of production, we’re going to stay there, absent a policy that actively prohibits certain types of production. The only way to raise labor’s share of output once we get the robots is through straight redistribution from robot owners to workers.

Note that this doesn’t mean that labor’s real wage is falling. They still have jobs, and their wages can still rise if there is total factor productivity change. But that won’t change the share of output that labor earns. I guess a big question is whether the increases in real wages from total factor productivity growth are sufficient to keep workers from grumbling about the smaller share of output that they earn.

I for one welcome….you know the rest.

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.

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.

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.

Oberfield and Raval on Capital/Labor Elasticity of Substitution

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

I was in Boston for the NBER summer institute on Friday, sitting in on what it typically called either “Growth day” or “Jones/Klenow” after the organizers. Regardless, here’s the program. It’s a chance to see what is some of the cutting/bleeding edge research in economic growth.

The first paper I saw was by Ezra Oberfield of Princeton and Devesh Raval of the Federal Trade Commission (I missed the Grossman/Helpman paper because I like to sleep, and didn’t get to Boston until 10:15am – sue me). They were doing two things. (1) providing an estimate of the aggregate elasticity of substitution (EOS) between capital and labor and (2) using that to try and account for the decline in labor’s share of income over the last 30-40 years.

On (1), they made the point that the aggregate EOS is not a technological constant, but rather is an artifact of the micro-level EOS. Specifically,

where is the EOS at the plant level. The weighting term reflects the variation in capital shares across firms. is the elasticity of demand for plant output. The demand elasticity is in to account for the fact that some of the response to a change in factor prices is to move demand away from the plants that tend to use the more expensive factor.

Regardless, Ezra and Devesh provide evidence that is really close to zero, so essentially this demand adjustment is negligible, and the aggregate EOS is roughly equivalent to the micro EOS. They estimate this from plant-level data, and find something like 0.52, meaning that capital and labor are not easily substituted for each other. Over time, the aggregate EOS is roughly stable at around 0.70, based on their values for and .

On (2), given their aggregate EOS, the implication is that the decline of labor’s factor share is biased technical change. Increased automation, IT investment, and offshoring, among other things, have driven down labor’s share of output down over time.

Changes in factor prices alone (wages and rental rates) would have raised labor’s share of output over this period, they find. The force of biased technical change was so strong it overcame that tendency.

It’s worth noting how important finding the EOS1, then firms can switch easily from labor to capital. Relatively cheap capital is substituted for labor, and labor’s share drops. If EOS>1, then the decline in labor share is driven in part by more expensive labor, and hence the implied degree of biased technical change is smaller.