# Plows were the Robots of the 13th Century

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

Jury duty this morning, which meant lots of quiet reading time and in the end no *actual* jury duty (yeah for settlements!).

I am reading Rural Economy and Country Life in the Medieval West, by Georges Duby. I came across the following description of how the development of improved harnesses and plows in the Medieval period displaced a large fraction of rural labor (p. 116):

On the other hand, manual laborers without draught animals underwent no technical progress and sustained no rise in yields: on the contrary there was a relative fall in their living conditions…..That the increased value of farming equipment strengthened the hold of the wealthy over the peasantry cannot be denied….Everywhere the lord maintained his authority over his men by helping them to acquire livestock or by threatening them with its confiscation. When in some provinces in the thirteenth century servitude was born anew and flourished, it was the need to acquire agricultural equipment, efficient though costly, which led poorer peasants to bind themselves into dependence. The same needs held them in servitude, for although they had the right to decamp….they could do so only…by giving up their plough animals. In fact because of this, agricultural growth appears to have been a very powerful agent of social differentiation.

A couple of things struck me about the passage. First, the analysis of the disruption caused by the introduction of a new technology embodied in capital goods (plows, harnesses, and horses) sounds similar to some worries regarding the introduction of robots. With capital owned by only a few, those without capital become dependent on the wealthy and have their living standards driven down. Second, innovation favors those with the skills to work with the new technology. Skilled ploughmen – who only got that way by having a team of horses and a plough to begin with – were the high human capital workers of their day.

Mainly, though, it is just an interesting example of how the same issues with innovation, technology, and displacement have been occurring forever. The question of what happens when robots are plentiful is not a question unique to robots, it is a question about how we adapt to disruptive technology. The evidence suggests that whoever owns the technology or the capital associated with it will use it as leverage over those who do not, just like always.

By the way, I think the lady next to me in the jury room would have looked less shocked if I had told her I was reading a porn magazine.

# These are Not the (Modeling Assumptions About) Droids You are Looking For

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

Let me start by saying that all future arguments about robots should use the word “droids” instead so that we can use more Star Wars references.

Benzell, Kotlikoff, LaGarda, and Sachs (BKLS) have a new NBER working paper out on robots (sorry, I don’t see an ungated version). They are attempting to do what needs to be done: provide a baseline model of economic growth that explicitly accounts for the ability of software in the form of robots to replace human workers. With such a baseline model we could then see under what conditions we get what they call “immiserating growth” where we are actually made worse off by inventing robots. Perhaps we could then use the model to test out different policies and see how to alleviate or prevent such immiserating growth.

Thus I am totally on board with the goals of the paper. But I don’t know that this particular model is the right way to think about how robots will affect us in the future. There are several reasons:

Wealth Distribution. The model has skilled workers and unskilled workers, yes, but does not distinguish between those with high initial wealth (capable of saving a lot in the form of robots) from those with little or none. This eliminates the possibility of having wealthy robot owners run the wage down so far that no human is employable anymore. While I don’t think that is going to happen, the model should allow for it so we can see under what conditions that might be right or wrong.

Modeling Code. The actual model of how capital (robots) and code work together seems too crude. Essentially, robots and code work together in a production function. Code today is equal to some fraction of the code from yesterday (the rest presumably becomes incompatible) plus whatever new code we hire skilled workers to write. The “shock” that BKLS study is a dramatic increase in the fraction of code that lasts from one period to the next. In their baseline, zero percent of the code lasts, meaning that to work with capital we have to continually reprogram them. Robotics, or AI, or whatever it is that they are intending to capture then shocks this percent up to 70%.

Is this how we should think about code? Perhaps it is a stock variable we could think about, sure. But is the coming of robots really a positive shock to the persistence of that code? I feel like I can tell an equally valid story about how robots and AI will mean that code becomes less persistent over time, and that we will continually be reprogramming them to suit our needs. Robots, by operating as general purpose machines, can easily be re-programmed every day with new tasks. A hammer, on the other hand, is “programmed” once into a heavy object useful for hitting things and then is stuck doing that forever. The code embedded in our current non-robot tools is very, very persistent because they are built for single tasks. Hammers don’t even have USB ports, for crying out loud.

Treating Code as a Rival Good. Leaving aside the issue of code’s persistence, their choice of production function for goods does not seem to make sense for how code operates. The production function depends on robots/capital (K) and code (A). Given their assumed parameters, the production function is

$\displaystyle Y = K^{\alpha}A^{1-\alpha}, \ \ \ \ \ (1)$

and code is treated like any other rival, exclusive factor of production. Their production function assumes that if I hold the amount of code constant, but increase the number of robots, then code-per-robot falls. Each new robot means existing ones will have less code to work with? That seems obviously wrong, doesn’t it? Every time Apple sells an iPhone I don’t have to sacrifice an app so that someone else can use it.

The beauty of code is precisely that it is non-rival and non-exclusive. If one robot uses code, all the other robots can use it too. This isn’t a problem with treating code as a “stock variable”. That’s fine. We can easily think of the stock of code depreciating (people get tired of apps, it isn’t compatible with new software) and accumulating (coders write new code). But to treat it like a rival, exclusive, physical input seems wrong.

You’re going to think this looks trivial, but the production function should look like the following

$\displaystyle Y = K^{\alpha} A. \ \ \ \ \ (2)$

I ditched the ${(1-\alpha)}$ exponent. So what? But this makes all the difference. This modified production function has increasing returns to scale. If I double both robots and the amount of code, output more than doubles. Why? Because the code can be shared across all robots equally, and they don’t degrade each other’s capabilities.

This is going to change a lot in their model, because now even if I have a long-run decline in the stock of robots ${K}$, the increase in ${A}$ can more than make up for it. I can have fewer robots, but with all that code they are all super-capable of producing goods for us. The original BKLS model assumes that won’t happen because if one robot is using the code, another one cannot.

But I’m unlikely to have a long-run decline in robots (or code) because with IRS the marginal return to robots is rising with the number of robots, and the marginal return to code is rising with the amount of code. The incentives to build more robots and produce more code are rising. Even if code persists over time, adding new code will always be worth it because of the IRS. More robots and more code mean more goods produced in the long-run, not fewer as BKLS find.

Of course, this means we’ll have produced so many robots that they become sentient and enslave us to serve as human batteries. But that is a different kind of problem entirely.

Valuing Consumption. Leave aside all the issues with production and how to model code. Does their baseline simulation actually indicate immiseration? Their measure of “national income” isn’t defined clearly, so I’m not sure what to do with that number. But they do report the changes in consumption of goods and services. We can back out a measure of consumption per person from that. They index the initial values of service and good consumption to 100. Then, in the “immiserating growth” scenario, service consumption rises to 127, but good consumption falls to 72.

Is this good or bad? Well, to value both initial and long-run total consumption, we need to pick a relative price for the two goods. BKLS index the relative price of services to 100 in the initial period, and the relative price falls to 43 in the long-run.

But we don’t want the indexed price, we want the actual relative price. This matters a lot. If the relative price of services is 1 in the initial period, then initial real consumption is

$\displaystyle C = P_s Q_s + Q_g = 1 \times 100 + 100 = 200. \ \ \ \ \ (3)$

In the long-run we need to use the same relative price so that we can compare real consumption over time. In the long-run, with a relative price of services of 1, real consumption is

$\displaystyle C = 1 \times 127 + 72 = 199. \ \ \ \ \ (4)$

Essentially identical, and my guess is that the difference is purely due to rounding error.

Note what this means. With a relative price of services of 1, real consumption is unchanged after the introduction of robots in their model. This is not immiserating growth.

But wait, who said that the relative price of services had to be 1? What if the initial price of services was 10? Then initial real consumption would be ${C = 10 \times 100 + 100 = 1100}$, and long-run real consumption would be ${C = 10 \times 127 + 72 = 1342}$, and real consumption has risen by 22% thanks to the robots!

Or, if you feel like being pessimistic, assume the initial relative price of services is 0.1. Then initial real consumption is ${C = .1 \times 100 + = 110}$, and long-run consumption is ${C = .1 \times 127 + 72 = 84.7}$, a drop of 23%. Now we’ve got immiserating growth.

The point is that the conclusion depends entirely on the choice of the actual relative price of services. What is the actual relative price of services in their simulation? They don’t say anywhere that I can find, they only report the indexed value is 100 in the initial period. So I don’t know how to evaluate their simulation. I do know that their having service consumption rise by 27% and good consumption fall by 28% does not necessarily imply that we are worse off.

Their model is too disconnected from reality (as are most models, this isn’t a BKLS failing) that we cannot simply look at a series from the BLS on service prices to get the right answer here. But we do know that the relative price of services to goods rose a bunch from 1950 to 2010 (see here). From an arbitrary baseline of 1 in 1950, the price of services relative to manufacturing was about 4.33 in 2010. You can’t just plug in 4.33 to the above calculation, but it gives you a good idea of how expensive services are compared to manufacturing goods. On the basis of this, I would lean towards assuming that the relative price of services is bigger than 1, and probably significantly bigger, and that the effect of the BKLS robots is an increase in real consumption in the long-run.

Valuing Welfare. BKLS provide some compensating differential measurements for their immiserating scenario, which are negative. This implies that people would be willing to pay to avoid robots. They are worse off.

This valuation depends entirely on the weights in the utility function, and those weights seem wrong. The utility function they use is ${U = 0.5 \ln{C_s} + 0.5 \ln{C_g}}$, or equal weights on the consumption of both services and goods. With their set-up, people in the BKLS model will spend exactly 50% of their income on services, and 50% on goods.

But that isn’t what expenditure data look like. In the US, services take up about 70-80% of expenditure, and goods only the remaining 20-30%. So the utility function should probably look like ${U = 0.75 \ln{C_s} + 0.25 \ln{C_g}}$. And this changes the welfare impact of the arrival of robots.

Let ${C_g}$ and ${C_s}$ both equal 1 in the baseline, pre-robots. Then for BKLS baseline utility is 0, and in my alternative utility is also 0. So we start at the same value.

With robots, goods consumption falls to 0.72 and service consumption rises to 1.27. For BKLS this gives utility of ${U = 0.5 \ln{1.27} + 0.5 \ln{0.72} = -.045}$. Welfare goes down with the robots. With my weights, utility is ${U = 0.75 \ln{1.27} + 0.25 \ln{0.72} = 0.097}$. Welfare goes up with the robots.

Which is right? It depends again on assumptions about how to value services versus goods. If you overweight goods versus services, then yes, the reduction of goods production in the BKLS scenario will make things look bad. But if you flip that around and overweight services, things look great. I’ll argue that overweighting services seems more plausible given the expenditure data, but I can’t know for sure. I am wary, though, of the BKLS conclusions because their assumptions are not inconsequential to their findings.

What Do We Know. If it seems like I’m picking on this paper, it is because the question they are trying to answer is so interesting and important, and I spent a lot of time going through their model. As I said above, we need some kind of baseline model of how future hyper-automated production influences the economy. BKLS should get a lot of credit for taking a swing at this. I disagree with some of the choices they made, but they are doing what needs to be done. I do think that you have to allow for IRS in production involving code, though. It just doesn’t make sense to me to do it any other way. And if you do that goods production is going to go up, not down, as they find.

The thing that keeps bugging me is that I have this suspicion that you can’t eliminate the measurement problem with real consumption or welfare entirely. This isn’t a failure of BKLS in particular, but probably an issue with any model of this kind. We don’t know the “true” utility function, so there is no way we’ll ever be able to say for sure whether robots will or will not raise welfare. In the end it will always rest on assumptions regarding utility weights.

# Harry Potter and the Residual of Doom

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

The productivity term in an aggregate production function is tough to get one’s head around. When I write down

$\displaystyle Y = K^{\alpha}(AL)^{1-\alpha} \ \ \ \ \ (1)$

for aggregate GDP, the term ${A}$ is the measure of (labor-augmenting) productivity. What exactly does ${A}$ mean, though? Sure, mathematically speaking if ${A}$ goes up then ${Y}$ goes up, but what is that supposed to mean? ${Y}$ is real GDP, so what is this thing ${A}$ that can make real GDP rise even if the stocks of capital (${K}$) and labor (${L}$) are held constant?

I think going to Universal Studios last week provided me with a good example. If you take all the employees (about 12,000 people) and capital (building supplies, etc..) at Universal Studios and set up a series of strip malls along I-4 in Orlando, then you’ll generate a little economic activity between people shopping at the Container Store and eating lunch at Applebee’s. But no one is flying to Orlando to go to those strip malls, and no one is paying hundreds of dollars for the right to walk around and *look* at those strip malls. The productivity, ${A}$, is very low in the sense that the capital and labor do not generate a lot of real GDP.

But call that capital “Diagon Alley” and dress the employees up in funny robes, and it is thick with thousands of people like me shelling out hundreds of dollars just for the right to walk around a copy of a movie set based on a book. Hundreds. Each.

This is pure productivity, ${A}$. The fictional character Harry Potter endows that capital and labor in Orlando with the magical ability to generate a much higher level of real GDP. No Harry Potter, no one visits, and real GDP is lower. The productivity is disembodied. It’s really brilliant. Calling this pile of capital “Gringotts” and pretending that the workers are wizard guards at a goblin bank creates real economic value. Economic transactions occur that would otherwise not have.

We get stuck on the idea that productivity, ${A}$, is some sort of technological change. But that is such a poor choice of words, as it connotes computers and labs and test tubes and machines. Productivity is whatever makes factors of production more productive. That is pretty great, because it means that we need not hinge all of our economic hopes on labs or computers. But it also stinks, because it means that you cannot pin down precisely what productivity is. It is necessarily an ambiguous concept.

A few further thoughts:

• It doesn’t matter what is bought/sold, real GDP is real GDP. Spending 40 dollars at Universal to buy an interactive wand at Ollivander’s counts towards GDP just the same as spending 40 dollars on American Carbide router bits (We bought two. Wands, not router bits). There is no such thing as “good” GDP or “bad” GDP. Certain goods (tools!) do not count extra towards GDP because you can fix something with them.
• Yes, you can create economic value out of “nothing”. Someone, somewhere, is writing the next Harry Potter or Star Wars or Lord of the Rings, and it is going to create significant productivity gains as someone else builds the new theme park, or lunch box, or action figure. This new character or story will endow otherwise unproductive capital and labor with the ability to produce GDP at a faster rate than before. ${A}$ will go up just from imagining something cool.
• This kind of productivity growth makes me think that we won’t necessarily end up working only 10 or 12 hours a week any time soon. The Harry Potter park doesn’t work without having lots of people walking around in robes playing the roles. It’s integral to the experience. So we pay to have those people there. Those people, in turn, pay to go see a Stones concert, where it is integral to have certain people working (Keith and Mick among others). We keep trading our time with each other to entertain ourselves. Markets are really efficient ways of allocating all of these entertainers to the right venues, times, etc.. so it wouldn’t surprise me if we all keep doing market work a lot of our time in the future.
• “Long-tail” creative productivity gains like Harry Potter exacerbate inequality, maybe more than robots ever will. You can buy shares in the robot factory, even in a small amount. But you cannot own even a little bit of Harry Potter. You can’t copy it effectively (*cough* Rick Riordan *cough*). So J.K. Rowling gets redonkulously rich because ownership of the productivity idea is highly concentrated.

# 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. # The Industrial Revolution and Modern Development NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site. I’m not an economic historian, but like most growth economists I am an avid consumer of economic history. Maybe it’s our version of “physics envy”. Regardless, it isn’t always obvious why growth economists look backwards so much for motivation, examples, and inspiration. Let me try to give an example of the usefulness of economic history by looking at recent “big theories” of the British Industrial Revolution (IR). If you have any interest in learning about the IR, then you could do a lot worse than reading the following two books: Mokyr’s theory is that there was a unique intellectual environment created in Britain during the Enlightenment, and that this generated cultural conditions that valued innovation as a valuable activity in and of itself, as well as a supply of trained engineers that took advantage of these conditions. What made the IR British was its adoption of science and reason as tools of economic progress. Allen’s theory has to do with relative factor prices. The IR was British because Britain had a unique combination of high wages (persisting after the Black Death) and low fuel costs (due to cheap coal) that made labor-saving and fuel-using innovations (e.g. the steam engine) profitable. Other countries failed to adopt, or lagged in adopting, because they had different relative prices for labor and fuel. There is some sense that these two have set up competing explanations of the Industrial Revolution, diametrically opposed. Mokyr does tend to downplay the “coal made the IR” idea. Allen does tend to downplay the notion that Britain was unique in its potential for innovation. But there is more subtlety to their arguments than that. The theories do not contradict each other, because they are fundamentally concerned with explaining different phenomenon. There are two different questions about the IR in Britain that we want to answer. First, why did several particularly important innovations take place in Britain, and not in other places? Second, of all the innovations available, why were they adopted first (or with greater speed) in Britain than in other areas of Europe? Mokyr’s theory is very much an answer to the first question, and provides a sound answer to the second. Newcomen and Watt and Arkwright and Darby and Hargreaves were all British. Perhaps more important than these noted innovators, according to Mokyr, is the small army of highly skilled engineers that patiently but steadily made improvements to the steam engine, spinning jenny, coke smelting, and other technologies. What set Britain apart from China (where most of the big innovations had occurred earlier) or France (which quickly had knowledge of the big innovations) were those engineers. Without them, you have curiosities. With them, you have industrialization. Britain led the IR because the Enlightenment took hold and produced both the original innovators and that army of engineers. Allen’s theory is very much an answer to the second question, but is relatively weak on the first. That is, we can use factor prices to understand why Britain adopted the steam engine or spinning jenny first, but they don’t explain why those things were invented in Britain. Allen suggests that those same factor prices played a role in inducing innovation, but that is shakier ground. Anton Howes just posted a reaction to Allen’s work that focuses precisely on that failure. So Mokyr’s theory is more comprehensive, but it lacks a compelling explanation for the failure of other countries to follow Britain quickly into industrialization. Allen’s work is really a theory of growth and development, articulated with examples from the British IR. We can easily adopt his concepts for other time periods and places, whereas Mokyr’s work is far more context-specific. Thus Allen’s theory is more relevant than Mokyr’s to thinking about the general process of development. The second question above – why do some places fail to adopt or lag in adopting new innovations? – is in some sense the central question of development. Research on development has been focusing a lot lately on the distribution of productivity across firms (see my reading list on misallocation). In China, India, or Mexico, for example, the ratio of labor productivity of the top firms to bottom firms is on the order of 10-1 or more. Even in the U.S. there are productivity gaps of something like 2-1 between the best and worst firms. Not all firms use the best techniques. Poor countries have particularly bad distributions, with the vast majority of their firms using low productivity technologies. If we could understand that distribution, we could understand a lot about the gap in income per capita between poor and rich countries. So far, most of the explanations hinge on firms facing some implicit distortion to factor costs, which makes them choose a sub-optimal level of inputs. Firms that may be very productive perhaps face high distortions, making factors expensive, and leading them to be too small. Firms that are unproductive face low distortions, making factors cheap, leading them to be too big. What this literature could learn from Allen is that the choice of technology itself is in play when factor prices are distorted. In particular, distortions that change the costs of materials relative to capital or labor could be instrumental in keeping firms from adopting leading technologies in poor countries. Cheap labor may make a firm inefficiently large in a poor country, yes. But it also removes the incentive to adopt a capital-using, labor-saving high technology production technology, even if the firm has full knowledge of the technology. This isn’t a brand new idea by Allen. Hicks talked about it in 1932. Hayami and Ruttan talked about induced innovation and the choice of technology with respect to agriculture in developing countries long ago. Banerjee and Duflo’s chapter on distortions considers the role of borrowing constraints (i.e. expensive capital) in generating a fat tail of small labor-intense firms in India. Daron Acemoglu‘s theory of directed technical change is basically induced innovation based on differentials in factor prices. Allen, though, provides a clear and compelling story about the power of factor prices in technology adoption. Think of his work as a “proof of concept” that induced innovation has a lot of explanatory power for differences between rich and poor countries. It is an excellent example of how studying economic history can produce insights into modern questions about development and growth. Factor price differences created decades-long lags in technology adoption across Europe, perhaps we shouldn’t be surprised at decades-long delays in adoption in developing countries. Relative factor prices may be a worthwhile avenue to explore, possibly as the lever on which institutions (hypocrite!?) or geography push to generate differences in living standards. [I appear to have slighted Mokyr’s work here in favor of Allen, but right now someone else is reading his book and gleaning from it some idea about culture and development that I missed completely. From the growth economist’s perspective, the purpose is not to decide who’s right in these economic history debates, it is to mercilessly steal all the good ideas.] # 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.

# Perfect Competition is Bad for Growth

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

You have to be careful in confusing “free markets” with “perfect competition”. By “free markets”, I think we mean free entry for new firms and/or products into the market. We don’t want restrictions on innovators from bringing their ideas to the market. We typically *assume* that free entry exists in economic models, but one thing that holds back development may be the absence of this free entry (think red tape and bad institutions).

But we don’t want “perfect competition” even if we do want “free markets”. One of the counter-intuitive things that comes up in growth courses is that perfect competition is not conducive to rapid growth. The story here involves a few steps

• Growth is ultimately driven by innovation
• People will innovate if they have incentives to innovate
• The incentive to innovate comes from economic profits
• Profits only exist when the innovator or firm has some market power

Innovators and/or firms need to charge a price greater than marginal cost to earn profits, otherwise there will be no incentive to innovate, and ultimately no growth. If you allow competitors to copy innovations they will drive the price down to marginal cost, eliminating profits and incentives for innovation. We want free entry of new firms with market power, but not free entry of imitators who produce perfect competition.

But perfect competition does maximize the combined consumer and producer surplus from a given product. So there is a tension here. Perfect competition maximizes the output of *existing* products, but minimizes the output from *potential* products. Think of it this way, if we decided that we had all the types of goods and services that we could ever want, then we’d want to enforce perfect competition. We would nullify every patent, and let competition take over to maximize the output of those existing goods and services. Nullifying patents (or any other kind of intellectual property) would crush the incentives to innovate, and we’d never get any new products.

This means that it is not obvious what the right policy is for intellectual property rights and/or competition in general. It depends on your long-run perspective. You can trade off long-run growth for a higher level of current output by canceling intellectual property rights. Or you can trade off current output for a higher long-run growth rate by enforcing property rights strictly, and probably instituting even stronger ones.

There is no *right* answer here, because it depends on your time preferences. But extreme answers are probably unlikely to be optimal for anyone. Strict perfect competition – allowing imitators to ensure P=MC – isn’t good because it prevents us from getting new products. Super strong market power – limiting each good to being produced by a perpetual monopolist, say – would shrink the availability of every existing product, even if it makes the incentive to innovate huge.

# Latitude and Income per Capita in Comparative Development

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

New paper out by Holger Strulik and Carl-Johan Dalgaard (who I predict is at this moment taking a smoke break). The paper looks at the reversal of the latitude/income relationship over history, and propose a physiological reason for it.

For starters, if you are familiar at all with basic development statistics, then you probably know that latitude and income per capita are positively correlated. The farther away from the equator you get (higher latitude) the richer you get. This works going north or south. South Africa is richer than Nigeria, for example, and Chile is richer than Ecuador. Dalgaard and Strulik have a nice graph showing this relationship holds not only for all countries, but also within Europe.

The first really interesting fact in the paper is that this gradient reverses if you look at pre-Industrial Revolution data. For 1500 CE, there is a negative relationship, and countries that are closer to the equator are richer. Again, this also holds within Europe. I had some vague concept that it probably reversed in the whole sample, but the within Europe evidence is really fascinating.

Around 1500, the Mediterranean countries in Europe were better off compared to their Northern neighbors. An aside: Were the Greeks and Italians of 1500 tsk-tsk-ing their profligate Teutonic cousins for their lazy attitudes and lack of robust economic institutions? Discuss.

Anyway, the latitude/income reversal, and the fact that it holds up within Europe, are both by themselves the kinds of stylized facts that you should cram into your head when thinking about comparative development.

But given that you have crammed that information in there, you probably have several questions. (1) Why are hot places rich in 1500, and cold places poor? (2) What changed to make the cold places rich today?

Dalgaard and Strulik take a swipe at these questions, focusing on the physiology involved in hot and cold places. There thesis rests on “Bergmann’s Rule”, which is a biological regularity noted in 1847. Bergmann’s rule states that average body mass of organisms rises as they get farther from the equator. This holds for people as well as animals. People generally have higher body mass farther from the equator (and no, that’s not just because of Wisconsin. I kid. Sort of.).

Why does Bergmann’s rule hold? Surface area to mass ratios. Big people have lower surface area to mass ratios, so they are more thermally efficient in cold climates. Thus the optimal body type for high, cold, latitudes is large, while for places closer to the equator small body types are optimal to maximize surface area to mass in order to radiate heat.

Now, large bodies have an additional feature. They require a lot of fuel (food), in particular for mothers when pregnant. Big women having big babies means using a lot of food. Thus people in cold latitudes were able to have fewer babies, given a supply of food, than their peers in equatorial regions. So we have bigger populations in equatorial regions and smaller ones in cold latitudes prior to the IR. Big populations mean more innovation in almost any type of growth model you write down, so equatorial regions had more innovation during the pre-IR era, and hence were richer.

But, eventually even the cold latitudes are going to innovate far enough to get the point of inventing technologies that rely on human capital. And the cold climate physiology gives them a natural tendency to favor quality over quantity of kids. Thus families in higher latitudes are going to more easily adopt the human capital using technology. This then starts a feedback effect, where by having a few, high-education kids means they can use the human-capital technology. Which raises income per capita. Which leads to further investment in kids at the expense of family size, and cold latitudes enter the Demographic Transition ahead of equatorial regions.

The reversal is inevitable in their model, given the initial physiological difference between latitudes. The physiological story is also consistent with differences in marriage patterns and child birth patterns between Europe and much of Asia in the pre-IR era.

They use Europe as an example, and how the latitude/income relationship holds today. But it holds in the U.S. as well. Is the income per capita of states in the U.S. consistent with the implied physiological differences between different areas of Europe, Africa, or Latin America due to population composition?

This paper predicts a reversal, but this reversal has to happen “just so” to avoid becoming a-historical. That is, the reversal has to happen just before the equatorial countries (China, India, various iterations of Islamic empires) become sufficiently rich to colonize Europe, snuffing out their development. This leaves Europe to effect the reversal, and go out to colonize the rest of the world. Did Europe get lucky here, or is there some reason that those places don’t become colonizers? Luck might be the answer, as you’ve got plenty of close-run things in European history [the Mongols turning back, Lepanto, Vienna].

The last thing that comes to mind here is that for this physiological difference to have such persistent effects, the family patterns it determines must become either (a) genetically rooted into populations or (b) some deeply ingrained in culture as to be permanent. Fertility behavior is mutable. For it to continue to be a reason for lack of development in equatorial regions you need some strong force keeping people locked into the “bad” preference for lots of kids. What is that force?

# Scale, Profits, and Inequality

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

After my post last week on inequality, I got a number of (surprisingly reasonable) responses. I pulled one line out of a recent comment, not to call out that particular commenter, but because it encapsulates an argument for *not* caring about inequality.

Gates and the Waltons really did probably add more value to humanity than the janitor at my school.

The general argument here is about incentives. Without the possibility of massive profits, people like Bill Gates or Sam Walton will not bother to innovate and create Microsoft and Walmart. So we should not raise taxes because those people deserve, in some sense, the fruits of their genius. More important, without them innovating, the economy wouldn’t grow.

But if we take seriously the incentives behind innovation, then it isn’t simply the genius of the individual that matters for growth. The scale of the economy is equally relevant. In any typical model of innovation and growth, the profits of a firm are going to be something like Profits = Q(Y)(P-MC), where (P-MC) is price minus marginal cost. Q(Y) is the quantity sold, and this depends on the aggregate size of the economy, Y.

The markup of price over marginal cost (P-MC), is going to depend on how much market power you have, and on the nature of demand for your product. This markup depends on your individual genius, in the sense that it depends on how indispensable people find your product. Apple is probably the better example here. They sell iPhones for way over marginal cost because they’ve convinced everyone through marketing and design that substitutes for iPhones are inferior.

The scale term, Q(Y) does not depend on genius. It depends on the size of the market you have to sell to. If we stuck Steve Jobs, Jon Ive, and some engineers on a remote island, they wouldn’t earn any profits no matter how many i-Devices they invented, because there would be no one to sell them to.

People like Gates and the Waltons earn profits on the scale effect of the U.S. economy, which they did not invent, innovate on, or produce. So the “rest of us”, like the janitor mentioned above, have some legitimate reason to ask whether those profits are best used in remunerating Bill Gates and the Walton family, or could be put to better use.

There isn’t necessarily any kind of efficiency loss from raising taxes on Gates, Walton, and others with large incomes. They may, on the margin, be slightly less willing to innovate. But if the taxes are put to use expanding the scale of the U.S. economy, then we might easily increase innovation by through the scale effect on profits. Investing in health, education, and infrastructure all will raise the aggregate size of the U.S. economy, and make innovation more lucrative. Even straight income transfers can raise the effective scale of the U.S. economy be transferring purchasing power to people who will spend it.

Can we argue about exactly how much of the profits are due to “genius” (the markup) and how much to scale? Sure, there is no precise answer here. But you cannot dismiss the idea of taxing high-income “makers” because their income represents the fruits of their individual genius. It doesn’t. Their incomes derive from a combination of ability and scale. And scale doesn’t belong to individuals.

The value-added of “the Waltons” is particularly relevant here. Sam Walton innovated, but the profits of Walmart are almost entirely derived from the scale of the U.S. (and world) economy. It’s the presence of thousands and thousands of those janitors in the U.S. that generates a huge portion of Walmart’s profits, not the Walton family’s unique genius.

Alice Walton is worth around \$33 billion. She never worked for Walmart. She is a billionaire many times over because her dad was smart enough to take advantage of the massive scale of the U.S. economy. I’m not willing to concede that Alice has added more value to humanity than anyone in particular. So, yes, I’ll argue that Alice should pay a lot more in taxes than she does today. And no, I’m not afraid that this will prevent innovation in the future, because those taxes will help expand the scale of the economy and incent a new generation of innovators to get to work.

# Productivity Pessimism from Productivity Optimists

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

The projected future path of labor productivity in the U.S. is perhaps the most important input to the projected future path of GDP in the U.S. There are lots of estimates floating around, many of them pessimistic in the sense that they project labor productivity growth to be relatively slow (say 1.5-1.8% per year) over the next few decades compared to the relatively fast rates (roughly 3% per year) seen from 1995-2005. Robert Gordon has laid out the case for low labor productivity growth in the future. John Fernald has documented that this slowdown probably pre-dates the Great Recession, and reflects a loss of steam in the IT revolution starting in about 2007. This has made Brad DeLong sad, which seems like the appropriate response to slowing productivity growth.

An apparent alternative to that pessimism was published recently by Byrne, Oliner, and Sichel. Their paper is titled “Is the IT Revolution Over?”, and their answer is “No”. They suggest that continued innovation in semi-conductors could make possible another IT boom, and boost labor productivity growth in the near future above the pessimistic Gordon/Fernald rate of 1.5-1.8%.

I don’t think their results, though, are as optimistic as they want them to be. A different way of saying this is: you have to work really hard to make yourself optimistic about labor productivity growth going forward. In their baseline estimate, they end up with labor productivity growth of 1.8%, which is slightly higher than the observed rate of 1.56% per year from 2004-2012. To get themselves to their optimistic prediction of 2.47% growth in labor productivity, they have to make the following assumptions:

1. Total factor productivity (TFP) growth in non-IT producing non-farm businesses is 0.62% per year, which is roughly twice their baseline estimate of 0.34% per year, and ten times the observed rate from 2004-2012 of 0.06%.
2. TFP growth in IT-producing industries is 0.46% per year, slightly higher than their baseline estimate of 0.38% per year, and not quite double the observed rate from 2004-2012 of 0.28%
3. Capital deepening (which is just fancy econo-talk for “build more capital”) adds 1.34% per year to labor productivity growth, which is one-third higher than their baseline rate of 1.03% and, and double the observed rate from 2004-2012 of 0.74%

The only reason their optimistic scenario doesn’t get them back to a full 3% growth in labor productivity is because they don’t make any optimistic assumptions about labor force quality/participation growth.

Why these optimistic assumptions in particular? For the IT-producing industries, the authors get their optimistic growth rate of 0.46% per year by assuming that prices for IT goods (e.g. semi-conductors and software) fall at the fastest pace observed in the past. The implication of very dramatic price declines is that productivity in these sectors must be rising very quickly. So essentially, assume that IT industries have productivity growth as fast as in the 1995-2005 period. For the non-IT industries, they assume that faster IT productivity growth will raise non-IT productivity growth to it’s upper bound in the data, 0.62%. Why? No explanation is given. Last, the more rapid pace of productivity growth in IT and non-IT will induce faster capital accumulation, meaning that its rate rises to 1.34% per year. This last point is one that comes out of a simple Solow-type model of growth. A shock to productivity will temporarily increase capital accumulation.

In the end here is what we’ve got: they estimate labor productivity will grow very fast if they assume labor productivity will grow very fast. Section IV of their paper gives more detail on the semi-conductor industry and the compilation of the price indices for that industry. Their narrative explains that we could well be under-estimating how fast semi-conductor prices are falling, and thus under-estimating how fast productivity in that industry is rising. Perhaps, but this doesn’t necessarily imply that the rest of the IT industry is going to experience rapid productivity growth, and it certainly doesn’t necessarily imply that non-IT industries are going to benefit.

Further, even rapid growth in productivity in the semi-conductor industry is unlikely to create any serious boost to US productivity growth, because the semi-conductor industry has a shrinking share of output in the U.S. over time. The above figure is from their paper. The software we run is a booming industry in the U.S., but the chips running that software are not, and this is probably in large part due to the fact that those chips are made primarily in other countries. If you want to make an optimistic case for IT-led productivity growth in the U.S., you need to make a software argument, not a hardware argument.

I appreciate that Byrne, Oliner, and Sichel want to provide an optimistic case for higher productivity growth. But that case is just a guess, and despite the fact that they can lay some numbers out to come up with a firm answer doesn’t make it less of a guess. Put it this way, I could write a nearly exact duplicate of their paper which makes the case that expected labor productivity growth is only something like 0.4% per year simply by using all of the lower bound estimates they have.

Ultimately, there is nothing about recent trends in labor productivity growth that can make you seriously optimistic about future labor productivity growth. But that doesn’t mean optimism is completely wrong. That’s simply the cost of trying to do forecasting using existing data. You can always identify structural breaks in a time series after the fact (e.g. look at labor productivity growth in 1995), but you cannot ever predict a structural break in a time series out of sample. Maybe tomorrow someone will invent cheap-o solar power, and we’ll look back ten years from now in wonder at the incredible decade of labor productivity growth we had. But I can’t possibly look at the existing time series on labor productivity growth and get any information on whether that will happen or not. Like it or not, extrapolating current trends gives us a pessimistic growth rate of labor productivity. Being optimistic means believing in a structural break in those trends, but there’s no data that can make you believe.