Just a quick reminder:
Just a quick reminder:
Hal Varian writing at the IMF:
Intelligent Technology: A computer now sits in the middle of virtually every economic transaction in the developed world. Computing technology is rapidly penetrating the developing world as well, driven by the rapid spread of mobile phones. Soon the entire planet will be connected, and most economic transactions worldwide will be computer mediated.
Data systems that were once put in place to help with accounting, inventory control, and billing now have other important uses that can improve our daily life while boosting the global economy.
Computer mediation can impact economic activity through five important channels.
Data collection and analysis: ...
Personalization and customization: ...
Experimentation and continuous improvement: ...
Contractual innovation: ...
Coordination and communication: ...
Putting it all together
Today’s mobile phones are many times more powerful and much less expensive than those that powered Apollo 11, the 1969 manned expedition to the moon. These mobile phone components have become “commoditized.” Screens, processors, sensors, GPS chips, networking chips, and memory chips cost almost nothing these days. You can buy a reasonable smartphone now for $50, and prices continue to fall. Smartphones are becoming commonplace even in very poor regions.
The availability of those cheap components has enabled innovators to combine and recombine these components to create new devices—fitness monitors, virtual reality headsets, inexpensive vehicular monitoring systems, and so on. The Raspberry Pi is a $35 computer designed at Cambridge University that uses mobile phone parts with a circuit board the size of a pack of playing cards. It is far more powerful than the Unix workstations of just 15 years ago.
The same forces of standardization, modularization, and low prices are driving progress in software. The hardware created using mobile phone parts often uses open-source software for its operating system. At the same time, the desktop motherboards from the personal computer era have now become components in vast data centers, also running open-source software. The mobile devices can hand off relatively complex tasks such as image recognition, voice recognition, and automated translation to the data centers on an as-needed basis. The availability of cheap hardware, free software, and inexpensive access to data services has dramatically cut entry barriers for software development, leading to millions of mobile phone applications becoming available at nominal cost.
The productivity puzzle
I have painted an optimistic picture of how technology will impact the global economy. But how will this technological progress show up in conventional economic statistics? Here the picture is somewhat mixed. Take GDP, for example. This is usually defined as the market value of all final goods and services produced in a given country in a particular time period. The catch is “market value”—if a good isn’t bought and sold, it generally doesn’t show up in GDP.
This has many implications. Household production, ad-supported content, transaction costs, quality changes, free services, and open-source software are dark matter as far as GDP is concerned, since technological progress in these areas does not show up directly in GDP. Take, for example, ad-supported content, which is widely used to support provision of online media. In the U.S. Bureau of Economic Analysis National Economic Accounts, advertising is treated as a marketing expense—an intermediate product—so it isn’t counted as part of GDP. A content provider that switches from a pay-per-view business model to an ad-supported model reduces GDP.
One example of technology making a big difference to productivity is photography. Back in 2000, about 80 billion photos were taken worldwide—a good estimate since only three companies produced film then. In 2015, it appears that more than 1.5 trillion photos were taken worldwide, roughly 20 times as many. At the same time the volume exploded, the cost of photos fell from about 50 cents each for film and developing to essentially zero.
So over 15 years the price fell to zero and output went up 20 times. Surely that is a huge increase in productivity. Unfortunately, most of this productivity increase doesn’t show up in GDP, since the measured figures depend on the sales of film, cameras, and developing services, which are only a small part of photography these days.
In fact, when digital cameras were incorporated into smartphones, GDP decreased, camera sales fell, and smartphone prices continued to decline. Ideally, quality adjustments would be used to measure the additional capabilities of mobile phones. But figuring out the best way to do this and actually incorporating these changes into national income accounts is a challenge.
Even if we could accurately measure the number of photos now taken, most are produced at home and distributed to friends and family at zero cost; they are not bought and sold and don’t show up in GDP. Nevertheless, those family photos are hugely valuable to the people who take them.
The same thing happened with global positioning systems (GPS). In the late 1990s, the trucking industry adopted expensive GPS and vehicular monitoring systems and saw significant increases in productivity as a result. In the past 10 years, consumers have adopted GPS for home use. The price of the systems has fallen to zero, since they are now bundled with smartphones, and hundreds of millions of people use such systems on a daily basis. But as with cameras, the integration of GPS with smartphones has likely reduced GDP, since sales of stand-alone GPS systems have fallen.
As in the case of cameras, this measurement problem could be solved by implementing a quality adjustment for smartphones. But it is tricky to know exactly how to do this, and statistical agencies want a system that will stand the test of time. Even after the quality adjustment problem is worked out, the fact that most photos are not exchanged for cash will remain—that isn’t a part of GDP, and technological improvements in that area are just not measured by conventional statistics.
Will the promise of technology be realized?
When the entire planet is indeed connected, everyone in the world will, in principle, have access to virtually all human knowledge. The barriers to full access are not technological but legal and economic. Assuming that these issues can be resolved, we can expect to see a dramatic increase in human prosperity.
But will these admittedly utopian hopes be realized? I believe that technology is generally a force for good—but there is a dark side to the force (see “The Dark Side of Technology,” in this issue of F&D). Improvements in coordination technology may help productive enterprises but at the same time improve the efficiency of terrorist organizations. The cost of communication may drop to zero, but people will still disagree, sometimes violently. In the long run, though, if technology enables broad improvement in human welfare, people might devote more time to enlarging the pie and less to squabbling over the size of the pieces.
Jason Furman at ProMarket:
Productivity, Inequality, and Economic Rents: Productivity growth—a necessary (though not sufficient) condition for rising incomes in the long run—has slowed since 1973... At the same time, inequality in the United States is higher and, in recent decades, has risen faster than in other major advanced economies. ...
These dual trends ... have many distinct sources, but insofar as they have some causes in common, there is the potential to address these causes in ways that simultaneously improve efficiency and equity. To this end, the evidence that a rise in rents is contributing to both phenomena is important. ...
The good news is that to the degree that the “rents” interpretation is correct, it suggests that it is possible to reduce inequality and promote productivity growth without hurting efficiency by changing how rents are divided—or even that it is possible to do both while increasing efficiency by acting to reduce rents in the economy. Policies that raise the minimum wage and provide greater support for collective bargaining can help level the playing field for workers in negotiations with employers, in turn changing the way that rents are divided. Measures that would rationalize licensing requirements for employment, reduce zoning and other land-use restrictions, appropriately balance intellectual property regimes, and change the incentives that have led to the expansion of the financial sector as a share of the economy would all help curb excessive rents.
Additional measures that would reduce the scope and unequal distribution of economic rents include the promotion of competition through rulemaking and regulations, as well as the elimination of regulatory barriers to competition. ...
The bad news, however, is that rents have beneficiaries and these beneficiaries fight hard to keep and expand their rents. As a result, political reforms and other steps aimed at curbing the influence of regulatory lobbying are important for reducing the ability of people and corporations to seek rents successfully. Such actions would help ensure that economic growth in the decades ahead is robust, sustainable, and widely shared.
At MoneyWatch (they choose the titles, not me):
The economic "disease" eating away at the U.S.: It's no secret that productivity -- an essential driver for economic growth -- has weakened in recent years in the U.S. and around the world. Less evident is what, if anything, can be done about it.
"Productivity isn't everything, but in the long run it is almost everything," Nobel Laureate economist Paul Krugman wrote in "The Age of Diminishing Expectations." "A country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker." ...
Recent data suggest that pattern is likely to continue for some time.
Over the first three months of the year, productivity increased 0.6 percent. That's below the 1.1 percent rate of growth since the end of the recession, and far below the 2.3 percent average rate of growth from 1990 to the onset of the Great Recession.
What does seem to be a secret is how to fix the problem... What can be done to boost productivity? ...
The productivity slump and what to do about it: ...today, I’d like to explore ... the significant downshift in productivity growth. ... I’m afraid the slowdown is real... I think there are 5 reasons: slower growth of capital per worker, slower TFP, capital misallocation, the absence of full employment, and dysfunctional government (labor quality has been pretty constant, so it isn’t much implicated in the slowdown). ...
First, as I detail here, there’s The Big Short problem: we have been misallocating capital to non-productive finance. ...
Second, we are failing to tap a full employment productivity multiplier (FEPM). Among the pantheon of wrong-headed economic theories is the one that says: firms failing to operate at the edge of their productivity potential will be competed out of business by more productive firms. Unfortunately, in slack labor and credit markets, inefficient firms can handily maintain profit margins by squeezing workers and rolling over cheap loans. At full employment, workers have more bargaining clout, labor costs go up, and inefficiencies become more costly (Josh Bivens agrees and offers some evidence.)
Third, analysis by Barry Eichengreen et al suggests that dysfunctional government eventually grinds down productivity growth. This strikes me as intuitive: an $18 trillion economy requires a government that can efficiently diagnose problems and prescribe solutions in areas of climate, infrastructure, education, innovation, social insurance, poverty and more. Our government, on the other hand, tends to engage in aimless votes to defund Obamacare and shudder the EPA and IRS.
So, how can we better allocate capital, move toward full employment and restore functional government? ... A deep infrastructure dive ... would be a big twofer, both on the productivity and full employment fronts. And by tightening the job market, there’d be positive feedback impact from the FEPM. ...
But here’s another idea with multiple benefits in this space: pay for these productivity enhancing investments with a small tax on financial transactions. That would both raise revenues needed for public investment and raise the cost of non-productive, “noise” trading. ...
Technology versus the Distribution of Workers in Aggregate Productivity: There was a recent post by an engineer rebutting Robert Gordon’s (and others) thesis that technological change was slowing down. The evidence cited is a series of plots and figures showing how specific technologies (battery storage, energy efficiency, computer speed, etc..) are advancing just as fast as they have for decades, if not faster. And there were a number of responses along the lines of “See, Gordon is wrong!”.
The mistake here is that this doesn’t constitute evidence that Gordon is wrong. But the mistake is partly forgivable because Gordon himself indulges in these kinds of anecdotal arguments to advance his thesis, and so it seems as if you could refute his conclusions by offereing alternative anecdotes.
But the important part of Gordon’s argument is not that specific technologies are or are not advancing. It is that aggregate productivity growth is slowing down. And aggregate productivity growth depends not only on individual technologies, but crucially on the distribution of workers using those technologies. Arguing about only those individual technologies is like using an increase in the price of milk to argue that inflation must be high.
Aggregate productivity growth depends on what we can call “within-sector” growth, which is going to be tied closely to those individual technologies. But it also depends on “across-sector” growth, which is tied to the movement of workers from one sector (or job) to another. If workers are shifting from high- to low-productivity sectors or jobs, then aggregate productivity growth may fall even though nothing happened to actual technological change.
I’ll make this more clear below, but here’s a quick summary of what I’ll try to establish. Gordon’s critics could well be right about their individual technologies, and yet wrong about this having anything to do with aggregate growth, because those sectors may not employ many people. And Gordon can be right about aggregate growth, but wrong about individual technologies stagnating, because the movement of workers to low-productivity sectors may be dragging down growth. In short, you cannot talk about aggregate productivity growth without talking about both technology and the distribution of workers across sectors.
Concentration and Growth:
A whole raft of posts and articles (here, here, here, here) has shown up recently regarding “rent-seeking”. This is kind of a catch-all for increased concentration within industries, more lenient anti-trust enforcement, and the increasing share of corporate profits in output. This is then tied to the slowdown in wages over the last few decades, increasing inequality, and even to slower aggregate growth.
The arguments made by those doing the concentrating and profit-making is that they are promoting efficiency, productivity, and hence growth. Do we know whether concentration or rent-seeking would be good or bad?
I’m going to draw a lot on one of my professors in grad school, Peter Howitt, who along with Philippe Aghion is really one of the godfathers of studying competition and growth. ...
The internet and the productivity slump: How much would an average American, whose annual disposable income is $42,300, need to be paid in order to be persuaded to give up their mobile phone and access to the internet, for a full year? ... The question is relevant to a much more familiar issue. Why has productivity growth slowed down so much in all major economies...
Chad Syverson reckons that the unrecorded value of the digital economy to the average citizen would need to be $8400 per year in order to explain the entire productivity gap. This is one fifth of net disposable income per person. He suggests, on prima facie grounds, that few people would value their access to the digital economy at one fifth of their disposable income.
Maybe, but what is the appropriate figure? ... Faced with the choice, I doubt whether they would be prepared to be transported back to the obsolete technology of a decade ago in exchange for an annual payment of less than, say, a few thousand dollars a year...
If that conjecture (and it is no more than a conjecture) is valid, there might be something in the mismeasurement hypothesis after all. But the evidence suggests that it is not the main reason for the productivity debacle .
Barriers to productivity growth: “The limits to productivity growth are set only by the limits to human inventiveness” says John Kay. This understates the problem. There are other limits. I’d mention two which I think are under-rated.
One is competition. Of course, this tends to increase productivity in many ways. But it has a downside. The fear of competition from future new technologies can inhibit investment today: no firm will spend £10m on robots if they fear a rival will buy better ones for £5m soon afterwards. ...
The second is that, as Brynjolfsson and MacAfee say, "significant organizational innovation is required to capture the full benefit of…technologies."
For example, Paul David has described (pdf) how the introduction of electricity into American factories did not immediately raise productivity much, simply because it merely replaced steam engines. It was only when bosses realized that electric motors allowed factories to be reorganized – dispensing with the need for machines to be close to a central power source – that productivity soared, as workflow improved and new cheaper buildings could be used. This took many years.
It's not just organizational change that's needed, though..., I suspect that if IT is to have (further?) productivity-enhancing effects, they require socio-organizational change. ...
However, there are always obstacles to the social and organizational change necessary for technical change to lead to productivity gains. These might be cognitive – such as the Frankenstein syndrome or “not invented here” mentality. Or they can be material. Socio-technical change is a process of creative destruction, the losers from which kick up a stink; think of taxi-drivers protesting against Uber.
Worse still, these losers aren’t always politically weak Ludditites. They can be well-connected bosses of incumbent firms, or managers seeking to maintain their power base. ...
The big question facing us is, therefore: do we have the right set of institutions to foster the socio-organizational change that beget productivity growth? These require a mix of healthy markets, to maximize ecological diversity; a financial system which backs risky new-comers; property rights which incentivise innovation; and state intervention that facilitates all these whilst not being captured by Luddites. If our politics weren’t so imbecilic, this question would be getting a lot more attention than it is.
Beating a Dead Robotic Horse: One of the recurring themes on this blog has been the consequences of robots, AI, or rapid technological change on labor demand. Will humans be put out of work by robots, and will this mean paradise or destitution? I’ve generally argued that we should be optimistic about robots and AI and the like, but others have made coherent arguments for pessimism. I spent a chunk of this week reading over posts, both new and old, and thinking more about these positions.
If there is one distinct difference between the robo-pessimist and robo-optimist view, it is almost exclusively down to timing. The pessimists are worried that the rapid decline of human labor is occurring now, and in many cases has been occurring for a while already. The optimists believe that we have time in front of us to sort things out before human labor is replaced en masse.
Brynjolfsson and McAfee‘s latest is a good example of this robo-optimist view. They concede that human labor is in danger of being replaced... But at the same time they do not think this is imminent...
On the robo-pessimism side, Richard Serlin has a mega-post about the declining prospects for human labor and the possible consequences. What is interesting about Richard’s post is that he essentially makes the case that the replacement of human labor by automation has been occurring for decades; we are already living with it...
I think it is helpful to get beyond the binary viewpoints. ...
I tend to be a weak robo-optimist. I, like Brynjolfsson and McAfee, completely agree that robots/AI will create a drag on the demand for human labor, and in particular unskilled labor. My robo-optimism isn’t a belief about technology. It is a belief that we can figure out how to manage the glide path towards shorter work hours while maintaining living standards for everyone. It’s a good thing that we’ll have to work less.
And there remains a little piece of strong robo-optimism lurking inside of me. I don’t think work less is really well defined. We will likely have to spend less time working for wages to afford the basic material goods in our lives. But that doesn’t mean we won’t spend lots of our time “working” for each other doing other things. Whether that work is paid in wages or not is immaterial.
[There's quite a bit more in the post that I left out.]
Olivier Blanchard, Eugenio Cerutti, and Lawrence Summers (the results are preliminary):
Inflation and Activity – Two Explorations and their Monetary Policy Implications Olivier Blanchard, Eugenio Cerutti, and Lawrence Summers NBER Working Paper No. 21726 November 2015: Introduction: We explore two empirical issues triggered by the Great Financial Crisis. First, in most advanced countries, output remains far below the pre-recession trend, leading researchers to revisit the issue of hysteresis... Second, while inflation has decreased, it has decreased less than was anticipated (an outcome referred to as the “missing disinflation’’), leading researchers to revisit the relation between inflation and activity.
Clearly, if confirmed, either the presence of hysteresis or the deterioration of the relation between inflation and activity would have major implications for monetary policy and for stabilization policy more generally. ...
First, we revisit the hysteresis hypothesis, defined as the hypothesis that recessions may have permanent effects on the level of output relative to trend. ... We find that a high proportion of recessions, about two-thirds, are followed by lower output relative to the pre-recession trend even after the economy has recovered. Perhaps more surprisingly, in about one-half of those cases, the recession is followed not just by lower output, but by lower output growth relative to the pre-recession output trend. That is, as time passes following recessions, the gap between output and projected output on the basis of the pre-recession trend increases. ...
Turning to the Phillips curve relation, we ... find clear evidence that the effect of the unemployment gap on inflation has substantially decreased since the 1970s. Most of the decrease, however, took place before the early 1990s. Since then, the coefficient appears to have been stable, and, in most cases, significant...
Finally, in the last section, we explore the implications of our findings for monetary policy. The findings of the second section have opposite implications for monetary policy... To the extent that recessions are due to the perception or anticipation of lower underlying growth, this implies that estimates of potential output, based on the assumption of an unchanged underlying trend, may be too optimistic, and lead to too strong a policy response to movements in output. However, to the extent that recessions have hysteresis or super-hysteresis effects, then the cost of allowing downward movements in output in response to shifts in demand increases implies that a stronger response to output gaps is desirable.
The findings of the third section yield less dramatic conclusions. To the extent that the coefficient on the unemployment gap, while small, remains significant, the implication is that, within an inflation targeting framework, the interest rate rule should put more weight on the output gap relative to inflation. ...
Anyone think this is correct?:
Economic Policy Splits Democrats, WSJ: The old guard of a party that laid the groundwork for the election of a two-term president watches with unease at what’s happening to their electoral prospects and economic policy proposals. ...
That alarm shines through in a new 52-page report from centrist Democratic think tank the Third Way...
“The right cares only about growth, hoping it will trickle down,” says Jonathan Cowan, president of Third Way. The left, meanwhile, is too focused on “redistribution to address income inequality.”
Third Way says a better agenda focuses on growth by promoting skills, job growth and wealth creation without adding to deficits or raising taxes on the middle class. Its report outlines a series of policies it says can do this...
The gist of the report concludes that the economic problems facing the American middle class have less to do with unfairness—or the idea that the system is fundamentally “rigged” against workers—and more to do with technological and globalization forces that can’t be reversed.
[That statement will drive Larry Mishel nuts.]
The report spotlights a divide on the left in both substance and style. ...
Progressives want to see a more fundamental rewrite of the rules to break up political power, on par with President Theodore Roosevelt‘s “trust-busting” of a century ago. “This country is in real trouble,” Ms. Warren said at the May event. “The game is rigged and we are running out of time.”
That kind of rhetoric gives Mr. Cowan fits because he says it isn’t a winning political message. ...
He says that leading economic ideas on the left, including advocacy for a $15 minimum wage, expanded Social Security benefits and a single-payer health-care system, won’t play well with independent voters. The report cites focus group research in advancing its argument that Americans, particularly independents and moderate voters, are more anxious than they are angry about these changes.
Third Way cites the failures of main street icons such as Kodak, Borders Books and Tower Records as proof that new technologies and delivery systems, as opposed to a “stacked deck” in Washington, are primarily responsible for economic upheaval.
Tower Records explains inequality? Seriously? From Larry Mishel (linked above):
Many economists contend that technology is the primary driver of the increase in wage inequality since the late 1970s, as technology-induced job skill requirements have outpaced the growing education levels of the workforce. The influential “skill-biased technological change” (SBTC) explanation claims that technology raises demand for educated workers, thus allowing them to command higher wages—which in turn increases wage inequality. A more recent SBTC explanation focuses on computerization’s role in increasing employment in both higher-wage and lower-wage occupations, resulting in “job polarization.” This paper contends that current SBTC models—such as the education-focused “canonical model” and the more recent “tasks framework” or “job polarization” approach mentioned above—do not adequately account for key wage patterns (namely, rising wage inequality) over the last three decades.
So, should I adopt a message I don't think is true because it sells with independents who have been swayed by Very Serious People, or should I say what I believe and try to convince people they are barking up the wrong tree? (For the most part anyway, I believe both the technological/globalization and institutional/unfairness explanations have validity -- but how do workers capture the gains Third Way wants to create through growth and wealth creation without the bargaining power they have lost over time with the decline in unionization, threats of offshoring, etc.? That's the bigger problem.) It is unfair when, say, economic or political power redirects income away from those who created it to those who did not (I am using the normative equity principle that each person has a right to keep what he or she produces, to reap what they have sowed, and I have little doubt that workers have been paid less than their productivity, and those at the top more. That's unfair, and redirecting income -- redistributing if you will -- to those who actually earned it is not harmful. It is just, and it creates the correct economic incentives). Wealth creation/growth has not been the biggest problem over the last four decades (i.e. since inequality started to increase), it is how the gains have been distributed. I'd rather convince people of the truth that more growth and more wealth creation won't solve the problem if we don't address workers' bargaining power at the same time than gain their support by patronizing their views. In the meantime redistributing income from those who didn't earn it to those who did can serve as a temporary solution until we get the more fundamental underlying problems fixed (e.g. level the playing field on bargaining power between workers and firms).
Maybe politicians have to tell people what they want to hear, I'll let them figure that out, but I will continue to call it as I see it even if "independents and moderate voters are more anxious than they are angry about these changes." That won't change if we play into those anxieties instead of explaining why new approaches are needed, and explaining how they will benefit from a system that does a better job of rewarding hard work instead of ownership, connections, and power.
Support for the point I was making in my column yesterday:
Rethinking Parameters of the US Welfare State, by Tim Taylor: ...The ... question ... was about whether the welfare state undermines productivity and growth. Garfinkel and Smeeding point out that in the big picture, all the high-income and high-productivity nations of the world have large welfare states; indeed, one can argue that growth rates for many high-income nations were higher in the mid- and late 20th century, when the welfare state was comparatively larger, than back in the 19th century when the welfare state was smaller. Indeed, improved levels of education and health are widely recognized as important components of improved productivity. As they write: "Furthermore, by reducing economic insecurity, social insurance and safety nets make people more willing to take economic risks."
One can make any number of arguments for improving the design of the various aspects of the welfare state, or to point to certain countries where aspects of the welfare state became overbearing or dysfunctional. But from a big-picture viewpoint, it's hard to make the case that a large welfare state has been a drag on growth. Garfinkel and Smeeding write:"Of course, many other factors besides social welfare spending have changed in the past 150 years. But, as we have seen, welfare state spending is now very large relative to the total production of goods and services in all advanced industrialized nations. If such spending had large adverse effects, it is doubtful that growth rates would have been so large in the last 30 years. The crude historical relationship suggests, at a minimum, no great ill effects and, more likely, a positive effect. The burden of proof clearly lies on the side of those who claim that welfare state programs are strangling productivity and growth. If they are right, they need to explain not only why all rich nations have large welfare states, but more importantly why growth rates have grown in most rich nations as their welfare states have grown larger."
The Romer Model turns 25: 25 years ago this month Paul Romer‘s paper, “Endogenous Technological Change” was published in the Journal of Political Economy. After over 20,000 citations, it is one of the most influential economics papers of that period. The short version of what that paper did was to provide a fully specified model whereby technological change (i.e., the growth of productivity) was driven not be outside (or exogenous) forces but, instead, by the allocation of resources to knowledge creation and with a complete description of the incentives involved that provided for that allocation. Other papers had attempted this in the past — as outlined in David Warsh’s great book of 2006 — and others provided alternatives at the same time (including Aghion, Howitt, Grossman, Helpman, Acemoglu and Weitzman) but Romer’s model became the primary engine that fueled a decade-long re-examination of long-term growth in economics; a re-examination that I was involved in back in my student days.
Recently, Romer himself has taken on others who, more recently, have continued to provide models of endogenous economic growth (most notably Robert Lucas) for not building on the work of himself and others that grounded the new growth theory in imperfect competition but instead trying to formulate models based on perfect competition instead. I don’t want to revisit that issue here but do want to note that “The Romer Model” is decidedly non-mathy. As a work of theoretical scholarship, every equation and assumption is carefully justified. The paper is laid out with as much text as there is mathematics. And in the end, you know how the model works, why it works and what drives its conclusions. ...
After explaining the contributions in detail, he also covers:
So why has work in this area somewhat petered out? ...
And ends with:
In summary, the Romer model was a milestone and led to much progress. It is a stunningly beautiful work of economic theory. But there is more to be done and my hope is we will see that happen in the future as the cumulative process that drives new knowledge can drive new economic knowledge as well.
For a long time, I have been making the argument that part of the reason for the inequality problem is distortions in the distribution of income driven by market imperfections such as monopoly power that allows prices to exceed marginal costs. What I didn't realize is that this can also affect measurements of productivity growth:
The relationship between U.S. productivity growth and the decline in the labor share of national income, by Nick Bunker: One of the ongoing debates about the state of the U.S. economy is the extent to which the profits from productivity gains are increasingly going to the owners of capital instead of wage earners. These researchers are debating the extent to which the labor share of income, once considered a constant by economists, is on the decline.
But what if the decline of national income going to labor actually affects the measured rate of U.S. productivity growth? In a blog post published last week, University of Houston economist Dietz Vollrath sketches out a model showing just that scenario. ...
Vollrath argues that businesses with more market power are able to charge higher markups on their goods and services, meaning their pricing is higher than the cost of producing an additional goods or services compared to pricing in a perfectly competitive market. So in this situation where markups are high, goods and services are being produced less efficiently, with the increased profits going to the owners of capital.
Vollrath argues that this is how measured productivity growth is affected by the decline of the labor share of income. Market power is important for thinking about measured productivity growth because, as Vollrath says, it “dictates how efficiently we use our inputs.” ... Impeding the most efficient use of capital and labor via marked-up prices will reduce measured productivity. ... Perhaps this could explain some of the reason why measured productivity growth looks so meager in the seeming age of innovation...
But Vollrath’s story isn’t a complete explanation of the fall in measured productivity, as he acknowledges...
But Vollrath’s market power explanation for falling productivity growth, alongside the falling share of national income going to wage earners, is supported by some evidence. Work by Massachusetts Institute of Technology graduate student Matt Rognlie, for example, found evidence of higher markups.
Whether and how the decline of the labor share of income affects productivity growth is obviously a topic far too large for a couple of blog posts. But Vollrath’s model is especially interesting for connecting two important trends in recent years: the slowdown in productivity growth and the declining labor share. It’s worth, at the very least, a bit more investigation.
Technological Progress Anxiety: Thinking About “Peak Horse” and the Possibility of “Peak Human”, by by Brad DeLong: Another well-written piece by an authorial team led by the very sharp Joel Mokyr–The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?–that in my mind fails to wrestle with the major question, and so leaves me unsatisfied.
Hitherto,... every form of non-human power that substitutes and thus tends to reduce the value of human backs and thighs...–from the horse to the watermill to the steam engine to the diesel to the jet engine–and every single source of manipulation–from the potter’s wheel to the loom to the spinning jenny to the assembly line to the mechanized factory–has required a cybernetic control mechanism. Without such a mechanism, machines are useless. They cannot keep themselves on course and on track. ... And as cybernetic control mechanisms human brains had an overwhelming productivity edge.
The fear is that this time things really are different. The fear is that, this time, technological anxiety is not misguided... For the first time, we find our machines substituting not for human backs, things, eyes, and hands, but for human brains. ... And this factor is offset only by the hope that our machines will reduce the market value of commodities faster than they reduce the value of the median worker’s labor...
I must say that I really do wish that Mokyr et al. had included, in their paper, a discussion of “peak horse”.
A standard economists’ argument goes roughly like this: Technology is introduced only when it is profitable, and lowers the costs of production. Thus the prices of the goods and services produced must go down, leaving consumers with more money to spend on other products, and this creates demand for any workers who are displaced. Thus there will always be new industries growing up to employ any workers displaced by technological change in existing industries.
But that argument applies just as well to the oats, apples, and grooming needed for horses to subsist as for the wages of humans, no? One could ... just as easily have said, a century ago, that: “Fundamental economic principles will continue to operate. Scarcities will still be with us…. Most horses will still have useful tasks to perform, even in an economy where the capacities of power sources and automation have increased considerably…”
Yet ... “Peak horse” in the U.S. came in the 1910s, I believe. After that there was no economic incentive to keep the horse population of America from declining sharply, as at the margin the horse was not worth its feed and care. And in a marginal-cost pricing world, in which humans are no longer the only plausible source of Turing-level cybernetic control mechanisms, what will happen to those who do not own property should the same come to be true, at the margin, of the human? What would “peak human” look like? Or–a related but somewhat different possibility–even “peak male”?
The pickings are a bit scant so far today. You'd think it was the weekend or something. Here's something from Erik Brynjolfsson & Andrew McAfee:
The jobs that AI can't replace,BBC News: Current advances in robots and other digital technologies are stirring up anxiety among workers and in the media. There is a great deal of fear, for example, that robots will not only destroy existing jobs, but also be better at most or all of the tasks required in the future.
Our research at the Massachusetts Institute of Technology (MIT) has shown that that's at best a half-truth. While it is true that robots are getting very good at a whole bunch of jobs and tasks, there are still many categories in which humans perform better. ...
Back to the Future: While I’m wide open to evidence that I’m wrong, I’ve been skeptical of the claim that the robots are coming for our jobs. To be technical, the economics question is this: is the pace at which labor-saving technology is entering the workforce accelerating? ...
There are various pieces of evidence suggesting that the answer is “no.” Most importantly, if the rate at which machines are replacing workers is increasing, then productivity growth—output/hours worked—should also be increasing. But it has been slowing.
One reason for slower productivity growth is diminished investment in capital goods—like machines—a trend that also doesn’t square with the acceleration hypothesis. ...
So, what we have is largely anecdote and our own observation..., but ... when it comes to observations, humans are good at seeing first derivatives (rates of change) and less good at seeing second derivatives (changes in rates of change). We see that iPads and self-scanners are replacing waitpersons and cashiers but it’s hard for us to tell whether “labor-saving technology” ...is coming more quickly than it has in the past.
Of course, this time might really be different (some smart people say it is).
Or, as this article ... reminded me (h/t: KN), this time might not be very different at all. It’s about a new quinoa restaurant in San Francisco, called Eatsa, where you order and get your food without ever interacting with a person. ...
Now, where have I seen that before? Fifty years ago (!), I used to love to go to Manhattan automats, where ... a few coins would get you a sandwich, a veggie (not quinoa!), a slice of delicious pie, and so on. For the record, productivity growth was faster and unemployment was lower back then (though at 10, I don’t recall knowing these facts at the time).
All’s I’m saying is that tech change is always with us, and it’s really hard to tell by observation whether the pace with which it’s replacing workers is accelerating. And there are so many more moving parts to this. I’d bet a big difference between the economies in these two pictures is where the machines were manufactured. In other words, technology doesn’t historically kill labor demand. But it does move it around to different industries, occupations, and today, countries.
So before we conclude we’re all robot fodder, let’s see it in the productivity and investment data. ...
This is from Robert Solow writing at Pacific Standard magazine:
The Future of Work: Why Wages Aren't Keeping Up: One of the more puzzling and damaging features of the American labor market in the last few decades has been the failure of real (i.e. inflation-adjusted) wages and benefits to keep up with the increase in productivity. ...
The custom is to think of value added in a corporation (or in the economy as a whole) as just the sum of the return to labor and the return to capital. But that is not quite right. There is a third component which I will call “monopoly rent” or, better still, just “rent.” ...
The suggestion I want to make is that one important reason for the failure of real wages to keep up with productivity is that the division of rent in industry has been shifting against the labor side for several decades. This is a hard hypothesis to test in the absence of direct measurement. But the decay of unions and collective bargaining, the explicit hardening of business attitudes, the popularity of right-to-work laws, and the fact that the wage lag seems to have begun at about the same time as the Reagan presidency all point in the same direction: the share of wages in national value added may have fallen because the social bargaining power of labor has diminished. ...
Now I would like to connect this hypothesis with another change taking place in the labor market..., the casualization of labor. The proportion of part-time workers has been rising... So are the numbers of workers on fixed-term contracts and independent contractors...
Casual workers have little or no effective claim to the rent component of any firm’s value added... If the division of corporate rents has indeed been shifting against labor, an increasingly casual work force will find it very hard to reverse that trend.
This is from Edmund Phelps. It was kind of hard to highlight the main points in brief extracts, so you may want to take a look at the full article:
What Is Wrong with the West’s Economies?: What is wrong with the economies of the West—and with economics? ...
Many of us in Western Europe and America feel that our economies are far from just...
With little or no effective policy initiative giving a lift to the less advantaged, the jarring market forces of the past four decades—mainly the slowdowns in productivity that have spread over the West and, of course, globalization, which has moved much low-wage manufacturing to Asia—have proceeded, unopposed, to drag down both employment and wage rates at the low end. The setback has cost the less advantaged not only a loss of income but also a loss of what economists call inclusion—access to jobs offering work and pay that provide self-respect. And inclusion was already lacking to begin with. ...
How might Western nations gain—or regain—widespread prospering and flourishing? Taking concrete actions will not help much without fresh thinking: people must first grasp that standard economics is not a guide to flourishing—it is a tool only for efficiency. Widespread flourishing in a nation requires an economy energized by its own homegrown innovation from the grassroots on up. For such innovation a nation must possess the dynamism to imagine and create the new—economic freedoms are not sufficient. And dynamism needs to be nourished with strong human values.
Of the concrete steps that would help to widen flourishing, a reform of education stands out. The problem here is not a perceived mismatch between skills taught and skills in demand. ... The problem is that young people are not taught to see the economy as a place where participants may imagine new things, where entrepreneurs may want to build them and investors may venture to back some of them. It is essential to educate young people to this image of the economy.
It will also be essential that high schools and colleges expose students to the human values expressed in the masterpieces of Western literature, so that young people will want to seek economies offering imaginative and creative careers. Education systems must put students in touch with the humanities in order to fuel the human desire to conceive the new and perchance to achieve innovations. This reorientation of general education will have to be supported by a similar reorientation of economic education.
We will all have to turn from the classical fixation on wealth accumulation and efficiency to a modern economics that places imagination and creativity at the center of economic life.
I'm skeptical that this is the answer to our inequality/job satisfaction problems.
You Can’t Reform Your Way to Rapid Growth: ...in response to the small back-and-forth that Noah Smith (also here) and John Cochrane had regarding Jeb! Bush’s suggestion/idea/hope to push the growth of GDP up to 4% per year. Cochrane asked “why not?”, and offered several proposals for structural reforms (e.g. reforming occupational licensing) that could contribute to growth. Smith was skeptical...
Oddly enough, the discussion of Jeb!’s 4% target is also a good entry point to talking about Greece, and the possibility that the various structural reforms insisted on by the Germans will manage to materially change their situation. But we’ll get to that.
First, what are the possibilities of generating 4% GDP growth in the U.S.? I’m presuming that we’re talking about whether we can boost per capita growth up to 4% per year for some relatively short time frame, because history suggests that sustained 4% growth in GDP is incredibly unlikely. From Jeb!’s perspective, I’m guessing either 4 or 8 years is the right window to look at, but let’s say we’re trying to achieve this for just 5 years. ...[discusses and illustrates the conclusions of a standard growth model]...
You can just scrape 4% growth if you continue to assume that structural reforms to the U.S. economy can add $3 trillion to potential GDP and that the convergence parameter is ... more than twice as big as any reliable empirical estimate. Or you could ... assume that structural reforms were capable of pushing potential GDP to $26 trillion, a 53% increase over potential GDP today. Both are huge stretches, and almost certainly wrong.
It is this same logic that is at play in Greece, by the way. ...
Massive structural reforms are not capable of generating immediate short-run jumps in growth rates in the U.S., Greece, or any other relatively developed economy. They play out over long periods of time, and the empirics we have suggest that by long periods we mean decades and decades of slightly above average growth. ...
Structural reforms don’t generate massive short-term changes in growth rates because they are fiddling with marginal decisions, making people marginally more likely to invest, or change jobs, or get an education, or start a company. By permanently changing those marginal decisions, structural reforms act like glaciers, slowly carving the economy into a new shape over long periods of time. ...
If you want to radically boost GDP growth now, then someone has to spend money now. Take infrastructure spending..., the beauty of infrastructure spending is that is doesn’t just push us closer to potential, it almost certainly raises potential GDP as well, and keeps the growth rate above average for longer. ...
The difference with infrastructure spending is that it does not nibble around the edges or play with marginal decisions. It dumps a bunch of new spending into the economy. And that is the only way to juice the growth rate appreciably in the short run. Structural reforms will raise GDP, and in the long run may raise GDP by far more than immediate infrastructure spending. But that increase in GDP will take decades, and the change in growth will be barely noticeable. You want demonstrably faster growth right now? Then be prepared to spend lots of money right now.
In the Greek situation, the implication is that without some kind of boost to spending now, they are unlikely to ever grow fast enough to ever get out of this hole they are in. ...
From Vox EU:
Unemployment benefits and job match quality, by Arash Nekoei and Andrea Weber: The generosity of unemployment insurance is often cited as a reason for long spells of joblessness. But this view neglects other important, and potentially positive, economic aspects of such programs. Using Austrian data, this column presents evidence that unemployment insurance has a positive effect on the quality of jobs that recipients find. This can in turn have a positive effect on future tax revenues, and has implications for the debate on optimal insurance generosity. ...
One more from Vox EU. This is by Markus Brückner and Daniel Lederman:
Effects of income inequality on economic growth: ... Conclusion Our empirical analysis is motivated by the theoretical work of Galor and Zeira (1993). who examined the relationship between inequality and aggregate output in the presence of credit market imperfections and indivisibilities in human capital investment. Galor and Zeira’s model predicts heterogeneity in the effects of inequality on aggregate output across countries' initial income levels. Taking this prediction seriously, our econometric model included an interaction between measures of income inequality and countries' initial level of GDP per capita. Instrumental variables estimates showed that income inequality has a significant negative effect on aggregate output for the average country in the sample. However, for poor countries income inequality has a significant positive effect. We document that this heterogeneity is also present when considering investment – in particular, investment in human capital – as a channel through which inequality affects aggregate output. Overall, our empirical results provide support for the hypothesis that income inequality is beneficial to economic growth in poor countries, but that it is detrimental to economic growth in advanced economies.
Stephen Cecchetti and Enisse Kharroubi in Vox EU:
Why growth in finance is a drag on the real economy: A booming financial sector means economic growth. Or does it? This column presents new evidence showing that when the financial sector grows more quickly, productivity tends to grow disproportionately slower in industries with either lower asset tangibility or in industries with higher research and development intensity. It turns out that financial booms are not, in general, growth-enhancing.
I have a new column:
Supply-Side Social Insurance: David Brooks’ claim that “the federal government spent nearly $14,000 per poor person” in 2013 and his claim that “over the last 30 years the poverty rate has scarcely changed” have both been thoroughly debunked. The responses show very clearly that spending is nowhere near as large as Brooks claims, and that using a measure of poverty that overcomes some of the problems with the standard measure shows a decline in the poverty rate, though the decline has been slower than we’d prefer. ...
Even if the number had been calculated correctly, it would overstate the true cost of social insurance programs due to the failure to consider “dynamic effects.” That is, these programs don’t just provide income to struggling households in times of need, income that can have a valuable stimulative effect during economic downturns; social insurance programs are also an investment in our future. ...
Not sure why "Doesn't Work" was added to the title -- my point is that it does, if only Republicans would support it.
Robert Hall at Vox EU:
This column is a lead commentary in the VoxEU Debate "Secular Stagnation"
Secular Stagnation in the US, by Robert E. Hall: The disappointing post-crisis performance of the US economy and even more disappointing performance of continental Europe and Japan have revived interest in the possibility of secular stagnation. Under stagnation, real incomes fail to grow or even shrink, and the economy’s output falls farther and farther below its earlier upward trend. Rising unemployment may also occur. Summers (2014) ignited interest in the possibility of secular stagnation.
One important factor in stagnations is the inability or reluctance of the central bank to lower interest rates as low as would seem to be appropriate, given the ability of low rates to stimulate output and employment. The Federal Reserve and the Bank of Japan have kept rates slightly positive since the crisis, while the ECB did the same until recently, when it pushed the rate just slightly negative. All three economies had combinations of high unemployment and substandard inflation that unambiguously called for lower rates, according to standard principles of modern monetary economics.
Extreme slack persists in continental Europe and Japan, but in the US, several labor-market indicators, such as low short-term unemployment and high levels of unfilled job openings, indicate the end of the period of slack that followed the crisis, while others, such as long-term unemployment and involuntary part-time work, still show slack but are declining and will probably reach normal levels in the coming year.1 Forecasters believe that the Fed will unpin the short-term interest rate in the middle of 2015 or a bit later in the year. Markets for forward rates agree.
European versus US secular stagnation: Demand versus supply factors
Thus a consensus is forming that inadequate demand will no longer be a factor in whatever US stagnation occurs in coming years. In Japan and Europe, on the other hand, the case for boosting demand is strong and inadequate demand is almost surely a main cause of the stagnation.
Despite the resumption of normal conditions in the US labor market and the consensus that slack is gone, the US economy is stagnated in the sense that the standard of living stopped growing around 2000. Family purchasing power today is just the same as in that year. Figure 1 shows that it grew briskly during the 1990s, slowed markedly prior to the crisis, dropped below its 2000 level as a result of the crisis, and grew slowly in recent years.
Two episodes of low purchasing-power growth despite a growing economy appear in the figure. From 2002 through 2007 (recovery from the 2001 recession), and 2010 to 2013 (the recovery from the 2008-09 Great Recession). The unemployment rate reached 4.8% in 2007 – well below the long-run average rate of 5.8% and is right at that long-run rate today. The evidence is strong that inadequate demand is not behind the general stagnation of purchasing power, though it was a factor in the period immediately following the Crisis. As of 2014, the US has had a decade and a half of a new kind of secular stagnation, one associated with declining supply.
Causes of US secular supply stagnation
Four factors account for the stagnation of purchasing power in the US economy: 1) declining labor share; 2) depleted capital; 3) reduced productivity growth; and 4) declining labor-force participation. I will discuss indexes that capture each of these factors in turn using indices that all start at unity in 1989. An index of total purchasing power from earnings is the result of multiplying the four indexes together.
Labor’s declining share of income.
Figure 2 shows an index of labor’s share (including fringe benefits) of total US income. It tends to be level in recessions, fall during the first half of ensuing expansions, then rise back to a high level at the next recession. But superimposed on that pattern is a general decline that cumulates to about 10% over the period. Like the general declining trend in earnings, the decline in the share seems to have started around 2000. Economists have pursued multiple explanations of the decline, but no consensus has formed.
Slow overall productivity growth.
Figure 3 shows that productivity grew rapidly from 1989 to 2007. The Great Recession caused a dip in productivity, as did past recessions (due mainly to idle facilities). Though productivity grew at normal rates during the recovery, it did not make up for the shock of the crisis, so the average growth since 2006 has been below par.2 Household earnings suffered proportionately. See Fernald (2014) for further discussion of productivity.
Depleted capital per household.
Figure 4 shows the third factor – the amount of capital available to equip the average worker. With more plant, equipment, and software, workers earn more. Capital per household rose rapidly during the 1990s, but more slowly after 2000. Capital per household actually fell during the Great Recession, and its more recent growth has not come close to placing capital per household where it would have been if the trend of the 1990s had continued.
Low labor-force participation.
Figure 5 displays the average household’s involvement in the workplace as measured by an index of annual hours of work of household members. Hours per household grew rapidly until 2000, fell as usual during the recession of 2001, flattened but did not grow during the boom of 2002 through 2007, unlike previous booms, collapsed in the Great Recession, and have risen during the recovery that is still underway. The decline in hours since 2000 is the single biggest factor in the decline in household earnings.3 Recent growth in hours per household offers some hope for the return of earnings growth in coming years.
Why hours worked declined
Because declining hours account for the biggest part of the stagnation of earnings, I will dig deeper, by breaking them down into three components: Labor-market participants per household; fraction of participants working; and hours per worker.
Figure 6 shows an index of participants per household.
As the chart illustrates, participation rose during the 1990s, especially in the second half of the decade, but has fallen since. The Great Recession depressed participation only slightly and does not appear to have been an important determinant of the overall decline in involvement in the labor market. Of course, the recession was a time when fewer participants were actually working and more were looking for work.
Economists have been working hard on trying to understand the surprising decline in participation, which exceeds forecasts that were made in earlier years. Most research agrees that the slack labor market had a relatively small discouraging effect. Another suspect that has been found to have at most a small role is changes in the composition of the working-age population – the negative effect of aging of the population on participation just offsets the positive effect of higher educational attainment. A large increase in the fraction of households subject to taxes imposed on families benefiting from food stamps, disability, and other safety-net programs may be a factor.
Figure 7 shows an index of the fraction of participants who were actually working – the remainder were unemployed and actively looking for work.
This factor was flat on average, falling in recessions and rising in the ensuing recoveries. It has risen recently, as unemployment has fallen to the upper-five-percent range. It is not an important element of the stagnation of earnings as of today.
Figure 8 tracks hours of work per week for the average household.
It was quite constant over most of the period, but fell sharply during the Great Recession and recovered only about half of the decline since. It is too early to judge whether hours per worker will return soon to its earlier level or remain as an element of the stagnation of earnings.
Work versus other time uses
Some indication about the changing balance between work and other uses of time comes from the American Time Use Survey, which began in 2003. Table 1 shows the change in weekly hours between 2003 and 2013 in a variety of activities.
For men, the biggest change by far is the decline of 2.5 hours per week at work, a big drop relative to a normal 40- hour work week.
A small part of the decline is attributable to higher unemployment—the unemployment rate was 6.0% in 2003 and 7.4% in 2013.
The decline for women is much smaller, at 0.8 hours per week.
For both sexes, the big increases were in personal care (including sleep) and leisure (mainly video-related activities). Essentially no change occurred in time spent in education. Women cut time spent on housework.
Is there hope for a return to normal growth of household purchasing power?
Capital seems likely to continue to return to its historical growth path, as Figure 4 suggests. For the three other major categories, forecasting is a challenge. There has been no sign of a reversal of the decline in labor’s share of total income and no body of research that supports the idea that it will. Productivity growth is definitely under way, at rates similar to those in the 1970s and 1980s, but well below the rates of the 1950s, 1960s, and 1990s. In particular, there is no sign that a burst of productivity growth will make up for the complete stall in productivity growth around the crisis, as Figure 3 shows.
Most importantly, there is no sign suggesting a departure from the decline in labor-force participation shown in Figure 6. Some commentators have declared a turnaround in participation based on recent monthly data, but Figure 9 suggests this is wishful thinking. Participation has declined along a straight line during the period of improving conditions in the labor market, suggesting a complete disconnect between participation and the state of the labor market.
One possibility for growth in purchasing power is that unemployment may dip below 5.5% -- the level that some believe defines full employment. The unemployment rate reached 3.8% in 2000 and 4.4% in 2007, in both cases at the ends of long expansions, without triggering inflation much above the Fed’s target of around 2%.
I reiterate that these conclusions apply to the United States. In continental Europe, the case is strong that demand has far from recovered. In Japan, unemployment is at low levels but the performance of the economy is substandard.
Fernald, John (2014). “Productivity and Potential Output Before, During, and After the Great Recession”, NBER Macro Annual, 2014, forthcoming.
Lawrence H. Summers, “U.S. Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound” Business Economics Vol. 49, No. 2 National Association for Business Economics
1 Complete backup for all of the calculations is available from my website, stanford.edu/~rehall
2 See Fernald (2014) for a discussion of the evidence.
3 See Foote and Ryan (2015).
Faster productivity growth would be great. I’m just not at all sure we can count on it to lift middle-class incomes: Recently, a number of economists and commentators have suggested that faster productivity growth would be a big way to boost the income of middle-class households. I’m all for faster productivity growth, though I’d argue no one knows how to reliably make it happen. But given the wedge of inequality between productivity and low and middle incomes, wages, and wealth, I’m skeptical that this would work as well as some think.
So I wrote this paper exploring the issue and adding some of my own estimates. Here’s the intro...
I tried to make a similar point here: Full Employment Alone Won’t Solve Problem of Stagnating Wages.
From the NBER Digest:
Secular Stagnation: The Long View, by Matt Nesvisky: Growth economists are divided on whether the U.S. is facing a period of "secular stagnation" - an extended period of slow economic growth in the coming decades. In "Secular Stagnation: The Long View" (NBER Working Paper No. 20836), Barry Eichengreen considers four factors that could contribute to a persistent period of below-potential output and slow growth: a rise in saving due to the global integration of emerging markets, a decline in the rate of population growth, an absence of attractive investment opportunities, and a drop in the relative price of investment goods. He concludes that a decline in the relative price of investment goods is the most likely contributor to an excess of saving over investment.
With regard to long-term future growth rates, a key point of debate is how to interpret, and project forward, the "Third Industrial Revolution": the computer age and the new economy it has created. Some argue that the economic impact of digital technology has largely run its course, while others maintain that we have yet to experience the full effect of computerization. In this context, Eichengreen looks at the economic consequences of the age of steam and of the age of electrification. His analysis identifies two dimensions of the economic impact: "range of applicability" and "range of adaptation."
Range of applicability refers to the number of sectors or activities to which the key innovations can be applied. Use of the steam engine of the first industrial revolution for many years was limited to the textile industry and railways, which accounted for only a relatively small fraction of economic activity. Electrification in the second industrial revolution, says Eichengreen, had a larger impact on output and productivity growth because it affected a host of manufacturing industries, many individual households, and a wide range of activities within decades of its development.
The "computer revolution" of the second half of the 20th century had a relatively limited impact on overall economic growth, Eichengreen writes, because computerization had deeply transformative effects on only a limited set of industries, including finance, wholesale and retail trade, and the production of computers themselves. This perspective suggests that the implications for output and productivity of the next wave of innovations will depend greatly on their range of applicability. Innovations such as new tools (quantum computers), materials (graphene), processes (genetic modification), robotics, and enhanced interactivity of digital devices all promise a broad range of applications.
Range of adaptation refers to how comprehensively economic activity must be reorganized before positive impacts on output and productivity occur. Eichengreen reasons that the greater the required range of adaptation, the higher the likelihood that growth may slow in the short run, as costly investments in adaptation must be made and existing technology must be disrupted.
Yet the slow productivity growth in the United States in recent years may have positive implications for the future, he writes. Many connected activities and sectors - health care, education, industrial research, and finance - are being disrupted by the latest technologies. But once a broad range of adaptations is complete, productivity growth should accelerate, he reasons. "This is not a prediction," Eichengreen concludes, "but a suggestion to look to the range of adaptation required in response to the current wave of innovations when seeking to interpret our slow rate of productivity growth and when pondering our future."
Moore's Law at 50: So many important aspects of the US and world economy turn on developments in information and communications technology and their effects These technologies were driving productivity growth, but will they keep doing so? These technologies have been one factor creating the rising inequality of incomes, as many middle-managers and clerical workers found themselves displaced by information technology, while a number of high-end workers found that these technologies magnified their output. Many other technological changes--like the smartphone, medical imaging technologies, decoding the human gene, or various developments in nanotechnology--are only possible based on a high volume of cheap computing power. Information technology is part of what has made the financial sector larger, as the technologies have been used for managing (and mismanaging) risks and returns in ways barely dreamed of before. The trends toward globalization and outsourcing have gotten a large boost because information technology made it easier
In turn, the driving force behind information and communications technology has been Moore's law, which can understood as the proposition that the number of components packed on to a computer chip would double every two years, implying a sharp fall in the capabilities of information technology. But the capability of making transistors ever-smaller, at least with current technology, is beginning to run into physical limits. IEEE Spectrum has published a "Special Report: 50 Years of Moore's Law," with a selection of a dozen short articles looking back at Moore's original formulation of the law, how it has developed over time, and prospects for the law continuing. Here are some highlights.
It's very hard to get an intuitive sense of the exponential power of Moore's law, but Dan Hutcheson takes a shot at it with few well-chosen sentences and a figure. He writes:In 2014, semiconductor production facilities made some 250 billion billion (250 x 1018) transistors. This was, literally, production on an astronomical scale. Every second of that year, on average, 8 trillion transistors were produced. That figure is about 25 times the number of stars in the Milky Way and some 75 times the number of galaxies in the known universe. The rate of growth has also been extraordinary. More transistors were made in 2014 than in all the years prior to 2011.
Here's a figure from Hutcheson showing the trends of semiconductor output and price over time. Notice that both axes are measured as logarithmic scales: that is, they rise by powers of 10. The price of a transistor was more than a dollar back in the 1950s, and now it's a billionth of a penny.
As the engineering project of making the components on a computer chip smaller and smaller is beginning to get near some physical limits. What might happen next?
Chris Mack makes the case that Moore's law is is not a fact of nature; instead, it's the result of competition among chip-makers, who viewed it as the baseline for their technological progress, and thus set their budgets for R&D and investment according to keeping up this pace. He argues that as technological constraints begin to bind, the next step will be for combining capabilities on a chip. ...
Andrew Huang makes the intriguing claim that a slowdown in Moore's law might be useful for other sources of productivity growth. He argues that when the power of information technology is increasing so quickly, there is an understandably heavy focus on adapting to these rapid gains. But if gains in raw information processing slow down, there would be room for more focus on making the devices that use information technology cheaper to produce, easier to use, and cost-effective in many ways.
Jonathan Koomey and Samuel Naffziger point out that computing power has become so cheap that we often aren't using what we've got--which suggests the possibility of efficiency gains in energy use and computer utilization...
Final note: I've written about Moore's law a couple of times previously this blog, including "Checkerboard Puzzle, Moore's Law, and Growth Prospects" (February 4, 2013) and "Moore's Law: At Least a Little While Longer" (February 18, 2014). These posts tend to emphasize that Moore's law may still be good for a few more doublings. But at that point, the course of technological progress in information technology, for better or worse, will take some new turns.
Pat Higgins of the Atlanta Fed's Macroblog:
Is Measurement Error a Likely Explanation for the Lack of Productivity Growth in 2014?: Over the past three years nonfarm business sector labor productivity growth has averaged only around 0.75 percent—well below historical norms. In 2014 it was negative, as can be seen in chart 1.
The previous macroblog post by Atlanta Fed economist John Robertson looked at possible economic explanations for why the labor productivity data, taken at face value, have been relatively weak in recent years. In this post I look at the extent to which “measurement error” can account for the weakness we have seen in the data. By measurement error, I mean incomplete data and/or sampling errors that are reduced when more comprehensive data are available several years later. I do not mean the inherent difficulties in measuring productivity in sectors such as health care or information technology.
As seen in chart 1, negative four-quarter productivity growth rates have been quite infrequent in nonrecessionary periods since 1948. In S. Borağan Aruoba's 2008 Journal of Money, Credit and Banking article “Data Revisions Are Not Well Behaved,” he found that initial estimates of annual productivity growth are negatively correlated with subsequent revisions. That is, low productivity growth rates tend to be revised up while high rates tend to be revised down. This is illustrated in chart 2.
In each of the panels, points in the scatterplot represent an initial estimate of fourth-quarter over fourth-quarter productivity growth together with a revised estimate published either one or three years later. For example, the green points in each plot show estimates of productivity growth over the four quarters ending in the fourth quarter of 2011. In each plot, the x-coordinate shows the March 7, 2012, estimate of this growth rate (0.3 percent). The y-coordinate of the green dot in chart 2a shows the March 7, 2013, estimate of fourth-quarter 2011/fourth-quarter 2010 productivity growth (0.4 percent) while the y-coordinate of the green dot in chart 2b shows the March 5, 2015, estimate (0.0 percent).
In each chart, the red dashed line shows the predicted revised value of productivity growth as a function of the early estimate (using a simple linear regression). Chart 2a shows that, on average, we would expect almost no revision to the most recent estimate of four-quarter productivity growth one year later. Chart 2b, however, shows that low initial estimates of productivity growth tend to be revised up three years later while high estimates tend to be revised down. Based on this regression line, the current estimate of -0.1 percent fourth-quarter 2014/fourth-quarter 2013 productivity growth is expected to be revised up to 0.3 percent by April 2018.
The intuition for this is fairly straightforward. Low productivity growth could come about from either underestimating output growth, overestimating growth in hours worked, or a combination of the two. Which of these is most likely to occur, according to historical revisions? This is shown in chart 3, which plots the predicted revisions to four-quarter nonfarm employment growth and four-quarter nominal gross domestic product (GDP) growth conditional on two assumed values for the initial estimate of four-quarter productivity growth: 0 percent (low) and 4 percent (high).
Nominal GDP is used instead of real GDP as methodological changes to the latter (e.g., the introduction of chain-weighting starting in 1996) make an apples-to-apples comparison of pre- and post-revised values difficult. Using fourth-quarter over fourth-quarter growth rates since 1981, the diamonds on the solid lines in chart 3 show that an initial estimate of 0 percent productivity growth would, on average, be associated with a three-year upward revision of 0.39 percentage point to four-quarter nominal GDP growth and a three-year downward revision of 0.10 percentage point to four-quarter nonfarm payroll employment.
With 4 percent productivity growth, the diamonds on the dashed lines show predicted three-year revisions to nominal GDP growth and employment growth of -0.40 percentage point and 0.14 percentage point, respectively. As the chart shows, these estimates are sensitive to the sample period used to predict the revisions. Using only data since 1989 (not shown), the regression would not predict a downward revision to employment growth conditional on an initial estimate of 0 percent productivity growth. Overall, however, the plot suggests that revisions to output growth are more sensitive to initial estimates of productivity growth than revisions to payroll employment growth are. This is consistent with the sentiments expressed by Federal Reserve Vice Chairman Stanley Fischer and Atlanta Fed President Dennis Lockhart at the March 30–April 1 Financial Markets Conference that employment or unemployment data may be more reliably measured than GDP.
Nevertheless, according to charts 2 and 3, the importance of measurement error in productivity growth is fairly modest. Ex-ante, we should not expect last year's puzzlingly low productivity growth simply to be revised away.
John Robertson at the Atlanta Fed's Macroblog:
What Seems to Be Holding Back Labor Productivity Growth, and Why It Matters: The Atlanta Fed recently released its online Annual Report. In his video introduction to the report, President Dennis Lockhart explained that the economic growth we have experienced in recent years has been driven much more by growth in hours worked (primarily due to employment growth) than by growth in the output produced per hour worked (so-called average labor productivity). For example, over the past three years, business sector output growth averaged close to 3 percent a year. Labor productivity growth accounted for only about 0.75 percentage point of these output gains. The rest was due primarily to growth in employment.
The recent performance of labor productivity stands in stark contrast to historical experience. Business sector labor productivity growth averaged 1.4 percent over the past 10 years. This is well below the labor productivity gains of 3 percent a year experienced during the information technology productivity boom from the mid-1990s through the mid-2000s.
John Fernald and collaborators at the San Francisco Fed have decomposed labor productivity growth into some economically relevant components. The decomposition can be used to provide some insight into why labor productivity growth has been so low recently. The four factors in the decomposition are:
- Changes in the composition of the workforce (labor quality), weighted by labor's share of income
- Changes in the amount and type of capital per hour that workers have to use (capital deepening), weighted by capital's share of income
- Changes in the cyclical intensity of utilization of labor and capital resources (utilization)
- Everything else—all the drivers of labor productivity growth that are not embodied in the other factors. This component is often called total factor productivity.
The chart below displays the decomposition of labor productivity for various time periods. The bar at the far right is for the last three years (the next bar is for the past 10 years). The colored segments in each bar sum to average annual labor productivity growth for each time period.
Taken at face value, the chart suggests that a primary reason for the sluggish average labor productivity growth we have seen over the past three years is that capital spending growth has not kept up with growth in hours worked—a reduction in capital deepening. Declining capital deepening is highly unusual.
Do we think this sluggishness will persist? No. In our medium-term outlook, we at the Atlanta Fed expect that factors that have held down labor productivity growth (particularly relatively weak capital spending) will dissipate as confidence in the economy improves further and firms increase the pace of investment spending, including on various types of equipment and intellectual capital. We currently anticipate that the trend in business sector labor productivity growth will improve to a level of about 2 percent a year, midway between the current pace and the pace experienced during the 1995–2004 period of strong productivity gains. That is, we are not productivity pessimists. Time will tell, of course.
Clearly, this optimistic labor productivity outlook is not without risk. For one thing, we have been somewhat surprised that labor productivity has remained so low for so long during the economic recovery. Moreover, the first quarter data don't suggest that a turning point has occurred. Gross domestic product (GDP) in the first quarter is likely to come in on the weak side (the latest GDPNow tracking estimate here is currently signaling essentially no GDP growth in the first quarter), whereas employment growth is likely to be quite robust (for example, the ADP employment report suggested solid employment gains). As a result, we anticipate another weak reading for labor productivity in the first quarter. We are not taking this as refutation of our medium-term outlook.
Continued weakness in labor productivity would raise many important questions about the outlook for both economic growth and wage and price inflation. For example, our forecast of stronger productivity gains also implies a similarly sized pickup in hourly wage growth. To see this, note that unit labor cost (the wage bill per unit of output) is thought to be an important factor in business pricing decisions. The following chart shows a decomposition of average growth in business sector unit labor costs into the part due to nominal hourly wage growth and the part offset by labor productivity growth:
The 1975–84 period experienced high unit labor costs because labor productivity growth didn't keep up with wage growth. In contrast, the relatively low and stable average unit labor cost growth we have experienced since the 1980s has been due to wage growth largely offset by gains in labor productivity. Our forecast of stronger labor productivity growth implies faster wage growth as well. That said, a rise in wage growth absent a pickup in labor productivity growth poses an upside risk to our inflation outlook.
Of course, the data on productivity and its components are estimates. It is possible that the data are not accurately reflecting reality in real time. For example, colleagues at the Board of Governors suggest that measurement issues associated with the price of high-tech equipment may be causing business investment to be somewhat understated. That is, capital deepening may not be as weak as the current data indicate. In a follow-up blog to this one, my Atlanta Fed colleague Patrick Higgins will explore the possibility that the weak labor productivity we have recently experienced is likely to be revised away with subsequent revisions to GDP and hours data.
Timothy Aeppel at the WSJ:
Be Calm, Robots Aren’t About to Take Your Job, MIT Economist Says: David Autor knows a lot about robots. He doesn’t think they’re set to devour our jobs. ... His is “the non-alarmist view”...
Mr. Autor’s latest paper, presented to a packed audience at this year’s meeting of central bankers at Jackson Hole, Wyo., emphasized how difficult it is to program machines to do many tasks that humans find often easy and intuitive. In it, he played off a paradox identified in the 1960s by philosopher Michael Polanyi, who noted that humans can do many things without being able to explain how, like identify the face of a person in a series of photographs as they age. Machines can’t do that, at least not with accuracy.
This is why big breakthroughs in automation will take longer than many predict, Mr. Autor told the bankers. If a person can’t explain how they do something, a computer can’t be programmed to mimic that ability. ...
To Mr. Autor, polarization of the job market is the real downside of automation. He calculates middle-skill occupations made up 60% of all jobs in 1979. By 2012, this fell to 46%. The same pattern is visible in 16 European Union economies he studied.
The upshot is more workers clustered at the extremes. At the same time, average wages have stagnated for more than a decade. He attributes this to the loss of all those relatively good-paying middle-range jobs, as well as downward pressure on lower-skilled wages as displaced workers compete for the lesser work. ...
I've been arguing for a long time that in coming decades the major question will be about distribution, not production. I'm not very worried about stagnation, etc. -- we'll have plenty of stuff to go around. I'm worried about, to quote the title of a political science textbook I used many, many, many years ago as an undergraduate, "who gets the cookies?" not how many cookies we're able to produce So I agree with Autor on this point:
Mr. Autor ... added, “If we automate all the jobs, we’ll be rich—which means we’ll have a distribution problem, not an income problem.”
Long-Run Real GDP Forecasts: The Hopeless Task of Trying to Pierce the Veil of Time and Ignorance Weblogging: Focus, by Brad DeLong: I draw somewhat different conclusions from the wavering track of potential GDP since 1990 than do the viri illustres Steve Cecchetti and Kermit Schoenholtz...
First, I think that monetary policymakers should not be looking at potential output and the output gap at all. They should be looking at the labor market. ...[graph 1, graph 2]
Second, I think that the most important macroeconomic research question of our age is the extent to which these fluctuations in the projected growth path arise because of signal-processing considerations in an environment in which the growth rate is subject to both transitory and permanent shocks, rather than to short-run shocks casting very long-run shadows. To the extent that it is the second–and the older I get the more it looks to me as though it might well be–the more it becomes the case that successful management of aggregate demand and the business cycle is the ball game, rather than just being an amuse bouche that it is nice to have.
Third, there is the question that I now harp upon incessantly of the relationship between measured real GDP and money-metric utility in a consumer-surplus sense. (Plus there is the question of the relationship between money-metric utility in a consumer surplus sense and societal well-being.)
Fourth, I question whether previous pre-1980 studies of the U.S. economy would reveal similar fluctuations in trend growth projections. In fact, as best as I can determine, it does not. Going back to the start of the 1890s, at least, and even with such enormous shocks as the Great Depression and World War II, straightforward projections of real GDP do not fluctuate nearly as much as those that have been made over the last twenty years...[graph 3] ... Is this an illusion? Accidental overlapping and offsetting shocks that just happened to sum to zero? It may well be...
Acemoglu, Autor, Dor, Hansen, and Price (I've noted this paper once or twice already in recent months, but thought it worthwhile to post their summary of te work):
The rise of China and the future of US manufacturing, by Daron Acemoglu, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price, Vox EU: The end of the Great Recession has rekindled optimism about the future of US manufacturing. In the second quarter of 2010 the number of US workers employed in manufacturing registered positive growth – its first increase since 2006 – and subsequently recorded ten consecutive quarters of job gains, the longest expansion since the 1970s. Advocating for the potential of an industrial turnaround, some economists give a positive spin to US manufacturing’s earlier troubles: while employment may have fallen in the 2000s, value added in the sector has been growing as fast as the overall US economy. Its share of US GDP has kept stable, an achievement matched by few other high-income economies over the same period (Lawrence and Edwards 2013, Moran and Oldenski 2014). The business press has giddily coined the term ‘reshoring’ to describe the phenomenon – as yet not well documented empirically – of companies returning jobs to the United States that they had previously offshored to low-wage destinations.
Before we declare a renaissance for US manufacturing, it is worth re-examining the magnitude of the sector’s previous decline and considering the causal factors responsible for job loss. The scale of the employment decline is indeed stunning. Figure 1 shows that in 2000, 17.3 million US workers were employed in manufacturing, a level that with periodic ups and downs had changed only modestly since the early 1980s. By 2010, employment had dropped to 11.5 million workers, a 33% decrease from 2000. Strikingly, most of this decline came before the onset of the Great Recession. In the middle of 2007, on the eve of the Lehman Brothers collapse that paralysed global financial markets, US manufacturing employment had already dipped to 13.9 million workers, such that three-fifths of the job losses over the 2000 to 2010 period occurred prior to the US aggregate contraction. Figure 1 also reveals the paltriness of the recent manufacturing recovery. As of mid-2014, the number of manufacturing jobs had reached only 12.1 million, a level far below the already diminished pre-recession level.
Figure 1. US employment , 1980q1-2014q3
Source: US Bureau of Labor Statistics.
We examine the reasons behind the recent decline in US manufacturing employment (Acemoglu et al. 2014). Our point of departure is the coincidence of the 2000s swoon in US manufacturing and a significant increase in import competition from China (Bernard et al. 2006). Between 1990 and 2011 the share of global manufacturing exports originating in China surged from two to 16% (Hanson 2012). This widely heralded export boom was the outcome of deep economic reforms that China enacted in the 1980s and 1990s, which were further extended by the country’s joining the World Trade Organization in 2001 (Brandt et al. 2012, Pierce and Schott 2013). China’s share in US manufacturing imports has expanded in concert with its global presence, rising from 5% in 1991 to 11% in 2001 before leaping to 23% in 2011. Could China’s rise be behind US manufacturing’s fall?
The first step in our analysis is to estimate the direct impact of import competition from China on US manufacturing industries. Suppose that the economic opening in China allows the country to realise a comparative advantage in manufacturing that had lain dormant during the era of Maoist central planning, which entailed near prohibitive barriers to trade. As reform induces China to reallocate labour and capital from farms to factories and from inefficient state-owned enterprises to more efficient private businesses, output will expand in the sectors in which the country’s comparative advantage is strongest. China’s abundant labour supply and relatively scarce supply of arable land and natural resources make manufacturing the primary beneficiary of reform-induced industrial restructuring. The global implications of China’s reorientation toward manufacturing – strongly abetted by inflows of foreign direct investment – are immense. China accounts for three-quarters of all growth in manufacturing value added that has occurred in low and middle income economies since 1990.
For many US manufacturing firms, intensifying import competition from China means a reduction in demand for the goods they produce and a corresponding contraction in the number of workers they employ. Looking across US manufacturing industries whose outputs compete with Chinese import goods, we estimate that had import penetration from China not grown after 1999, there would have been 560,000 fewer manufacturing jobs lost through 2011. Actual US manufacturing employment declined by 5.8 million workers from 1999 to 2011, making the counterfactual job loss from the direct effect of greater Chinese import penetration amount to 10% of the realised job decline in manufacturing.
These direct effects of trade exposure do not capture the full impact of growing Chinese imports on US employment. Negative shocks to one industry are transmitted to other industries via economic linkages between sectors. One source of linkages is buyer-supplier relationships (Acemoglu et al. 2012). Rising import competition in apparel and furniture – two sectors in which China is strong – will cause these ‘downstream’ industries to reduce purchases from the ‘upstream’ sectors that supply them with fabric, lumber, and textile and woodworking machinery. Because buyers and suppliers often locate near one another, much of the impact of increased trade exposure in downstream industries is likely to transmit to suppliers in the same regional or national market. We use US input-output data to construct downstream trade shocks for both manufacturing and non-manufacturing industries. Estimates from this exercise indicate sizeable negative downstream effects. Applying the direct plus input-output measure of exposure increases our estimates of trade-induced job losses for 1999 to 2011 to 985,000 workers in manufacturing and to two million workers in the entire economy. Inter-industry linkages thus magnify the employment effects of trade shocks, almost doubling the size of the impact within manufacturing and producing an equally large employment effect outside of manufacturing.
Two additional sources of linkages between sectors operate through changes in aggregate demand and the reallocation of labour. When manufacturing contracts, workers who have lost their jobs or suffered declines in their earnings subsequently reduce their spending on goods and services. The contraction in demand is multiplied throughout the economy via standard Keynesian mechanisms, depressing aggregate consumption and investment. Helping offset these negative aggregate demand effects, workers who exit manufacturing may take up jobs in the service sector or elsewhere in the economy, replacing some of the earnings lost in trade-exposed industries. Because aggregate demand and reallocation effects work in opposing directions, we can only detect their net impact on total employment. A further complication is that these impacts operate at the level of the aggregate economy – as opposed to direct and input-output effects of trade shocks which operate at the industry level – meaning we have only as many data points to detect their presence as we have years since the China trade shock commenced. Since China’s export surge did not hit with full force until the early 1990s, the available time series for the national US economy is disconcertingly short.
To address this data challenge, we supplement our analysis of US industries with an analysis of US regional economies. We define regions to be ‘commuting zones’ which are aggregates of commercially linked counties that comprise well-defined local labour markets. Because commuting zones differ sharply in their patterns of industrial specialisation, they are differentially exposed to increased import competition from China (Autor et al. 2013). Asheville, North Carolina, is a furniture-making hub, putting it in the direct path of the China maelstrom. In contrast, Orlando, Florida (of Disney and Harry Potter World Fame), focuses on tourism, leaving it lightly affected by rising imports of manufactured goods. If the reallocation mechanism is operative, then when a local industry contracts as a result of Chinese competition, some other industry in the same commuting zone should expand. Aggregate demand effects should also operate within local labour markets, as shown by Mian and Sufi (2014) in the context of the recent US housing bust. If increased trade exposure lowers aggregate employment in a location, reduced earnings will decrease spending on non-traded local goods and services, magnifying the impact throughout the local economy.
Our estimates of the net impact of aggregate demand and reallocation effects imply that import growth from China between 1999 and 2011 led to an employment reduction of 2.4 million workers. This figure is larger than the 2.0 million job loss estimate we obtain for national industries, which only captures direct and input-output effects. But it still likely understates the full consequences of the China shock on US employment. Neither our analysis for commuting zones nor for national industries fully incorporates all of the adjustment channels encompassed by the other. The national-industry estimates exclude reallocation and aggregate demand effects, whereas the commuting-zone estimates exclude the national component of these two effects, as well as the non-local component of input-output linkage effects. Because the commuting zone estimates suggest that aggregate forces magnify rather than offset the effects of import competition, we view our industry-level estimates of employment reduction as providing a conservative lower bound.
What do our findings imply about the potential for a US manufacturing resurgence? The recent growth in manufacturing imports to the US is largely a consequence of China’s emergence on the global stage coupled with its deep comparative advantage in labour-intensive goods. The jobs in apparel, furniture, shoes, and other wage-sensitive products that the United States has lost to China are unlikely to return. Even as China’s labour costs rise, the factories that produce these goods are more likely to relocate to Bangladesh, Vietnam, or other countries rising in China’s wake than to reappear on US shores. Further, China’s impact on US manufacturing is far from complete. During the 2000s, the country rapidly expanded into the assembly of laptops and cell-phones, with production occurring increasingly under Chinese brands, such as Lenovo and Huawei. Despite this rather bleak panorama, there are sources of hope for manufacturing in the United States. Perhaps the most encouraging sign is that the response of many companies to increased trade pressure has been to increase investment in innovation (Bloom et al. 2011). The ensuing advance in technology may ultimately help create new markets for US producers. However, if the trend toward the automation of routine jobs in manufacturing continues (Autor and Dorn 2013), the application of these new technologies is likely to do much more to boost growth in value added than to expand employment on the factory floor.
Acemoglu D, V Carvalho, A Ozdaglar, and A Tahbaz-Salehi (2012), “The Network Origins of Aggregate Fluctuations.” Econometrica, 80(5): 1977-2016.
Acemoglu D, D H Autor, D Dorn, G H Hanson, and B Price (2014), “Import Competition and the Great US Employment Sag of the 2000s.” NBER Working Paper No. 20395.
Autor, D H and D Dorn (2013), “The Growth of Low Skill Service Jobs and the Polarization of the US Labor Market.” American Economic Review, 103(5), 1553-1597.
Autor D H, D Dorn, and G H Hanson (2013a) “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” American Economic Review, 103(6): 2121-2168.
Bernard A B, J B Jensen, and P K Schott (2006), “Survival of the Best Fit: Exposure to Low-Wage Countries and the (Uneven) Growth of US Manufacturing Plants.” Journal of International Economics, 68(1), 219-237.
Bloom N, M Draca, and J Van Reenen (2012), “Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT, and Productivity.” Mimeo, Stanford University.
Brandt L, J Van Biesebroeck, and Y Zhang (2012), “Creative Accounting or Creative Destruction? Firm-Level Productivity Growth in Chinese Manufacturing.” Journal of Development Economics, 97(2): 339-351.
Hanson, G (2012), “The Rise of Middle Kingdoms: Emerging Economies in Global Trade.” Journal of Economic Perspectives, 26(2): 41-64.
Mian, A and A Sufi (2014), “What Explains the 2007-2009 Drop in Employment?” Econometrica, forthcoming.
Pierce, J R and P K Schott (2013), “The Surprisingly Swift Decline of US Manufacturing Employment.” Yale Department of Economics Working Paper, November.
The opening lines of a relatively long discussion from Robin Harding at the FT of "the productivity puzzle":
US economy: The productivity puzzle, by Robin Harding: To glimpse the miracle of productivity growth there is nowhere better to look than the ... US Corn Belt. A hundred years ago, an army of farmers toiled to produce 30 bushels an acre; now only a few hands are needed to produce 160 bushels from the same land.
The rise of modern civilisation rested on this trend: for each person to produce ever more. For the past 120 years, as if bound by some inexorable law, output per head of population increased by about 2 per cent a year. That is, until now.
There is a fear – voiced by credible economists such as Robert Gordon... – that 2 per cent is no law but a wave that has already run its course. According to Prof Gordon’s analysis, 2 per cent could easily become 1 per cent or even less, for the next 120 years. ...
Yet there are also techno-optimists, such as Erik Brynjolfsson and Andrew McAfee..., whose faith in new discoveries is such that they expect growth to accelerate, not decline.
Then there are more phlegmatic economists, whose answers are less exciting but also less speculative – and come in a bit below 2 per cent for growth in output per head.
The productivity question is of the greatest possible consequence for the US economy...
Pricing Power and Lower Potential GDP: One of the results of the Great Recession has been a severe downward revision in potential GDP across many countries. Laurence Ball just had a Vox post on this..., finding that potential GDP is lower by 8.4% on average across the OECD, and up to 30% lower in places like Greece. This is similar to Fernald’s recent finding that potential GDP is lower in the U.S., the only difference being that Fernald finds the slowdown in potential GDP started before 2007. Potential GDP is growing more slowly than previously because of slower capital accumulation, slowing (or falling) labor force participation, and/or lower growth in total factor productivity (TFP).
One interpretation of slowing TFP growth is that we are actively getting worse at innovating and/or bringing innovations to market. For Fernald, the burst of innovations coming from the IT revolution are running out. In a recent Brookings report, new firms are not starting up as quickly, possibly reducing the rate at which new innovations are brought on board. Ball doesn’t really take a stand on what is happening, but the implication is that the Great Recession did something that is pulling down productivity levels.
The point I want to make here is that declining measures of TFP do not necessarily imply that our ability to innovate or bring innovations to market is declining. Measured aggregate TFP can decline, or grow more slowly, even though firms are just as technically productive as before, and are innovating at the same rate as before. Instead, measured TFP growth may be slowing down because of changes in the market power of firms during the recession. ...
...suppose that at the moment real wages or inflation begin to rise, the central bank tightens monetary policy. This would raise the cost of capital, and could be interpreted as an attempt to prevent real wages rising. ... Monetary policy, which in theory is just keeping inflation under control, is in fact keeping real wages and productivity low.
Monetary policy makers would describe this as unfair and even outlandish. A gradual rise in interest rates, begun before inflation exceeds its target, is designed to maintain a stable environment. ...
I also have another concern about a monetary policy which tightens as soon as real wages start increasing. What little I know about economic history suggests an additional dynamic. As long as the firm is employing labour rather than buying a machine, there is no incentive for anyone to improve the productivity of machines. The economy where real wages and labour productivity stay low may also be an economy where innovation slows down. The low productivity economy becomes the low productivity growth economy.
[The extract does not fully reflect the argument in the post -- see here for more.]
Hours Worked, No Change; Output, Up 42%: Here's one snapshot of how the U.S. economy evolved in the last 15 years: an identical number of total hours worked in 1998 and 2013, even though the population rose by over 40 million people, but a 42% gain in output. Shawn Sprague explains in "What can labor productivity tell us about the U.S. economy?" published as the Beyond the Numbers newsletter from the U.S. Bureau of Labor Statistics for May 2014. ...
A lot can be said about this basic fact pattern. Of course, the comparison years are a bit unfair, because 1998 was near the top of the unsustainably rapid dot-com economic boom, with an unemployment rate around 4.5%, while 2013 is the sluggish aftermath of the Great Recession. The proportion of U.S. adults who either have jobs or are looking for jobs--the "labor force participation rate"--has been declining for a number of reasons: for example, the aging of the population so that more adults are entering retirement, a larger share of young adults pursuing additional education and not working while they do so, a rise in the share of workers receiving disability payments, and the dearth of decent-paying jobs for low-skilled labor. ...
The more immediate question is what to make of an economy that is growing in size, but not in hours worked, and that is self-evidently having a hard time generating jobs and bringing down the unemployment rates as quickly as desired. I'm still struggling with my own thoughts on this phenomenon. But I keep coming back to the tautology that there will be more good jobs when more potential employers see it as in their best economic interest to start firms, expand firms, and hire employees here in the United States.
Automation Alone Isn’t Killing Jobs, by Tyler Cowen, Commentary, NY Times: Although the labor market report on Friday showed modest job growth, employment opportunities remain stubbornly low in the United States, giving new prominence to the old notion that automation throws people out of work.
Back in the 19th century, steam power and machinery took away many traditional jobs, though they also created new ones. This time around, computers, smart software and robots are seen as the culprits. They seem to be replacing many of the remaining manufacturing jobs and encroaching on service-sector jobs, too.
Driverless vehicles and drone aircraft are no longer science fiction, and over time, they may eliminate millions of transportation jobs. Many other examples of automatable jobs are discussed in “The Second Machine Age,” a book by Erik Brynjolfsson and Andrew McAfee, and in my own book, “Average Is Over.” The upshot is that machines are often filling in for our smarts, not just for our brawn — and this trend is likely to grow.
How afraid should workers be of these new technologies? There is reason to be skeptical of the assumption that machines will leave humanity without jobs. ...
Moore's Law: At Least a Little Longer: One can argue that the primary driver of U.S. and even world economic growth in the last quarter-century is Moore's law--that is, the claim first advanced back in 1965 by Gordon Moore, one of the founders of Intel Corporation that the number of transistors on a computer chip would double every two years. But can it go on? Harald Bauer, Jan Veira, and Florian Weig of the McKinsey Global Institute consider the issues in "Moore’s law: Repeal or renewal?" a December 2013 paper. ...
The authors argue that technological advances already in the works are likely to sustain Moore's law for another 5-10 years. This As I've written before, the power of doubling is difficult to appreciate at an intuitive level, but it means that the increase is as big as everything that came before. Intel is now etching transistors at 22 nanometers, and as the company points out, you could fit 6,000 of these transistors across the width of a human hair; or if you prefer, it would take 6 million of these 22 nanometer transistors to cover the period at the end of a sentence. Also, a 22 nanometer transistor can switch on and off 100 billion times in a second.
The McKinsey analysts point out that while it is technologically possible for Moore's law to continue, the economic costs of further advances are becoming very high. They write: "A McKinsey analysis shows that moving from 32nm to 22nm nodes on 300-millimeter (mm) wafers causes typical fabrication costs to grow by roughly 40 percent. It also boosts the costs associated with process development by about 45 percent and with chip design by up to 50 percent. These dramatic increases will lead to process-development costs that exceed $1 billion for nodes below 20nm. In addition, the state-of-the art fabs needed to produce them will likely cost $10 billion or more. As a result, the number of companies capable of financing next-generation nodes and fabs will likely dwindle."
Of course, it's also possible to have performance improvements and cost decreases on chips already in production: for example, the cutting edge of computer chips today will probably look like a steady old cheap workhorse of a chip in about five years. I suspect that we are still near the beginning, and certainly not yet at the middle, of finding ways for information and communications technology to alter our work and personal lives. But the physical problems and higher costs of making silicon-based transistors at an ever-smaller scale won't be denied forever, either.
... In theory, wages should grow at the rate of inflation plus the rate of growth of productivity. But in the last several years wage growth has been below this benchmark. Why? Here are five factors that are conspiring to restrain wage growth. ...
Robots and Economic Luddites: They Aren't Taking Our Jobs Quickly Enough: Lydia DePillis warns us in the Post of 8 ways that robots will take our jobs. It is amazing how the media have managed to hype the fear of robots taking our jobs at the same time that they have built up fears over huge budget deficits bankrupting the country. You don't see the connection? Maybe you should be an economics reporter for a leading national news outlet.
Okay, let's get to basics. The robots taking our jobs story is a story of labor surplus, too many workers, too few jobs. Everything that needs to be done is being done by the robots. There is nothing for the rest of us to do but watch.
There can of course be issues of distribution. If the one percent are able to write laws that allow them to claim everything the robots produce then they can make most of us very poor. But this is still a story of society of plenty. We can have all the food, shelter, health care, clean energy, etc. that we need; the robots can do it for us.
Okay, now let's flip over to the budget crisis that has the folks at the Washington Post losing sleep. This is a story of scarcity. We are spending so much money on our parents' and grandparents' Social Security and Medicare that there is no money left to educate our kids.
Some confused souls may say that the problem may not be an economic one, but rather a fiscal problem. The government can't raise the tax revenue to pay for both the Social Security and Medicare for the elderly and the education of our kids. This is confused because if we are living in the world where the robots are doing all the work then the government really doesn't need to raise tax revenue, it can just print the money it needs to back its payments.
Okay, now everyone is completely appalled. The government is just going to print trillions of dollars? That will send inflation through the roof, right? Not in the world where robots are doing all the work it won't. If we print money it will create more demands for goods and services, which the robots will be happy to supply. As every intro econ graduate knows, inflation is a story of too much money chasing too few goods and services. But in the robots do everything story, the goods and services are quickly generated to meet the demand. Where's the inflation, robots demanding higher wages?
In short, you can craft a story where we have huge advances in robot technology so that the need for human labor is drastically reduced. You can also craft a story where an aging population leads to too few workers being left to support too many retirees. However, you can't believe both at the same time unless you write on economic issues for the Washington Post.
Just in case anyone cares about what the data says on these issues, the robots don't seem to be winning out too quickly. Productivity growth has slowed sharply over the last three years and it is well below the pace of 1947-73 golden age. (Robots are just another form of good old-fashioned productivity growth.)
On the other hand, the scarcity mongers don't have much of a case either. Even if productivity growth stays at just a 1.5 percent annual rate its impact on raising wages and living standards will swamp any conceivable tax increases associated with caring for a larger population of retirees.
From Lawrence Mishel, Heidi Shierholz, and John Schmitt:
Don’t Blame the Robots: Assessing the Job Polarization Explanation of Growing Wage Inequality, by Lawrence Mishel, Heidi Shierholz, and John Schmitt, EPI–CEPR Working Paper: Executive summary Many economists contend that technology is the primary driver of the increase in wage inequality since the late 1970s, as technology-induced job skill requirements have outpaced the growing education levels of the workforce. The influential “skill-biased technological change” (SBTC) explanation claims that technology raises demand for educated workers, thus allowing them to command higher wages—which in turn increases wage inequality. A more recent SBTC explanation focuses on computerization’s role in increasing employment in both higher-wage and lower-wage occupations, resulting in “job polarization.” This paper contends that current SBTC models—such as the education-focused “canonical model” and the more recent “tasks framework” or “job polarization” approach mentioned above—do not adequately account for key wage patterns (namely, rising wage inequality) over the last three decades. Principal findings include:
1. Technological and skill deficiency explanations of wage inequality have failed to explain key wage patterns over the last three decades, including the 2000s.
The early version of the “skill-biased technological change” (SBTC) explanation of wage inequality posited a race between technology and education where education levels failed to keep up with technology-driven increases in skill requirements, resulting in relatively higher wages for more educated groups, which in turn fueled wage inequality (Katz and Murphy 1992; Autor, Katz, and Krueger 1998; and Goldin and Katz 2010). However, the scholars associated with this early, and still widely discussed, explanation highlight that it has failed to explain wage trends in the 1990s and 2000s, particularly the stability of the 50/10 wage gap (the wage gap between low- and middle-wage earners) and the deceleration of the growth of the college wage premium since the early 1990s (Autor, Katz, and Kearney 2006; Acemoglu and Autor 2012). This motivated a new technology-based explanation (formally called the “tasks framework”) focused on computerization’s impact on occupational employment trends and the resulting “job polarization”: the claim that occupational employment grew relatively strongly at the top and bottom of the wage scale but eroded in the middle (Autor, Levy, and Murnane 2003; Autor, Katz, and Kearney 2006; Acemoglu and Autor 2012; Autor 2010). We demonstrate that this newer version—the task framework, or job polarization analysis—fails to explain the key wage patterns in the 1990s it intended to explain, and provides no insights into wage patterns in the 2000s. We conclude that there is no currently available technology-based story that can adequately explain the wage trends of the last three decades.
2. History shows that middle-wage occupations have shrunk and higher-wage occupations have expanded since the 1950s. This has not driven any changed pattern of wage trends.
We demonstrate that key aspects of “job polarization” have been taking place since at least 1950. We label this “occupational upgrading” since it primarily consists of shrinkage in relative employment in middle-wage occupations and a corresponding expansion of employment in higher-wage occupations. Lower-wage occupations have remained a small (less than 15 percent) and relatively stable share of total employment since the 1950s, though they have grown in importance in the 2000s. Occupational upgrading has occurred in decades with both rising and falling wage inequality and in decades with both rising and falling median wages, indicating that occupational employment patterns, by themselves, cannot explain the salient wage trends.
3. Evidence for job polarization is weak.
We use the Current Population Survey to replicate existing findings on job polarization, which are all based on decennial census data. Job polarization is said to exist when there is a U-shaped plot in changes in occupational employment against the initial occupational wage level, indicating employment expansion among high- and low-wage occupations relative to middle-wage occupations. As shown in Figure E (explained later in the paper but introduced here), in important cases, these plots do not take the posited U-shape. More importantly, in all cases the lines traced out fit the data very poorly, obscuring large variations in employment growth across occupational wage levels.
4. There was no occupational job polarization in the 2000s.
In the 2000s, relative employment expanded in lower-wage occupations, but was flat at both the middle and the top of the occupational wage distribution. The lack of overall job polarization in the 2000s is a phenomenon visible in both the analyses of decennial census/American Community Survey data provided by proponents of the tasks framework/job polarization perspective (Autor 2010; Acemoglu and Autor 2012) and in our analysis of the Current Population Survey. Thus, the standard techniques applied to the data for the 2000s do not establish even a prima facie case for the existence of overall job polarization in the most recent decade. This leaves the job polarization story, at best, as an account of wage inequality in the 1990s. It certainly calls into question whether it should be a description of current labor market trends and the basis of current policy decisions.
5. Occupational employment trends do not drive wage patterns or wage inequality.
We demonstrate that the evidence does not support the key causal links between technology-driven changes in tasks and occupational employment patterns and wage inequality that are at the core of the tasks framework and job polarization story. Proponents of job polarization as a determinant of wage polarization have, for the most part, only provided circumstantial evidence: both trends occurred at the same time. The causal story of the tasks framework is that technology (i.e., computerization) drives changes in the demand for tasks (increasing demand at the top and bottom relative to the middle), producing corresponding changes in occupational employment (increasing relative employment in high- and low-wage occupations relative to middle-wage occupations). These changes in occupational employment patterns are said to drive changes in overall wage patterns, raising wages at the top and bottom relative to the middle. However, the intermediate step in this story must be that occupational employment trends change the occupational wage structure, raising relative wages for occupations with expanding employment shares and vice-versa. We demonstrate that there is little or no connection between decadal changes in occupational employment shares and occupational wage growth, and little or no connection between decadal changes in occupational wages and overall wages. Changes within occupations greatly dominate changes across occupations so that the much-focused-on occupational trends, by themselves, provide few insights.
6. Occupations have become less, not more, important determinants of wage patterns.
The tasks framework suggests that differences in returns to occupations are an increasingly important determinant of wage dispersion. Using the CPS, we do not find this to be the case. We find that a large and increasing share of the rise in wage inequality in recent decades (as measured by the increase in the variance of wages) occurred within detailed occupations. Furthermore, using DiNardo, Fortin, and Lemieux’s reweighting procedure, we do not find that occupations consistently explain a rising share of the change in upper tail and lower tail inequality for either men or women.
7. An expanded demand for low-wage service occupations is not a key driver of wage trends.
We are skeptical of the recent efforts of Autor and Dorn (2013) that ask the low-wage “service occupations” to carry much or all of the weight of the tasks framework. First, the small size and the slow, relatively steady growth of the service occupations suggest significant limitations of a technology-driven expansion of service occupations to be able to explain the large and contradictory changes in wage growth at the bottom of the distribution (i.e., between middle and low wages, the 50/10 wage differential), let alone movements at the middle or higher up the wage distribution. The service occupations remain a relatively small share of total employment; in 2007, they accounted for less than 13 percent of total employment, and just over half of employment in the bottom quintile of occupations ranked by wages. Moreover, these occupations have expanded only modestly in recent decades, increasing their employment share by 2.1 percentage points between 1979 and 2007, with most of the gain in the 2000s. Relative employment in all low-wage occupations, taken together, has been stable for the last three decades, representing a 21.1 percent share of total employment in 1979, 19.7 percent in 1999, and 20.0 percent in 2007.
Second, the expansion of service occupation employment has not driven their wage levels and therefore has not driven overall wage patterns. The timing of the most important changes in employment shares and wage levels in the service occupations is not compatible with conventional interpretations of the tasks framework. Essentially all of the wage growth in the service occupations over the last few decades occurred in the second half of the 1990s, when the employment share in these occupations was flat. The observed wage increases preceded almost all of the total growth in service occupations over the 1979–2007 period, which took place in the 2000s, when service occupation wages were falling (another trend that contradicts the overall claim of the explanatory power of service occupation employment trends).
8. Occupational employment trends provide only limited insights into the main dynamics of the labor market, particularly wage trends.
A more general point can and should be drawn from our findings: Occupational employment trends do not, by themselves, provide much of a read into key labor market trends because changes within occupations are dominant. Recent research and journalistic treatment of the labor market has highlighted the pattern of occupational employment growth to assess the extent of structural unemployment, the disproportionate increase in low-wage jobs, and the “coming of robots”—changes in workplace technology and the consequent impact on wage inequality. The recent academic literature on wage inequality has highlighted the role of changes in the occupational distribution of employment as the key factor. In particular, occupational employment trends have become increasingly used as indicators of job skill requirement changes, reflecting the outcome of changes in the nature of jobs and the way we produce goods and services. Our findings indicate, however, that occupational employment trends give only limited insight and leave little imprint on the evolution of the occupational wage structure, and certainly do not drive changes in the overall wage structure. We therefore urge extreme caution in drawing strong conclusions about overall labor market trends based on occupational employment trends by themselves.
I suppose I should note that I haven't read this closely enough yet to endorse every word (or not). Full paper here (scroll down).
Yet another travel day, can't remember the last weekend I was home (no complaints though), so one more from Brad DeLong and that's it for awhile:
Physiocracy and Robots, by Brad DeLong: The physiocrats saw France as having four kinds of jobs:
- Skilled artisans
- Landowning aristocrats
Farmers, they thought, produced the net value in the economy--the net product. Their labor combined with water, soil, and sun grew the food they and others ate. Artisans, the physiocrats thought, were best seen not as creators but as transformers of wealth--transformers of wealth in the form of food into wealth in the form of manufactures. Aristocrats collected this net product--agricultural production in excess of farmers' subsistence needs--and spent it buying manufactured goods and, when they got sated with manufactured goods, employing flunkies.
In this framework, the key economic variables are:
- the fraction f who are farmers.
- the net product per farmer n.
- the fraction who can be set to work making manufactured goods that aristocrats can consume before becoming sated m.
The key equilibrium quantity in this system is:
(nf-m)/(1-f-m) = W
This gives the standard of living of the typical flunky--say, a runner for His Grace the Cardinal. The numerator is the amount of resources on which flunkies can subsist. The denominator is the number of flunkies. If this quantity W is low, the country is poor: flunkies are ill-paid, begging and thievery are rampant, and the reserve army of potential unemployed puts downward pressure on artisan and farmer living standards as well. If this quantity is high, the country is prosperous.
The physiocrats saw a France undergoing a secular decline in the farmer share f, and they worried. A fall in f produced a sharper decline in W. Therefore they called for:
- Scientific farming to boost n and so boost the net product nf.
- A reallocation of the tax burden to make it less onerous to be a farmer--and so boost the farmer share f and so boost the net product nf.
With the unquestioned assumption that there were limits on how high the net product per farmer n could be pushed, the physiocrats would have forecast that France of today, with only 5% of the population farmers, would be a hellhole: huge numbers of ill-paid flunkies sucking up to the aristocratic landlords.
Well, the physiocrats were wrong about the decline of the agricultural share of the labor force. And let us hope that the techno-pessimists are similarly wrong about the rise of the robots.
Emmanuel Saez and Thomas Piketty:
Why the 1% should pay tax at 80%, by Emmanuel Saez and Thomas Piketty, theguardian.com: In the United States, the share of total pre-tax income accruing to the top 1% has more than doubled, from less than 10% in the 1970s to over 20% today (pdf). A similar pattern is true of other English-speaking countries..., however, globalization and new technologies are not to blame. Other OECD countries ... have seen far less concentration of income among the mega rich.
At the same time, top income tax rates on upper income earners have declined significantly since the 1970s... At a time when most OECD countries face large deficits and debt burdens, a crucial public policy question is whether governments should tax high earners more. The potential tax revenue at stake is now very large. ...
There is a strong correlation between the reductions in top tax rates and the increases in top 1% pre-tax income shares...
The ... data show that there is no correlation between cuts in top tax rates and average annual real GDP-per-capita growth since the 1970s. ... What that tells us is that a substantial fraction of the response of pre-tax top incomes to top tax rates may be due to increased rent-seeking at the top (that is, scenario three), rather than increased productive effort....
By our calculations about the response of top earners to top tax rate cuts being due in part to increased rent-seeking behavior and in part to increased productive work, we find that the top tax rate could potentially be set as high as 83% (as opposed to the 57% allowed by the pure supply-side model). ...
In the end, the future of top tax rates depends on what the public believes about whether top pay fairly reflects productivity or whether top pay, rather unfairly, arises from rent-seeking. With higher income concentration, top earners have more economic resources to influence both social beliefs (through thinktanks and media) and policies (through lobbying)...
The job of economists should be to make a top rate tax level of 80% at least "thinkable" again.
From the Atlanta Fed's macroblog:
The New Normal? Slower R&D Spending: In case you need more to worry about, try this: the pace of research and development (R&D) spending has slowed. The National Science Foundation defines R&D as “creative work undertaken on a systematic basis in order to increase the stock of knowledge” and application of this knowledge toward new applications. ...
R&D spending is often cited as an important source of productivity growth within a firm, especially in terms of product innovation. But R&D is also an inherently risky endeavor, since the outcome is quite uncertain. So to the extent that economic and policy uncertainty has helped make businesses more cautious in recent years, a slow pace of R&D spending is not surprising. On top of that, the federal funding of R&D activity remains under significant budget pressure. See, for example, here.
So you can add R&D spending to the list of things that seem to be moving more slowly than normal. Or should we think of it as normal?
The entrepreneurial state?, by Joshua Gans: In Slate, Marianna Mazzucato argues that it is a myth that entrepreneurs drive innovation. I’ll chalk that up to the Slate sub-editorial title writers because what the article is really saying is the ‘independent’ entrepreneurs do not drive innovation. Instead, in many classic situations the hand of the government was there and it is difficult and probably semantic to say who really drove innovation. It was probably combining the two.
Fair enough but then Mazzucato moves away from looking at evidence to call for changes that would increase the rate of innovation. And that is when she arrives at this:
It is time for the state to get something back for its investments. How? First, this requires an admission that the state does more than just fix market failures—the usual way economists justify state spending. The state has shaped and created markets and, in doing so, taken on great risks. Second, we must ask where the reward is for such risk-taking and admit that it is no longer coming from the tax systems. Third, we must think creatively about how that reward can come back.
There are many ways for this to happen. ...
Recognizing the state as a lead risk-taker, and enabling it to reap a reward, will not only make the innovation system stronger, it will also spread the profits of growth more fairly. This will ensure that education, health, and transportation can benefit from state investments in innovation, instead of just the small number of people who see themselves as wealth creators, while relying increasingly on the courageous, entrepreneurial state.
Now as I understand it, her argument is that what the state needs is to appropriate more of the returns from innovation it funds on its books. However, this is at the same time, that she argued that state success in funding and seeding innovation was precisely because it didn’t have to worry about getting the returns from innovation on its books. In other words, the problem with state-funded innovation is apparently it is not sufficiently like private funded innovation in its return calculus.
Again, for all I know that could be correct but if I were to guess the thing that makes the system work well now is precisely the diversity of goals between the state and private sector coming together to produce innovation rather than that being the problem.
Making Do With Less: Working Harder During Recessions, by Edward P. Lazear, Kathryn L. Shaw, Christopher Stanton, NBER Working Paper No. 19328 Issued in August 2013: There are two obvious possibilities that can account for the rise in productivity during recent recessions. The first is that the decline in the workforce was not random, and that the average worker was of higher quality during the recession than in the preceding period. The second is that each worker produced more while holding worker quality constant. We call the second effect, “making do with less,” that is, getting more effort from fewer workers. Using data spanning June 2006 to May 2010 on individual worker productivity from a large firm, it is possible to measure the increase in productivity due to effort and sorting. For this firm, the second effect—that workers’ effort increases—dominates the first effect—that the composition of the workforce differs over the business cycle.
Jared Bernstein responds to the Robert Shiller article I linked to yesterday:
Does the Government Stifle Innovation? I Don’t See It (To the Contrary…): I usually find economist Robert Shiller’s commentaries resonant and insightful, but this one seemed more confusing than enlightening. The thrust of the piece is the concern that government activities to promote innovation can just as easily stifle it.
The piece introduces the notion of corporatism, from a new book by Ed Phelps. What means “corporatism”? It’s:
…a political philosophy in which economic activity is controlled by large interest groups or the government. Once corporatism takes hold in a society…people don’t adequately appreciate the contributions and the travails of individuals who create and innovate. An economy with a corporatist culture can copy and even outgrow others for a while…but, in the end, it will always be left behind. Only an entrepreneurial culture can lead.
... I don’t get it. While “entrepreneurial culture” will always be essential, many innovations that turned out to be economically important in the US have government fingerprints all over them. From machine tools, to railroads, transistors, radar, lasers, computing, the internet, GPS, fracking, biotech, nanotech—from the days of the Revolutionary War to today—the federal government has supported innovation often well before private capital would risk the investment (read about it here).
Shiller’s critical, for example, of the manufacturing innovation institutes that the White House has been both touting and setting up. He’s certainly right to ask what it is these new creations do and why we need them... But most manufacturers I’ve spoken to about them tells me they fill an important niche, essentially building a path through the Death Valley between the university lab and the factory floor. If so, that’s a classic coordination failure in which markets have been known to underinvest. ...
To be clear, my argument is not at all that government efforts in this area are all successful or are somehow always free of the corruption that is too common when politics enters the fray. My points are that a) many important innovations have involved government support somewhere along the way, and b) while one could and should worry about waste in this area, I’ve not seen evidence, nor does Shiller provide any, of stifling. ...
So I’d suggest we be more careful in where we point the corporatist finger.
Quick one -- see previous post -- from Robert Shiller:
Why Innovation Is Still Capitalism’s Star, by Robert Shiller, Commentary, NY Times: Capitalism is culture. To sustain it, laws and institutions are important, but the more fundamental role is played by the basic human spirit of independence and initiative.
The decisive role of the “spirit of capitalism” is an old concept, going back at least to Max Weber, but it needs refreshing today with new evidence and new thinking. Edmund S. Phelps, a professor of economics at Columbia University and a Nobel laureate, has written an interesting new book on the subject. It’s called “Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge and Change” (Princeton University Press), and it contains a complex new analysis of the importance of an entrepreneurial culture.
Professor Phelps discerns a troubling trend in many countries, however, even the United States. He is worried about corporatism, a political philosophy in which economic activity is controlled by large interest groups or the government. Once corporatism takes hold in a society, he says, people don’t adequately appreciate the contributions and the travails of individuals who create and innovate. An economy with a corporatist culture can copy and even outgrow others for a while, he says, but, in the end, it will always be left behind. Only an entrepreneurial culture can lead.
Is the United States really becoming corporatist? I don’t entirely agree with such a notion. ...