Douglas O. Staiger, James H. Stock, and Mark W. Watson (1997): How Precise Are Estimates of the Natural Rate of Unemployment?: “Our main finding is that the natural rate is measured quite imprecisely. For example, we find that a typical estimate of the NAIRU in 1990 is 6.2%, with a 95% confidence interval for the NAIRU in 1990 being 5.1% to 7.7% (this is the “Gaussian” confidence interval for the quarterly specification with a con- stant NAIRU, reported in section 5.2)…” http://www.nber.org/chapters/c8885.pdf
Gavyn Davies: The Fed’s lowflation dilemma: “there is now a fly in the Fed’s ointment. The last two monthly releases for consumer price inflation have been much weaker than anyone expected…” https://www.ft.com/content/b165f756-e4bf-3a70-880f-74474f6538fa
Abbe Gluck: How the G.O.P. Sabotaged Obamacare: “Obamacare is not ‘collapsing under its own weight’…. The Republican Party should be held accountable not only for any potential replacement of the law, but also for having tried to starve it to death… https://www.nytimes.com/2017/05/25/opinion/republicans-obamacare-aca.html
Suppose you had, back in 1992 or 2004 or indeed any other time since 1990 and before 2013, asked a question of Charlie Evans—or, indeed, any of the other non-Neanderthal participants in the Federal Reserve’s Federal Open Market Committee meetings. Suppose you had asked whether a 25-54 employment-to-population ratio in the United States of today’s 78.5% was anywhere near “full employment”. What would they have said?
They would have said “of course not!”
They would have observed that the post-baby boom decline in fertility, wage stagnation among male earners, and the coming of feminism had greatly increased the share of 25-54 year old women who wanted paying jobs outside the home.
They would have pointed out that upward pressure on core inflation at an 80% 25-54 employment-to-population ratio in 1990 was small, that upward pressure on core inflation at an 82.5% 25-54 employment-to-population ratio in 2000 was minimal, and that upward pressure on core inflation at an 80% 25-54 employment-to-population ratio in 2007 was minimal.
They would have pointed out that today’s 78.5% was between between the 78.1% 1992 recession trough and the 78.6% 2003 recession trough.
They would have concluded that, by the standards of the post-feminist revolution era, a labor market with a 25-54 employment-to-population ratio of 78.5% was a labor market as bad from a business cycle perspective as the labor market got during the Great Moderation.
So why is Charlie Evans now saying that today the United States has “essentially returned to full employment”? Why no qualifiers? Why no “if you look only at the unemployment rate, and put the shockingly-low labor force participation statistics to one side…? Why no “it may well be the case that the U.S. has essentially returned to full employment? Why this certainty on the part of even the non-Neanderthal members of the FOMC—in public, at least—that the unemployment rate is the sole guide?
And why the puzzlement at the failure of core inflation to rise to 2%? That is a puzzle only if you assume that you know with certainty that the unemployment rate is the right variable to put on the right hand side of the Phillips Curve. If you say that the right variable is equal to some combination with weight λ on prime-age employment-to-population and weight 1-λ on the unemployment rate, then there is no puzzle—there is simply information about what the current value of λ is:
…more quickly than anticipated in 2013…. We were able to scale back the QE3 purchases…. Today, we have essentially returned to full employment in the U.S.
Unfortunately, low inflation has been more stubborn, being slower to return to our objective. From 2009 to the present, core PCE inflation, which strips out the volatile food and energy components, has underrun 2% and often by substantial amounts. This is eight full years below target. This is a serious policy outcome miss…
Charlie says a lot of good things in his talk. His discussions of “outcome-based policies… symmetric inflation target[s]… [and] risk management” are wise. But wisdom can be usefully applied only if you know where you start from. And we start from a position in which we really do not know how close the U.S. economy is right now to “full employment”—how much headroom for catch-up growth and catch-up employment remains, and how powerful and useful more aggressive policies to stimulate spending would be right now.
In 25 years my students are likely to ask me why the FOMC was so certain the U.S. was “essentially at full employment” today. What will I tell them?
Should-Read: A 25-54 employment-to-population ratio in the United States of today’s 78.5% as “full employment”, qualified only by an “essentially”? WTF?! Why isn’t this qualified—why isn’t this, instead: “today, we may have essentially returned to full employment”?
…more quickly than anticipated in 2013…. We were able to scale back the QE3 purchases…. Today, we have essentially returned to full employment in the U.S…
In responding to the global financial crisis back in 2007, central bankers in many countries tried policies that had once seemed unthinkable. Nominal interest rates were slashed to zero and kept there for years. Central banks purchased trillions of dollars of assets, and their communication practices were radically changed. The recovery from the crisis over the past several years also sparked debate about even further changes to central bank tools and targets. Now, almost 10 years after the start of the crisis, will some of these new policies endure?
One way to figure out what post-crisis monetary policy tools are still considered to be useful is to ask the people most likely to make those decisions—central bankers and academic economists who study monetary policy. A recent paper by Alan Blinder of Princeton University, Michael Ehrmann of the European Central Bank, Jakob de Haan of the University of Groningen, and David-Jan Jansen of De Nederlandsche Bank does exactly that. The four economists sent out a survey to academic economists whose research covers monetary policy and central bankers across the world. The central bankers were from 55 different banks, and 16 of them were from high-income countries. The 159 economists who replied were much more concentrated in high-income countries, with 101 of them currently living in the United States.
Academics appear to be much more willing to keep the new unconventional tools of the past few years, while central bankers are a bit more hesitant. One case in point: 68 percent of the surveyed academics want to keep quantitative easing—the purchasing of large quantities of assets—using government debt in the policy toolkit. Compare that with the 35 percent of central bankers who want to keep quantitative easing as a viable policy measure. The gap disappears when only central bankers from high-income countries are included, with about 54 percent of those bankers wanting to keep it around.
A more significant difference is related to forward guidance. This form of central bank communication calls for central banks to forecast publicly future policy decisions such as interest rate hikes. Both central bankers and academics want to keep forward guidance around, but they are torn between what kind of guidance is best. Academics, about 69 percent of them, favor data-based forward guidance, where changes in policies are tied to specific thresholds such as a level of the unemployment rate. Central bankers tend to prefer more qualitative forward guidance, which allows for more wiggle room for changes in policy.
So it seems from the survey done by the four economists that several of the key monetary policy innovations of the crisis era are likely to remain in central bankers’ toolkits. Yet academics and central bankers also should consider the policies they didn’t pull out during the panic, such as nominal gross domestic product targeting. Remember that several unconventional ideas back in 2007 eventually became real-world policy. Who knows what the next downturn might require central bankers to turn to spark a turnaround.
…She said that “the times in which we can fully count on others are somewhat over, as I have experienced in the past few days.”… Trump, who has previously called global warming a hoax, came under concerted pressure from the other leaders to honour the 2015 Paris Agreement on curbing carbon emissions. Although he tweeted to say he would make a decision next week, his apparent reluctance to embrace the first legally binding global climate change deal, signed by 195 countries, clearly annoyed Ms Merkel.
“The entire discussion about climate was very difficult, if not to say very dissatisfying,” she told reporters. “There are no indications whether the United States will stay in the Paris Agreement or not.” G7 leaders went on to blame the US for the failure to reach an agreement on climate change, in an unusually frank statement which read:
The United States of America is in the process of reviewing its policies on climate change and on the Paris Agreement and thus is not in a position to join the consensus on these topics. Understanding this process, the heads of state and of government of Canada, France, Germany, Italy, Japan and the United Kingdom and the presidents of the European Council and of the European Commission reaffirm their strong commitment to swiftly implement the Paris Agreement…
Must-Read: The Federal Funds rate is currently bouncing around between 0.82 and 0.91%. When the Federal Reserve embarked on its tightening cycle in December 2015, its median expectation was that by now it would have raised the Federal Funds rate to between 2.25 and 2.50%—that it would have undertaken 9 25 basis point interest rate hikes rather than three. Its expectation was that, even after those nine hikes, PCE core inflation would be running at 1.9% per year rather than the 1.5% per year that the smart money currently sees.
A policy significantly looser than they thought they were embarking on. And inflation outcomes noticeably worse, in the sense of falling below target, than they anticipated even with the tighter policies they thought they would adopt.
Yet I have very little sense of how the Federal Reserve is adjusting its thinking to its forecasting overoptimism for 2016 and now for 2017. Nor do I have any great sense of how the Federal Reserve is dealing with the fact that it has now been overoptimistic in forecasting 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, and 2008:
…When the PCE deflator is released next Tuesday, it will probably show the 12-month core inflation rate at 1.5 per cent in April, the lowest figure since the end of 2015…. The Fed’s decisions are supposed to be data dependent…. The Fulcrum inflation models… produce short term projections for inflation based on methods that extract underlying price increases from noisy monthly data. The models’ near term inflation projections (red line) have dropped sharply as a result of the March and April CPI announcements, and the inflation rate for the rest of 2017 is now projected to run well below the rates forecast by the Fed in March (blue dots)….
Where does that leave the FOMC? I agree with Tim Duy that their present stance is still biased towards gradual tightening, because they are not yet placing much weight on changes in data methodology, or in the Phillips Curve. They have now dug themselves into a position where they will be extremely reluctant to drop the intended 25 basis points increase in the fed funds rate on 14 June, or the start of balance sheet shrinkage, probably announced in September. Lowflation will probably be less evident in coming months. Only if that fails to happen will monetary policy normalisation be placed on hold.
Let me follow the example of our Lord and Master Alpha-Go as it takes the high ground first.
Let me, therefore, take the hyper-Olympian and very long run historical point of view.
The human brain is a massively parallel supercomputer that fits inside half a shoebox. It draws 50 watts of power. It is an amazing innovation, analysis, assessment and creation machine. 600 million years of proto-mammalian and mammalian evolution coupled with the genetic algorithm means that almost every single human can solve AI problems far beyond our current engineering reach—so much so that much of what our machines find impossible our brains find so trivially easy that we call such capabilities “unskilled”.
When combined with our brains, human fingers are amazingly fine manipulation devices.
Human back and leg muscles—especially when testosterone soaked—are quite good at moving heavy objects.
Thus back in the environment of evolutionary adaptation, we used our brains, our big muscles, and our fingers to lead cognitively interesting if stressful and short lives.
But history has rolled forward since the hunter-gatherer age. And as history has rolled forward, we have figured out other things to do to add economic and sociological value than their uses in the hunger-gathers paradigm. Over the long historical sweep, the ability to add value using our backs to move heavy objects and our fingers to perform fine manipulations in cognitively-interesting ways has, relatively, declined. We have, so far:
turned many of us into robots ourselves, performing simple routinized repetitive and vastly boring tasks to fill in the gaps in value chains between the robots that we know how to build.
found jobs as microcontrollers for domesticated animals and machines—the horse does not know what plowing the furrow is.
found jobs as relatively simple accounting and software bots, keeping track of stuff, what it is useful for, and how its use is to be decided.
become personal servitors.
become social engineers—trying to keep all those things and all those people—especially, perhaps, trying to keep those brains soaked in testosterone—somehow working in harmony, somehow pulling together, although admittedly with limited success.
remained innovators, analyzers, assessors, and creators as well.
Backs started to go out with the domestication of the horse. Fingers began to go out with the invention of the spinning jenny. But humans-as-microcontrollers, humans-as-accounting-‘bots—paper shufflers—and humans-as-the-robots we cannot yet build—took up all the job slack. Every horse needs a microcontroller. And a human
brain was the only possible option. Even today, to a large amount every textile machine needs a human watching it at least part of the time. It doesn’t know when it’s gone wrong. It has no clue how to fix itself. It no more understands the idea of “fixing” any more than Alpha-Go understands that it is playing Go, and not just solving a problem of outputting a two-element vector in response to a 19 x 19 matrix of inputs with the additional structure that the output changes the matrix and that the possible matrices have a value-function structure.
Now, however, we can finally peer into a future in which the microcontrollers and the accounting bots are on their way out in a manner analogous to the backs and the fingers. Fortunately, this brings with it the forthcoming extinction of the the jobs that treat humans as simple robots: simple cogs in the machine that is Henry Ford’s River Rouge assembly line. Many occupations that vastly underutilize the massively parallel supercomputer that fits in half a shoebox are on the way out—and good: for those are not properly “human” jobs at all.
That leaves us with a future of work—not next year, and not next decade, but further out by some unknown time—in which humans’ jobs will be as:
personal servitors,
social engineers, and
innovators, analyzers, assessors’ nd creators.
And here we might well, someday, have a huge problem.
The market economy will amply fund AI research that replaces workers in capital intensive production processes by machines. Such industries have mammoth returns to scale. They thus tend to be characterized by large oligopolies. And so the firm that funds such labor-replacing research will capture with its own scale and in its own value chain a substantial part of the benefits of such R&D. But the market economy will to amply fund AI research that assists and amplifies workers in labor intensive production processes. Such tend to be small scale. The inventors and the innovators cannot capture even a small part of the benefit in their own production processes and value chains. And intellectual property is a very weak reed indeed to rely on to fix the problem—in fact, intellectual property is more likely to be the problem than the solution, cf. Nathan Myhrvold, and Intellectual Ventures.
That means that the combination of coming AI with a market economy will be absolute poison for equity and equitable growth. It will race ahead with the first: shedding workers in capital intensive production processes. Yet AI could be gold for equity: amplifying the capabilities of workers in labor intensive production processes would, as John Maynard Keynes once said, bring us vastly closer to economic El Dorado.
Utopia or dystopia? Heaven or hell? I turn that over to you. And by “you”, I definitely include our engineering dean Shankar Sastry. Because firms will not invest on a large scale in AI that amplifies the capabilities of labor in labor intensive industries, it will not happen unless some NGO does. How about an engineering school? How about an engineering school like an engineering school at a public university?
Since I get to go first, I will preemptively take the hyper-Olympian and very long-run historical point of view…
The human brain is a massively parallel supercomputer that fits in half a shoebox. It draws 50 W of power. 600 million years of proto- and mammalian evolution mean that almost every single human can solve AI problems that our machines cannot—what our machines find very hard or impossible, our brains find so trivially easy that we call such capabilities “unskilled”.
Human fingers are amazingly fine manipulation devices. Human back and leg muscles—especially when testosterone soaked—are quite good at moving heavy objects. And so, back in the environment of evolutionary adaptation, we used our brains, big muscles, and fingers to lead interesting, if stressful and short, lives.
But as history has enrolled we have done other things to add economic and sociological value than use our backs, our fingers, and our brains to innovate and create. Over the long historical sweep, backs and fingers have declined and we have turned many of us into, instead:
robots performing repetitive tasks,
microcontrollers for domesticated animals and machines,
relatively simple accounting and recording software bots,
personal servitors,
social engineers trying to keep all those things controlled by brains—especially by the testosterone soaked ones—working together harmoniously. With limited success.
while remaining innovators and creators.
Backs started to go out with the domestication of the horse. Fingers with the invention of the spinning jenny. Microcontrollers and accounting ‘bots, we can see, are now on the way out too. So, fortunately, are the jobs that treat humans as simple robots.
That leaves us with a future of work made up of:
personal servitors,
social engineers,
innovators and creators.
The market economy will fund AI that replaces workers in capital-intensive production processes. Such are large scale and oligopolistic: firms profit from R&D because they capture a significant portion of efficiencies in their value chains. There is no equivalent market force funding AI that assists and amplifies workers in labor-intensive production processes.
The first is poison for equity and inclusion. The second is gold.
That second is one thing this NGO institution that surrounds us would be good at doing, and needs to do.
Utopia or dystopia? Heaven or hell?
Over to you, James. And, in a broader sense, over to all of you—in the audience, and out there in Internet land.
Khadeeja Safdar: J.Crew’s Mickey Drexler Confesses: I Underestimated How Tech Would Upend Retail: “Retail legend didn’t understand how speed and price would drive internet shoppers; 10 quarters of falling sales…. Now, competitors with high-tech, data-driven supply chains can copy styles faster and move them into stores in a matter of weeks…” https://www.wsj.com/articles/j-crews-big-miss-how-technology-transformed-retail-1495636817
Harvard Class of 1982 35th Reunion :: Science Center B :: Saturday, May 27, 2018, 10:45-12:00 noon
Seth Lloyd, MIT: Moderator
Brad DeLong, U.C. Berkeley
Ivonne Garcia, Kenyon
Noel Michele Holbrook, Harvard
William Sakas, CUNY
Carol Steiker, Harvard
In the spring of our freshman year, then-young economics professor Richard Freeman came to Ec 10 to tell us that going to Harvard would not make us rich.
He was wrong.
Up until 1980 America was winning, and Richard Freeman expected it to keep on winning, the race between education and technology: Thus there were ample numbers of people to take the increasing number of jobs requiring formal education for first class performance. Thus the amount the market paid you extra for taking a college requiring rather than a high school requiring job was modest: 30% or so–not enough to make up for the income you would’ve earned, had you taken the tuition you would not have spent and the extra wages you would have made from working, and put them into some reasonable investment.
But after 1980 America began to lose the race between education and technology.
The expansion of American higher education slowed massively. Higher education for native-born males simply froze in its tracks. As a result, in the world in which we have worked for the past 35 years employers have been betting up the relative price of college graduates: Rather than making 30% more than our counterparts who went straight into the job market after high school did, we have on average received double.
The freezing and of the relative numbers of native born American males taking advantage of hire education as demand, supply, and heterogeneity components.
On the demand-side, states withdrew tuition subsidies. Public college ceased to be free. Those whose parents were not rich worried about their student loans: what if they didn’t succeed and finish and could not get one of those high paying jobs? How were they going to pay back their loans? Americans almost surely over worry about this. But people are who they are, and not who economic theory dictates they should rationally be.
On the supply side, states stopped building campuses. Getting the courses you wanted and needed at public universities became iffy: five or six years rather than four.
And on the heterogeneity side, our colleges are designed for those who take to print literacy and to Arabic mathematics like ducks to water–if you do not have that, or are not trained to have that, learning the way we are taught to teach becomes much more difficult. We economists see this every semester, as even Ec 10 requires great facility in reading, in arithmetic, in algebra, and in algebraic geometry. The extra slice of the population that we would have been sending to higher education in a better counterfactual world in which America had not lost the race between education and technology would have been less well prepared and less suited to benefit.
What is the balance between these supply, demand, and heterogeneity considerations? That, we say, is a research problem.
How important is all this? I would say that about 1/3 of the problem is with America that have developed over the past 35 years–1/3 of the ways in which I see America today falling far short of what I confidently helped America would be by now–are due to our losing the race between education and technology.
Let me make one final point: Over the past generation, Harvard has not helped. We had 1600 in our class. Last week’s graduating class was essentially the same size. Worldwide, between five and ten times as many people are well-qualified to join my niece as freshmen this fall. In our class there were perhaps four times as many people well-qualified to attend as Harvard admitted. Today there are between twenty and forty. Yet Presidents Bok, Pusey, and Rudenstine seemed to have little interest in helping America and the world in the race between education and technology. Contrast that with the University of California, which, under Chancellor and President Clark Kerr and California Governor Pat Brown, set in motion the plan to clone itself across the state and increase enrollment tenfold.
If you are thinking about giving money to help America win this race with education and technology, I would not recommend Harvard. U.C. Berkeley, Columbia, and MIT for moving people whose parents’ were in the bottom quintile into the top 1%. And for overall bottom fifth to top fifth mobility? CUNY. U.T.-Pan American. TCI. SUNY Stonybrook. Pace. and Cal State-LA. That is what Yagan, Turner, Saez, Friedman, and Chetty say… http://www.equality-of-opportunity.org/papers/coll_mrc_paper.pdf.
