At its December meeting, the Federal Reserve raised its benchmark interest rate a quarter point. The move, while widely expected, represented a clear rebuke to President Trump, who has repeatedly urged the Fed to keep rates low. He took to Twitter after the move to attack Fed head Jerome Powell as a golfer who has no touch (“he can’t putt”)—strong words in the president’s social circle.
Trump’s critics on the left may be tempted to cheer the Fed’s decision as a welcome triumph of the separation of powers. But opposing him on the grounds that the labor market is already great may end up weakening the case for a progressive agenda. We need to consider the possibility that, in this one case, the president is right.
By raising rates, the Fed is signaling that it thinks that the economy is now operating at potential, or full employment. Conventional economic theory says that when the economy is below potential, more spending will bring unemployed and underemployed people to work, and more fully utilize structures and equipment, but once potential is reached, additional spending will just lead to higher prices. So when output is below potential, anything that raises spending—whether it is tax cuts, increased federal spending, a more favorable trade balance, or lower interest rates—is macroeconomically useful. But once the economy is at potential, and there are no more unemployed people or underused buildings and machines, the same policies will lead only to more inflation.
By this standard, the case for the most recent rate increase was plausible, though not a slam dunk. By the official measures produced by the Bureau of Economic Analysis (BEA), 2018 was the first year since 2007 that GDP reached potential, and at 3.7 percent, the headline unemployment rate is quite low by historical standards. So textbook logic suggests that if demand growth does not slow, inflation is likely to rise.
The past decade, however, has given us reason to doubt the textbook models. As I argued in the Roosevelt report What Recovery?, it is far from clear that the BEA’s measure does a good job capturing the productive potential of the economy. Similarly, the headline unemployment rate may no longer be a good measure of the economically relevant category of people available for work; many people move directly between being out of the laborforce and being employed. The behavior of inflation has defied any mechanical linkage with GDP growth, wages, or unemployment. And even if one accepts that output is nearing potential, a higher interest rate may not be necessary to slow it. (This is related to the idea of r*, the “neutral” rate of interest, which neither raises nor lowers demand—something that many people, including Powell himself, have suggested we don’t actually know.) Given these uncertainties, manypeople—across the political spectrum—have argued that it’s foolish for the central bank to try to make policy based on guesses of where inflation is heading. Instead, they should wait to raise rates until it is clear that inflation is above target.
More broadly, the question of whether the economy is at full employment implies a judgement on whether this is the best we can do, economically. Are the millions of people who have dropped out of the laborforce over the past decade really unable or unwilling to engage in paid work? Is the decline of American manufacturing the inevitable result of a lack of competitiveness? Are the millions of people working at low-wage, dead-end jobs incapable of doing anything more rewarding? The decision to raise rates implicitly assumes that the answers are yes. People who think that the economy could work better for ordinary people should hesitate to agree.
We live in a country filled with energetic, talented, creative people, many of whom are forced to spend their days doing tedious busywork. Personally, I find it offensive to claim that a job at McDonald’s or in a nail salon or Amazon warehouse is the fullest use of anyone’s potential. When John Maynard Keynes said “we will build our New Jerusalem out of the labour which in our former vain folly we were keeping unused and unhappy in enforced idleness,” he didn’t only mean literal idleness, but wasted labor more broadly. In a society in which aggregate expenditure was constantly pushing against supply constraints, millions of people today who spend their working hours in menial, unproductive activities would instead be developing their capacities as engineers, artists, electricians, doctors, and scientists.
Progressives concerned about the distribution of income should also pause before cheering an interest rate hike. The textbook model assumes that wage changes are passed more or less one for one to prices (that’s why the Fed pays so much attention to unemployment). But we know that this is not true. Slow wage growth may simply mean a lower share of income going to workers, rather than lower inflation, and high wages may lead to an increase in labor share rather than to higher inflation. Indeed, as a matter of math, the labor share of income cannot rise unless wages rise faster than the sum of productivity growth and inflation. For most of the past decade—and much of the decade before—wages have risen more slowly than this. As a result, labor compensation has fallen to 58 percent of value added in the corporate sector (where it is most reliably measured), down from 60 percent a decade ago and 66 percent in 2000. The only way that this shift from labor to capital can be reversed is if we see an extended period of “excessive” wage growth. This recent hike suggests that the Fed will not tolerate that.
The alternative is to deliberately foster what is sometimes called a “high-pressure” economy. Allowing the unemployment rate to remain low enough for sustained rapid wage growth won’t just help restore the ground that workers have lost over the past decade. It could also boost laborforce participation, as discouraged workers return to the labor market. And it could boost productivity, as scarce workers and strong demand encourage businesses to undertake labor-saving investment. An increasing number of economists think that these kinds of effects, called hysteresis, mean that weak demand conditions can reduce the economy’s productive potential—and strong demand can increase it.
We are already seeing some signs of this. The fall in the laborforce participation over the past decade was, according to moststudies, was much larger than can be explained by aging and other demographic factors. Now, as the labor market gets stronger, people who dropped out of the laborforce are reentering it. Some businesses in low-unemployment areas are now paying for English lessons so they can hire non-English speaking immigrants, who are normally among the last to be employed. After years of stagnation, wages are beginning to rise fast enough to produce a modest rise in the hare of output going to workers—the predictable result of a strong labor market. A recent study by the Federal Reserve Bank of Atlanta confirmed that a high-pressure economy, with unemployment well below normal levels, can boost earnings and strengthen attachment to the laborforce. The effects are long-lasting and strongest for those at the back of the hiring queue, such as Black Americans and those with less-formal education. Labor productivity has yet to pick up, but business investment is now quite strong, so it is likely that productivity may soon start rising as well. None of these gains will be realized if the Fed acts too quickly to rein in a boom.
Critics of the president who argue that the economy is already at full employment risk replaying the 2016 election, where the Democrats were perceived—fairly or not—as defenders of the status quo, while Trump spoke to and for those left behind by the recovery. And they risk throwing away one of the best arguments for a progressive program in 2021 and beyond. The next Democratic president will enter office with an ambitious agenda. Whether the top priority is Medicare for All, a Green New Deal, universal childcare, or free higher education, realizing this agenda will require a substantial increase in government spending. Making the case for this will be much easier if there is broad agreement that the economy still suffers from a demand shortfall that public spending can fill.
EDIT: The one thing I did not mention here and should have is that the principle of central bank indpedence is also not something that anyone on the left should be defending. Like the various countermajoritarian features of the US political system, it will be wielded more aggressively against any kind of progressive program. And as Mike Konczal and I have argued, both financial crises and extended periods of weak demand have forced central banks to broaden their mandate, making it much harder to mark off “monetary policy” proper from economic policy in general.
Below the fold is a draft of a chapter I’m contributing to an edited volume on aggregate demand and employment. My chapter is supposed to cover macroeconomic policy and employment in the US, with other chapters covering other countries and regions.
The chapter is mostly based on material I’ve pulished elsewhere, mainly my Roosevelt papers “What Recovery?” and “A New Direction for the Federal Reserve.” My goal was something that summarized the arguments there for an audience of (presumably) heterodox macroeconomists, and that could also be used in the classroom.
There is still time to revise this, so comments/criticisms are very welcome.
In response to my earlier post on climate change and aggregate demand, Lance Taylor sends along his recent article “Economic Growth, Income Distribution, and Climate Change,” coauthored with Duncan Foley and Armon Rezai.
The article, which was published in Ecological Economics, lays out a structuralist growth model with various additions to represent the effects of climate change and possible responses to it. The bulk of the article works through the formal properties of the model; the last section shows the results of some simulations based on plausible values of the various parmaters. 1 I hadn’t seen the article before, but its conclusions are broadly parallel to my arguments in the previous two posts. It tells a story in which public spending on decarbonization not only avoids the costs and dangers of climate change itself, but leads to higher private output, income and employment – crowding in rather than crowding out.
Before you click through, a warning: There’s a lot of math there. We’ve got a short run where output and investment are determined via demand and distribution, a long run where the the investment rate from the short run dynamics is combined with exogenous population growth and endogenous productivity growth to yield a growth path, and an additional climate sector that interacts with the economic variables in various ways. How much the properties of a model like this change your views about the substantive question of climate change and economic growth, will depend on how you feel about exercises like this in general. How much should the fact that that one can write down a model where climate change mitigation more than pays for itself through higher output, change our beliefs about whether this is really the case?
For some people (like me) the specifics of the model may be less important that the fact that one of the world’s most important heterodox macroeconomists thinks the conclusion is plausible. At the least, we can say that there is a logically coherent story where climate change mitigation does not crowd out other spending, and that this represents an important segment of heterodox economics and not just an idiosyncratic personal view.
If you’re interested, the central conclusions of the calibrated model are shown below. The dotted red line shows the business-as-usual scenario with no public spending on climate change, while the other two lines show scenarios with more or less aggressive public programs to reduce and/or offset carbon emissions.
Here’s the paper’s summary of the outcomes along the business-as-usual trajectory:
Rapid growth generates high net emissions which translate into rising global mean temperature… As climate damages increase, the profit rate falls. Investment levels are insufficient to maintain aggregate demand and unemployment results. After this boom-bust cycle, output is back to its current level after 200 years but … employment relative to population falls from 40% to 15%. … Those lucky enough to find employment are paid almost three times the current wage rate, but the others have to rely on subsistence income or public transfers. Only in the very long run, as labor productivity falls in response to rampant unemployment, can employment levels recover.
In the other scenarios, with a peak of 3-6% of world GDP spent on mitigation, we see continued exponential output growth in line with historical trends. The paper doesn’t make a direct comparison between the mitigation cases and a world where there was no climate change problem to begin with. But the structure of the model at least allows for the possibility that output ends up higher in the former case.
The assumptions behind these results are: that the economy is demand constrained, so that public spending on climate mitigation boosts output and employment in the short run; that investment depends on demand conditions as well as distributional conflict, allowing the short-run dynamics to influence the long-run growth path; that productivity growth is endogenous, rising with output and with employment; and that climate change affects the growth rate and not just the level of output, via lower profits and faster depreciation of existing capital.2
This is all very interesting. But again, we might ask how much we learn from this sort of simulation. Certainly it shouldn’t be taken as a prediction! To me there is one clear lesson at least: A simple cost benefit framework is inadequate for thinking about the economic problem of climate change. Spending on decarbonization is not simply a cost. If we want to think seriously about its economic effects, we have to think about demand, investment, distribution and induced technological change. Whether you find this particular formalization convincing, these are the questions to ask.
I’ve gotten some pushback on the line from my decarbonization piece that “wartime mobilization did not crowd out civilian production.” More than one person has told me they agree with the broader argument but don’t find that claim believable. Will Boisvert writes in comments:
Huh? The American war economy was an *austerity* economy. There was no civilian auto production or housing construction for the duration. There were severe housing shortages, and riots over housing shortages. Strikes were virtually banned. Millions of soldiers lived in barracks, tents or foxholes, on rations. So yeah, there were drastic trade-offs between guns and butter (which was rationed for civilians).
It’s true that there were no new cars produced during the war, and very little new housing.1 But this doesn’t tell us what happened to civilian output in general. For most of the war, wartime planning involved centralized allocation of a handful of key resources — steel, aluminum, rubber — that were the most important constraints on military production. This obviously ruled out making cars, but most civilian production wasn’t directly affected by wartime controls. 2 If we want to look at what happened to civilian production overall, we have to look at aggregate measures.
The most comprehensive discussions of this I’ve seen are in various pieces by Hugh Rockoff.3 Here’s the BEA data on real (inflation-adjusted) civilian and military production, as he presents it:
As you can see, civilian and military production rose together in 1941, but civilian production fell in 1942, once the US was officially at war. So there does seem to be some crowding out. But looking at the big picture, I think my claim is defensible. From 1939 to its peak in 1944, annual military production increased by 80 percent of prewar GDP. The fall in real civilian production over this period was less than 4 percent of prewar GDP. So essentially none of the increase in military output came at the expense of civilian output; it was all additional to it. And civilian production began rising again before the end of the war; by 1945 it was well above 1939 levels.
Production is not the same as living standards. As it happens, civilian investment fell steeply during the war — in 1943-44, it was only about one third its prewar level. If we look at civilian consumption rather than output, we see a steady rise during the war. By the official numbers, real per-capita civilian consumption was 5 percent higher in 1944 – the peak of war production — than it had been in 1940. Rockoff believes that, although the BLS did try to correct for the distortions created by rationing and price controls, the official numbers still understate the inflation facing civilians. But even his preferred estimate shows a modest increase in per-capita civilian consumption over this period.
We can avoid the problems of aggregation if we look at physical quantities of particular goods. For example, shoes were rationed, but civilians nonetheless bought about 5 percent more shoes annually in 1942-1944 than they had in 1941. Civilian meat consumption increased by about 10 percent, from 142 pounds of meat per person in 1940 to 154 pounds per person in 1944. As it happens, butter seems to be one of the few categories of food where consumption declined during the war. Here’s Rockoff’s discussion:
Consumption of edible fats, particularly butter, was down somewhat during the war. Thus in a strict sense the United States did not have guns and butter. The reasons are not clear, but the long-term decline in butter consumption probably played a role. Ice cream consumption, which had been rising for a long time, continued to rise. Thus, the United States did have guns and ice cream. The decline in edible fat consumption was a major concern, and the meat rationing system was designed to provide each family with an adequate fat ration. The concern about fats aside, [civilian] food production held up well.
As this passage suggests, rationing in itself should not be seen as a sign of increased scarcity. It is, rather, an alternative to the price mechanism for the allocation of scarce goods. In the wartime setting, it was introduced where demand would exceed supply at current prices, and where higher prices were considered undesirable. In this sense, rationing is the flipside of price controls. Rationing can also be used to deliver a more equitable distribution than prices would — especially important where we are talking about a necessity like food or shoes.
The fundamental reason why rationing was necessary in the wartime US was not that civilian production had fallen, but because civilian incomes were rising so rapidly. Civilian consumption might have been 5 percent higher in 1944 than in 1940; but aggregate civilian wages and salaries were 170 percent higher. Prices rose somewhat during the war years; but without price controls and rationing inflation would undoubtedly have been much higher. Rockoff’s comment on meat probably applies to a wide range of civilian goods: “Wartime shortages … were the result of large increases in demand combined with price controls, rather than decreases in supply.”
Another issue, which Rockoff touches on only in passing, is the great compression of incomes during the war. Per Piketty and co., the income share of the top 10 percent dropped from 45 percent in 1940 to 33 percent in 1945. If civilian consumption rose modestly in the aggregate, it must have risen by more for the non-wealthy majority. So I think it’s pretty clear that in the US, civilian living standards generally rose during the war, despite the vast expansion of military production.
You might argue that even if civilian consumption rose, it’s still wrong to say there was no crowding out, since it could have risen even more without the war. Of course one can’t know what would have happened; even speculation depends on what the counterfactual scenario is. But certainly it didn’t look this way at the time. Real per capita income in the US increased by less than 2 percent in total over the decade 1929-1939. So the growth of civilian consumption during the war was actually faster than in the previous decade. There was a reason for the popular perception that “we’ve never had it so good.”
It is true that there was already some pickup in growth in 1940, before the US entered the war (but rearmament was already under way). But there was no reason to think that faster growth was fated to happen regardless of military production. If you read stuff written at the time, it’s clear that most people believed the 1930s represented, at least to some degree, a new normal; and no one believed that the huge increase in production of the war years would have happened on its own.
Will also writes:
War production itself was profoundly irrational. Expensive capital goods were produced, thousands of tanks and warplanes and warships, whose service lives spanned just a few hours. Factories and production lines were built knowing that in a year or two there would be no market at all for their products.
I agree that military production itself is profoundly irrational. Abolishing the military is a program I fully support. But I don’t think the last sentence follows. Much wartime capital investment could be, and was, rapidly turned to civilian purposes afterwards. One obvious piece of evidence for this is the huge increase in civilian output in 1946; there’s no way that production could increase by one third in a single year except by redirecting plant and equipment built for the military.
And of course much wartime investment was in basic industries for which reconversion wasn’t even necessary. The last chapter of Mark Wilson’s Destructive Creation makes a strong case that postwar privatization of factories built during the war was very valuable for postwar businesses, and that acquiring them was a top priority for business leaders in the reconversion period. 4 By one estimate, in the late 1940s around a quarter of private manufacturing capital consisted of plant and equipment built by the government during the war and subsequently transferred to private business. In 1947, for example, about half the nation’s aluminum came from plants built by the government during the war for aircraft production. All synthetic rubber — about half total rubber production — came from plants built for the military. And so on. While not all wartime investment was useful after the war, it’s clear that a great deal was.
I think people are attracted to the idea of wartime austerity because we’ve all been steeped in the idea of scarcity – that economic problems consist of the allocation of scarce means among alternative ends, in Lionel Robbins’ famous phrase. Aggregate demand is, in that sense, a profoundly subversive idea – it suggests that’s what’s really scarce isn’t our means but our wants. Most people are doing far less than they could be, given the basic constraints of the material world, to meet real human needs. And markets are a weak and unreliable tool for redirecting our energies to something better. World War II is the biggest experiment to date on the limits of boosting output through a combination of increased market demand and central planning. And it suggests that, altho supply constraints are real — wartime controls on rubber and steel were there for a reason – in general we are much, much farther from those constraints than we normally think.
The International Economy has asked me to take part in a couple of their recent roundtables on economic policy. My first contribution, on productivity growth, is here (scroll down). My second one, on green investment, is below. But first, I want to explain a little more what I was trying to do with it.
I am not trying to minimize that challenge of dealing with the climate change. But I do want to reject one common way of thinking about those challenges — as a “cost”, as some quantity of other needs that will have to go unmet. I reject it because output isn’t fixed — a serious effort to deal with climate change will presumably lead to a boom with much higher levels of employment and investment. And more broadly I reject it because it’s profoundly wrong to think of the complex activities of production as being equivalent to a certain quantity of “stuff”.
There’s a Marxist version of this, which I also reject — that the reproduction of capitalism requires an ever-increasing flow of material inputs and outputs, which rules out any kind of environmental sustainability. I think this mistakenly equates the situation facing the individual capitalist — the need to maximize money sales relative to money outlays — with the logic of the system as a whole. There is no necessary link between endless accumulation of money and any particular transformation of the material world. To me the real reason capitalism makes it so hard to address climate change isn’t any objective need to dump carbon into the atmosphere. It’s the obstacles that private property and the pursuit of profit — and their supporting ideologies — create for any kind of conscious reorganization of productive activity.
The question was, who will be the winners and losers from the transition away from carbon? Here’s what I wrote:
The response to climate change is often conceived as a form of austerity—how much consumption must we give up today to avoid the costs of an uninhabitable planet tomorrow? This way of thinking is natural for economists, brought up to think in terms of the allocation of scarce means among competing ends. By some means or other—prices, permits, or plans—part of our fixed stock of resources must, in this view, be used to prevent (or cope with) climate change, reducing the resources available to meet other needs.
The economics of climate change look quite different from a Keynesian perspective, in which demand constraints are pervasive and the fundamental economic problem is not scarcity but coordination. In this view, the real resources for decarbonization will not have to be with- drawn from other uses. They can come from an expansion of society’s productive capabilities, thanks to the demand created by clean-energy investment itself. Addressing climate change need not imply a lower standard of living—if it boosts employment and steps up the pace of technological change, it may well lead to a higher one.
People rightly compare the scale of the transition to clean technologies to the mobilization for World War II. Often forgotten, though, is that in countries spared the direct destruction of the fighting, like the United States, wartime mobilization did not crowd out civilian production. Instead, it led to a remarkable acceleration of employment and productivity growth. Production of a liberty ship required 1,200 man hours in 1941, only 500 by 1944. These rapid productivity gains, spurred by the high-pressure economy of the war, meant there was no overall tradeoff between more guns and more butter.
At the same time, the degree to which all wartime economies—even the United States—were centrally planned, reinforces a lesson that economic historians such as Alexander Gerschenkron and Alice Amsden have drawn from the experience of late industrializers: however effective decentralized markets may be at allocating resources at the margin, there is a limit to the speed and scale on which they can operate. The larger and faster the redirection of production, the more it requires conscious direction—though not necessarily by the state.
In a world where output is fundamentally limited by demand, action to deal with climate change doesn’t require sacrifice. Do we really live in such a world? Think back a few years, when macroeconomic discussions were all about secular stagnation and savings gluts. The headlines may have faded, but the conditions that prompted them have not. There’s good reason to think that the main limit to capital spending still is not scarce savings, but limited outlets for profitable investment, and that the key obstacle to faster growth is not technology or “structural” constraints, but the willingness of people to spend money. Bringing clean energy to scale will call forth new spending, both public and private, in abundance.
Of course, not everyone will benefit from the clean energy boom. The problem of stranded assets is real— any effective response to climate change will mean that much of the world’s coal and oil never comes out of the ground. But it’s not clear how far this problem extends beyond the fossil fuel sector. For manufacturers, even in the most carbon-intensive industries, only a small part of their value as enterprises comes from the capital equipment they own. More important is their role in coordinating production—a role that conventional economic models, myopically focused on coordination through markets, have largely ignored. organizing complex production processes, and maintaining trust and cooperation among the various participants in them, are difficult problems, solved not by markets but by the firm as an ongoing social organism. This coordination function will retain its value even as production itself is transformed.
UPDATE: Followup post on the World War II experience here.
In the paper I tried to give a fairly comprehensive overview of the evidence and arguments that the US economy is not in any meaningful sense at potential output or full employment. But of course it was just one small piece of a larger conversation. Here are a few things I’ve found interesting recently on the same set of issues. .
Perhaps the most important new academic contribution to this debate is this paper by Olivier Coibion, Yuriy Gorodnichenko, and Mauricio Ulate, on estimates of potential output, which came out too late for me to mention in the Roosevelt report. Their paper rigorously demonstrates that, despite their production-function veneer, the construction of potential output estimates ensures that any persistent change in growth rates will appear as a change in potential. It follows that there is “little value added in estimates of potential GDP relative to simple measures of statistical trends.” (Matthew Klein puts it more bluntly in an Alphaville post discussing the paper: “‘Potential’ output forecasts are actually worthless.”) The paper proposes an alternative measure of potential output, which they suggest can distinguish between transitory demand shocks and permanent shifts in the economy’s productive capacity. This alternative measure gives a very similar estimate for the output gap as simply looking at the pre-2008 forecasts or extrapolating from the pre-2008 trend. “Our estimates imply that U.S. output remains almost 10 percentage points below potential output, leaving ample room for policymakers to close the gap through demand-side policies if they so chose to.” Personally, I ‘m a little less convinced by their positive conclusions than by their negative ones. But this paper should definitely put to the rest the idea (as in last year’s notorious CEA-chair letter) that it is obviously wrong — absurd and unserious — that a sufficient stimulus could deliver several years of 4 percent real growth, until GDP returned to its pre-recession trend. It may or may not be true, but it isn’t crazy.
Many of the arguments in my paper were also made in this valuable EPI report by Josh Bivens, reviving the old idea of a “high pressure economy”. Like me, Bivens argues that slow productivity growth is largely attributable to low investment, which in turn is due to weak demand and slow wage growth, which blunts the incentive for business to invest in labor-saving technology. One important point that Bivens makes that I didn’t, is that much past variation in productivity growth has been transitory; forecasts of future productivity growth based on the past couple of years have consistently performed worse than forecasts based on longer previous periods. So historical evidence gives us no reason see the most recent productivity slowdown as permanent. My one quibble is that he only discusses faster productivity growth and higher inflation as possible outcomes of a demand-driven acceleration in wages. This ignores the third possible effect, redistribution from from profits to wages — in fact a rise in the labor share is impossible without a period of “overfull” employment.
Minneapolis Fed president Neel Kashkari wrote a long post last fall on “diagnosing and treating the slow recovery.” Perhaps the most interesting thing here is that he poses the question at all. There’s a widespread view that once you correct for demographics, the exceptional performance of the late 1990s, etc., there’s nothing particularly slow about this recovery — no problem to diagnose or treat.
Another more recent post by Kashkari focuses on the dangers of forcing the Fed to mechanically follow a Taylor rule for setting interest rates. By his estimate, this would have led to an additional 2.5 million unemployed people this year. It’s a good illustration of the dangers of taking the headline measures of economic performance too literally. I also like its frank acknowledgement that the Fed — like all real world forecasters — rejects rational expectations in the models it uses for policymaking.
Kashkari’s predecessor Narayan Kocherlakota — who seems to agree more with the arguments in my paper — has a couple short but useful posts on his personal blog. The first, from a year ago, is probably the best short summary of the economic debate here that I’ve seen. Perhaps the key analytic point is that following a period of depressed investment, the economy may reach full employment given the existing capital stock while it is still well short of potential. So a period of rapid wage growth would not necessarily mean that the limits of expansionary policy have ben reached, even if those wage gains were fully passed through to higher prices. His emphasis:
Because fiscal policy has been too tight, we have too little public capital. … At the same time, physical investment has been too low… Conditional on these state variables, we might well be close to full employment. … But, even though we’re close to full employment, there’s a lot of room for super-normal growth. Both capital and TFP are well below their [long run level]. The full-employment growth rate is going to be well above its long-run level for several years. We can’t conclude the economy is overheating just because it is growing quickly.
His second post focuses on the straightforward but often overlooked point that policy should take into account not just our best estimates but our uncertainty about them, and the relative risks of erring on each side. And if there is even a modest chance that more expansionary policy could permanently raise productivity, then the risks are much greater on the over-contractionary side.  In particular, if we are talking about fiscal stimulus, it’s not clear that there are any costs at all. “Crowding out” is normally understood to involve a rise in interest rates and a shift from private investment to public spending. In the current setting, there’s a strong case that higher interest rates at full employment would be a good thing (at least as long as we still rely on as the main tool of countercyclical policy). And it’s not obvious, to say the least, that the marginal dollar of private investment is more socially useful than many plausible forms of public spending.  Kashkari has a post making a similar argument in defense of his minority vote not to raise rates at the most recent FOMC meeting. (Incidentally, FOMC members blogging about their decisions is a trend to be encouraged.)
In a post from March which I missed at the time, Ryan Avent tries to square the circle of job-destroying automation and slow productivity growth. One half of the argument seems clearly right to me: Abundant labor and low wages discourage investment in productivity-raising technologies. As Avent notes, early British and even more American industrialization owe a lot to scarce labor and high wages. The second half of the argument is that labor is abundant today precisely because so much has been displaced by technology. His claim is that “robots taking the jobs” is consistent with low measured productivity growth if the people whose jobs are taken end up in a part of the economy with a much lower output per worker. I’m not sure if this works; this seems like the rare case in economics where an eloquent story would benefit from being re-presented with math.
Along somewhat similar lines, Simon Wren-Lewis points out that unemployment may fall because workers “price themselves into jobs” by accepting lower-wage (and presumably lower-productivity) jobs. But this doesn’t mean that the aggregate demand problem has been solved — instead, we’ve simply replaced open unemployment with what Joan Robinson called “disguised unemployment,” as some of people’s capacity for work continues to go to waste even while they are formally employed. “But there is a danger that central bankers would look at unemployment, … and conclude that we no longer have inadequate aggregate demand…. If demand deficiency is still a problem, this would be a huge and very costly mistake.”
Karl Smith at the Niskanen Center links this debate to the older one over the neutrality of money. Central bank interventions — and aggregate demand in general — are understood to be changes in the flow of money spending in the economy. But a lon-standing tradition in economic theory says that money should be neutral in the long run. As we are look at longer periods, changes in output and employment should depend more and more on real resources and technological capacities, and less and less on spending decisions — in the limit not at all. If you want to know why GDP fell in one quarter but rose in the next (this is something I always tell my undergraduates) you need to ask who chose to reduce their spending in the first period and who chose to increase it in the first. But if you want to know why we are materially richer than our grandparents, it would be silly to say it’s because we choose to spend more money. This is the reason why I’m a bit impatient with people who respond to the fact that, relative to the pre-2008 trend, output today has not recovered from the bottom of the recession, by saying “the trend doesn’t matter, deviations in output are always persistent.” This might be true but it’s a radical claim. It means you either take the real business cycle view that there’s no such thing as aggregate demand, even recessions are due to declines in the economy’s productive potential; or you must accept that in some substantial sense we really are richer than our grandparents because we spend more money. You can’t assert that GDP is not trend-stationary to argue against an output gap today unless you’re ready to accept these larger implications.
The invaluable Tom Walker has a fascinating post going back to even older debates, among 19th century anti-union and pro-union pamphleters, about whether there was a fixed quantity of labor to be performed and whether, in that case, machines were replacing human workers. The back and forth (more forth than back: there seem to be a lot more anti-labor voices in the archives) is fun to read, but what’s the payoff for todays’ debates?
The contemporary relevance of this excursion into the archives is that economic policy and economic thought walks on two legs. Conservative economists hypocritically but strategically embrace both the crowding out arguments for austerity and the projected lump-of-labor fallacy claims against pensions and shorter working time. They are for a “fixed amount” assumption when it suits their objectives and against it when it doesn’t. There is ideological method to their methodological madness. That consistency resolves itself into the “self-evidence” that nothing can be done.
That’s exactly right. When we ask why labor’s share has fallen so much over the past generation, we’re told it’s because of supply and demand — an increased supply of labor from China and elsewhere, and a decreased demand thanks to technology. But if it someone says that it might be a good idea then to limit the supply of labor (by lowering the retirement age, let’s say) and to discourage capital-intensive production, the response is “are you crazy? that will only make everyone poorer, including workers.” Somehow distribution is endogenous when it’s a question of shifts in favor of capital, but becomes exogenously fixed when it’s a question of reversing them.
A number of heterdox writers have identified the claim that productivity growth depends on demand as Verdoorn’s law (or the Kaldor-Verdoorn Law). For example, the Post Keynesian blogger Ramanan mentions it here and here. I admit I’m a bit dissatisfied with this “law”. It’s regularly asserted by heterodox people but you’ll scour our literature in vain looking for either a systematic account of how it is supposed to operate or quantitative evidence of how and how much (or whether) it does.
Adam Ozimek argues that the recent rise in employment should be seen as an argument for continued expansionary policy, not a shift away from it. After all, a few years ago many policymakers believed such a rise was impossible, since the decline in employment was supposed to be almost entirely structural.
Finally, Reihan Salam wants to enlist me for the socialist flank of a genuinely populist Trumpism. This is the flipside of criticism I’ve sometimes gotten for making this argument — doesn’t it just provide intellectual ammunition for the Bannon wing of the administration and its calls for vast infrastructure spending, which is also supposed to boost demand and generate much faster growth? Personally I think you need to make the arguments for what you think is true regardless of their political valence. But I might worry about this more if I believed there was even a slight chance that Trump might try to deliver for his working-class supporters.
 Kocherlakota talks about total factor productivity. I prefer to focus on labor productivity because it is based on directly observable quantities, whereas TFP depends on estimates not only of the capital stock but of various unobservable parameters. The logic of the argument is the same either way.
EDIT: My comments on the heterodox literature on the Kaldor-Verdoorn Law were too harsh. I do feel this set of ideas is underdeveloped, but there is more there than my original post implied. I will try to do a proper post on this work at some point.
We know that US GDP fell sharply in 2008-2009. We know that none of that decline has been made up by faster growth since the recession: GDP today is about 14 percent below the pre-2008 trend, a gap that shows no sign of closing. We also know that one-third of that shortfall is accounted for by slower productivity growth, and the remaining two-thirds by slower employment growth.
To put numbers on it: Over the past decade, US employment rose by a total of 6 percent, or about 0.5 percent per year. This is about half the rate of employment growth over the last ten years before the recession, and less a quarter the average rate for the postwar period as a whole. 2000-2010 was the first decade since the Depression in which US employment actually fell. Since the unemployment rate today is very close to that of ten years ago, this whole slowdown is accounted for by a decline in laborforce participation.
Employment growth, unlike productivity growth, was already slowing prior to the recession, and pre-recession forecasts predicted a further slowdown comparable to what actually occurred. This is consistent with a widely-held view that the slowdown in employment is the result of demographic and other structural factors, not of the recession or demand weakness in general. In the next couple posts, I want to take a critical look at this claim. How confident should we be that employment would be the same today in a counterfactual world where the 2008-2009 didn’t happen? How responsive might employment be to stronger demand going forward? And more broadly, how much do changes in laborforce participation seem to be explained by more or less exogenous factors like demographics, and how much by demand and labor-market conditions?
The rest of this post is about an approach to this question that did not produce the results I was hoping for. So I probably won’t include this material in whatever paper comes out of these posts. But as we feel our way into reality it’s important to note down the dead ends as well as the routes that seem promising. And even though this exercise didn’t help much in answering the big questions posed in the previous paragraph, it’s still interesting in its own right.
Can the fall in laborforce participation be explained as a direct, predictable effect of the rise in unemployment during the recession? It seems like maybe it can. The starting point is the observation that unemployed workers are much more likely to drop out of the laborforce than people with jobs are. You can see this clearly in the BLS tables on employment transitions. As the figure below shows, about 3 percent of employed people exit the laborforce each month, a fraction that has been remarkably stable since the data begins in 1990. Meanwhile, about 20 percent of unemployed people drop out of the laborforce each month.
On the face of it, this 17-point difference suggests an important role for the unemployment rate in changes in labor force participation. All else equal, each year-point of additional unemployment should reduce the labor-force participation rate by two points. (0.17 x 12 = 2.) So you would think that much of the recent fall in laborforce participation could be explained simply by the rise in unemployment during the recession.
When I thought of this it seemed very logical. It would be easy to do a counterfactual exercise, I thought, showing how laborforce participation would have evolved simply based on the historical transition rates between employment, unemployment and out of the laborforce, and the actual evolution of employment and unemployment. If you could show that something like the actual fall in laborforce participation was a predictable result of the rise in unemployment during the recession, that would support the idea that demand rather than “structural” factors are at work. And even if it wasn’t that strong positive evidence, it would suggest skepticism about similar counterfactual exercises using historical participation rates by age and so on.
I mean, it makes sense, right? Unemployed people are much more likely to leave the workforce than employed people, so a rise in unemployment should naturally lead to a decline in laborforce participation. But as the figure below shows, the numbers don’t work.
What I did was start with the populations of employe, unemployed and not-in-the-laborforce people at the end of the recession in December 2009. Then I created a counterfactual scenario for the remaining period using the actual transition rates between employment and unemployment but the pre-recession average rates for transitions between not in the workforce and unemployment and employment. In other words, just knowing the average rates that people move between employment, unemployment and out of the workforce, and the actual shifts between employment and unemployment, what path would you have predicted for laborforce participation over 2010-2016?
The heavy gray line shows the historical fraction of the population aged 16 and over who are not in the laborforce. The black line shows the results of the counterfactual exercise. Not very close.
There turn out to be two reasons why the counterfactual exercise gives such a poor fit. Both are interesting and neither was obvious before doing the exercise. The first reason is that there are surprisingly large flows from out of the labor force back into it. Per the BLS, about 7 percent of people who report being out of the labor force in a given month are either employed or unemployed (i.e. actively seeking work) the following month. This implies that the typical duration of being out of the workforce is less than a year — though of course this is a mix of people who leave the workforce for just a month or two and people who leave for good. For present purposes, the important thing is that exogenous changes to the employment-population ratio decline quickly, with a half-life of only about a year. So while the historical data suggests that a rise in unemployment like we saw in 2008-2009 should have been associated with a large rise in the share of the population not in the laborforce, it also suggests that this effect should have been transitory — a couple years after unemployment rates returned to normal, participation rates should have as well. This is not what we’ve seen.
The large gross movements in and out of the laborforce mean that sustained lower participation rates can’t be straightforwardly understood as the “echo” of high unemployment in the past. But they do also tend to undermine the structural story — if the typical stint outside the laborforce lasts less than a year it can hardly be due to something immutable.
The second reason why the counterfactual doesn’t fit the data was even more surprising, at least to me. I constructed my series using the historical average transition rates into and out of the workforce. But transition rates during the recession and early recovery departed from the historical average in an important way: unemployed workers were significantly less likely to exit the workforce. This turns out to be the normal pattern, at least over the previous two business cycles — if you look back to that first figure, you can see dips in the transition rate from unemployed to out of the workforce in the early 1990s and early 2000s downturns as well. The relationship is clearer in the next figure, a scatter of the unemployment rate and the share of unemployed workers leaving the workforce each month.
As you can see, there is a strong negative relationship — when unemployment was around 4 percent in 1999-2000 and again in 2006-2007, about a quarter of the unemployed exited the laborforce each month. But at the peak of the past recession when unemployment reached 10 percent, only 18 percent of the unemployed left the laborforce each month. That might not seem like a huge difference, but it’s enough to produce quite different dynamics. It’s also a bit surprising, since you would think that people would be more likely to give up searching for work when unemployment is high than when when it is low. The obvious explanation would be that the people who are out of work when the unemployment rate is low are not simply a smaller set of the same people who are out of work when the rate is high, but are different in some way. The same factors that keep them at the back of the hiring queue may make also be likely to push them out of the laborforce altogether. Extended unemployment insurance might also play a role.
It would be possible to explore this further using CPS data, which is the source for the BLS tables I’m working with. No doubt there are papers out there describing the different characteristics of the unemployed in periods of high versus low unemployment. (Not being a labor economist, I don’t know this literature.) But I am going to leave it here.
Summary: The fact that unemployed people are much more likely to leave the laborforce than employed people are, suggests that some part of the fall in laborforce particiaption since 2008 might be explained by the lingering effects of high unemployment in the recession and early recovery. But this story turns out not tow work, for two reasons. First, the rapid turnover of the not in the laborforce population means that this direct effect of high unemployment on participation is fairly shortlived. Second, the rate at which unemployed people exit the laborforce turns out to be lower when unemployment is high. Together, these two factors produce the results shown in the second figure — the fall in participation you would predict based simply on high unemployment is steeper but shorter-lived than what actually occurred. The first factor — the large flows in and out of the laborforce — while it vitiates the simple story I proposed here, is consistent with a broader focus on demand rather than demographics as an explanation for slow employment growth. If people are frequently moving in and out of the laborforce, it’s likely that their decisions are influenced by their employment prospects, and it means they’re not determined by fixed characteristics like age. The second factor — that unemployed people were less likely to give up looking for jobs during 2009-2011, as in previous periods of high unemployment — is, to me, more surprising, and harder to fit into a demand-side story.
Empirically-oriented macroeconomists have recognized since the early 20th century that output, employment and productivity move together over the business cycle. The fact that productivity falls during recessions means that employment varies less over the cycle than output does. This behavior is quite stable over time, giving rise to Okun’s law. In the US, Okun’s law says that the unemployment rate will rise by one point in each 2.5 point shortfall of GDP growth over trend — a ratio that doesn’t seem to have declined much since Arthur Okun first described it in the mid-1960s. 
It’s not obvious that potential should show this procyclical behavior. As I noted in the previous post, a naive prediction from a production function framework would be that a negative demand shock should reduce employment more than output, since business can lay off workers immediately but can’t reduce their capital stock right away. In other words, productivity should rise in recessions, since the labor of each still-employed worker is being combined with more capital.
There are various explanations for why labor productivity behaves procyclically instead. The most common focus on the transition costs of changing employment. Since hiring and firing is costly for businesses, they don’t adjust their laborforce to every change in demand. So when sales fall in recessions, they will keep extra workers on payroll — paying them now is cheaper than hiring them back later. Similarly, when sales rise businesses will initially try to get more work out of their existing employees. This shows up as rising labor productivity, and as the repeated phenomenon of “jobless recoveries.”
Understood in these terms, the positive relationship between output, employment and productivity should be a strictly short-term phenomenon. If a change in demand (or in other constraints on output) is sustained, we’d expect labor to fully adjust to the new level of production sooner or later. So over horizons of more than a year or two, we’d expect output and employment to change in proportion. If there are other limits on production (such as non-produced inputs like land) we’d expect output and labor productivity to move inversely, with faster productivity growth associated with slower employment growth or vice versa. (This is the logic of “robots taking the jobs.”) A short-term positive, medium term negative, long-term flat or negative relationship between employment growth and productivity growth is one of the main predictions that comes out of a production function. But it doesn’t require one. You can get there lots of other ways too.
And in fact, it is what we see.
The figure shows the simple correlation of employment growth and productivity growth over various periods, from one quarter out to 50 quarters. (This is based on postwar US data.) As you can see, over periods of a year or less, the correlation is (weakly) positive. Six-month periods in which employment growth was unusually weak are somewhat more likely to have seen weak productivity growth as well. This is the cyclical effect presumably due to transition costs — employers don’t always hire or fire in response to short-run changes in demand, allowing productivity to vary instead. But if sales remain high or low for an extended period, employers will eventually bring their laborforce into line, eliminating this relationship. And over longer periods, autonomous variation in productivity and labor supply are more important. Both of these tend to produce a negative relationship between employment and productivity. And that’s exactly what we see — a ten-year period in which productivity grew unusually quickly is likely to be one in which employment grew slowly. (Admittedly postwar US data doesn’t give you that many ten-year periods to look at.)
Another way of doing this is to plot an “Okun coefficient” for each horizon. Here we are looking at the relationship between changes in employment and output. Okun’s law is usually expressed in terms of the relatiojship between unemployment and output, but here we will look at it in terms of employment instead. We write
(1) %ΔE = a (g – c)
where %ΔE is the percentage change in employment, g is the percentage growth in GDP, c is a constant (the long-run average rate of productivity growth) and a is the Okun coefficient. The value of a says how much additional growth in employment we’d expect from a one percentage-point increase in GDP growth over the given period. When the equation is estimated in terms of unemployment and the period is one, year, a is generally on the order of 0.4 or so, meaning that to reduce unemployment by one point over a year normally requires GDP growth around 2.5 points above trend. We’d expect the coefficient for employment to be greater, but over short periods at least it should still be less than one.
Here is what we see if the estimate the equation for changes in output and employment for various periods, again ranging from one quarter up to ten years. (Again, postwar US data. The circles are the point estimates of the coefficients; the dotted lines are two standard errors above and below, corresponding to a standard 95% confidence interval.)
What’s this show? If we estimate Equation (1) looking at changes over one quarter, we find that one percentage point of additional GDP growth is associated with just half a point of additional employment growth. But if we estimate the same equation looking at changes over two years, we find that one point of additional GDP growth is associated with 0.75 points of additional employment growth.
The fact that the coefficient is smallest for the shorter periods is, again, consistent witht he conventional understanding of Okun’s law. Because hiring and firing is costly, employers don’t fully adjust staffing unless a change in sales is sustained for a while. If you were thinking in terms of a production function, the peak around 2 years represents a “medium-term” position where labor has adjusted to a change in demand but the capital stock has not.
While it’s not really relevant for current purposes, it’s interesting that at every horizon the coefficient is significantly below zero. What this tells us is that there is no actual time interval corresponding to the “long run” of the model– a period long enough for labor and the capital stock to be fully adjusted but short enough that technology is fixed. Over this hypothetical long run, the coefficient would be one. One way to think about the fact that the estimated coefficients are always smaller, is that any period long enough for labor to adjust, is already long enough to see noticeable autonomous changes in productivity. 
But what we’re interested in right now is not this normal pattern. We’re interested in how dramatically the post-2008 period has departed from it. The past eight years have seen close to the slowest employment growth of the postwar period and close to the slowest productivity growth. It is normal for employment and productivity to move together for a couple quarters or a year, but very unusual for this joint movement to be sustained over nearly a decade. In the postwar US, at least, periods of slow employment growth are much more often periods of rapid productivity growth, and conversely. Here’s a regression similar to the Okun one, but this time relating productivity growth to employment growth, and using only data through 2008.
While the significance lines can’t be taken literally given that these are overlapping periods, the figure makes clear that between 1947 and 2008, there were very few sustained periods in which both employment and productivity growth made large departures from trend in the same direction.
Put it another way: The past decade has seen exceptionally slow growth in employment — about 5 percent over the full period. If you looked at the US postwar data, you would predict with a fair degree of confidence that a period of such slow employment growth would see above-average productivity growth. But in fact, the past decade has also seen very low productivity growth. The relation between the two variables has been much closer to what we would predict by extrapolating their relationships over periods of a year. In that sense, the current slowdown resembles an extended recession more than it does previous periods of slower growth.
As I suggested in an earlier post, I think this is a bigger analytic problem than it might seem at first glance.
In the conventional story, productivity is supposed to be driven by technology, so a slowdown in productivity growth reflects a decline in innovation and so on. Employment is driven by demographics, so slower employment growth reflects aging and small families. Both of these developments are negative shifts in aggregate supply. So they should be inflationary — if the economy’s productive potential declines then the same growth in demand will instead lead to higher prices. To maintain stable prices in the face of these two negative supply shocks, a central bank would have to raise interest rates in order to reduce aggregate spending to the new, lower level of potential output. Is this what we have seen? No, of course not. We have seen declining inflation even as interest rates are at historically low levels. So even if you explain slower productivity growth by technology and explain slower employment growth by demographics, you still need to postulate some large additional negative shift in demand. This is DeLong and Summers’ “elementary signal identification point.”
Given that we are postulating a large, sustained fall in demand in any case, it would be more parsimonious if the demand shortfall also explained the slowdown in employment and productivity growth. I think there are good reasons to believe this is the case. Those will be the subject of the remaining posts in this series.
In the meantime, let’s pull together the historical evidence on output, employment and productivity growth in one last figure. Here, the horizontal axis is the ten-year percentage change in employment, while the vertical axis is the ten-year percentage change in productivity. The years are final year of the comparison. (In order to include the most recent data, we are comparing first quarters to first quarters.) The color of the text shows average inflation over the ten year period, with yellow highest and blue lowest. The diagonal line corresponds to the average real growth rate of GDP over the full period.
What we’re looking at here is the percentage change in productivity, employment and prices over every ten-year period from 1947-1957 through 2006-2016. So for instance, growth between 1990 and 2000 is represented by the point labeled “2000.” During this decade, total employment rose by about 20 percent while productivity rose by a total of 15 percent, implying an annual real growth of 3.3 percent, very close to the long-run average.
One natural way to think about this is that yellow points below and to the right of the line suggest negative supply shocks: If the productive capacity of the economy declines for some reason, output growth will slow, and prices will rise as private actors — abetted by a slow-to-react central bank — attempt to increase spending at the usual rate. Similarly, blue points above the line suggest positive supply shocks. Yellow points above the line suggest positive demand shocks — an increase in spending can increase output growth above trend, at least for a while, but will pull up prices as well. And blue points below the line suggest negative demand shocks. This, again, is Delong and Summers’ “elementary signal identification point.”
We immediately see what an outlier the recent period is. Both employment and productivity growth over the past ten years have been drastically slower than over the preceding decade — about 5 percent each, down from about 20 percent. 2000-2010 and 2001-2011 were the only ten-year periods in postwar US history when total employment actually declined. The abruptness of the deceleration on both dimensions is a challenge for views that slower growth is the result of deep structural forces. And the combination of the slowdown in output growth with falling prices — especially given ultra-low interest rats — strongly suggests that we’ve seen a negative shift in desired spending (demand) rather than in the economy’s productive capacities (supply).
Another way of looking at this is as three different regimes. In the middle is what we might call “the main sequence” — here there is steady growth in demand, met by varying mixes of employment and productivity growth. On the upper right is what gets called a “high-pressure economy,” in which low unemployment and strong demand draw more people into employment and facilitates the reallocation of labor and other resources toward more productive activity, but put upward pressure on prices. On the lower left is stagnation, where weak demand discourages participation in the labor force and reduces productivity growth by holding back investment, new business formation and by leaving a larger number of those with jobs underemployed, and persistent slack leads to downward pressure on prices (though so far not outright deflation). In other words, macroeconomically speaking the past decade has been a sort of anti-1960s.
 There are actually two versions of Okun’s law, one relating the change in the unemployment rate to GDP growth and one relating the level of unemployment to the deviation of GDP from potential. The two forms will be equivalent if potential grows at a constant rate.
 The assumption that variables can be partitioned into “fast” and “slow” ones, so that we can calculate equilibrium values of the former with the latter treated as exogenous, is a very widespread feature of economic modeling, heterodox as much as mainstream. I think it needs to be looked at more critically. One alternative is dynamic models where we focus on the system’s evolution over time rather than equilibrium conditions. This is, I suppose, the kind of “theory” implied by VAR-type forecasting models, but it’s rare to see it developed explicitly. There are people who talk about a system dynamics approach, which seems promising, but I don’t know much about them.
Labor dynamism and demand. My colleagues Mike Konczal and Marshall Steinbaum have an important new paper out on the decline in new business starts and in labor mobility. They argue that the data don’t support a story where declining labor-market dynamism is the result of supply-side factors like occupational licensing. It looks much more like the result of chronically weak demand for labor, which for whatever reason is not picked up by the conventional unemployment rate. This is obviously relevant to the potential output question I’m interested in — a slowdown in the rate at which workers move to new firms is a natural channel by which weak demand could reduce labor productivity. It’s also a very interesting story in its own right.
The decline of entrepreneurship and “business dynamism” has become an accepted fact … Explanations for these trends … broadly fall on the supply side: that increasingly onerous occupational licensing impedes entry into certain protected professions and restricts licensed workers to staying where they are; that the high cost of housing thanks to restrictions on development hampers individuals from moving… But we find that the data reject these supply-side explanations: If there were increased restrictions on changing jobs or starting a business, we would expect those few workers and entrepreneurs who do manage to move to enjoy increased wage gains relative to periods with higher worker flows, and we would expect aggressive hiring by employers with vacancies. … Instead, we see the opposite…
We propose a different organizing principle: Declining business dynamism and labor mobility are features of a slackening labor market … workers lucky enough to have formal employment stay where they are rather than striking out as entrepreneurs …
John Kenneth who? Real World Economics Review polled its subscribers on the most important economics books of the past 100 years. Here’s the top ten. Personally I suspect Debt will have more staying power than Capital in the 21st Century, and I think Minsky’s book John Maynard Keynes is a better statement of his vision than Stabilizing an Unstable Economy, a lot of which is focused on banking-sector developments of the 1970s and 1980s that aren’t of much interest today. But overall it’s a pretty good list. The only one I haven’t read is The Affluent Society. I wonder if anyone under the age of 50 picked that one?
Deflating the elephant. Here is a nice catch from David Rosnick. Brank Milanovic has a well-known graph of changes in global income distribution over 1988-2008. What we see is that, while within most countries there has been increased polarization, at the global level the picture is more complicated. Yes, the top of the distribution has gone way up, and the very bottom has gone down. But the big fall has been in the upper-middle of the distribution — between the 80th and 99th percentiles — while most of the lower part has has risen, with the biggest gains coming around the 50th percentile. The decline near the high end is presumably working-class people in rich countries and most people in the former Soviet block —who were still near the top of the global distribution in 1988. A big part of the rise in the lower half is China. A natural question is, how much? — what would the distribution look like without China? Milanovic had suggested that the overall picture is still basically the same. But as Rosnick shows, this isn’t true — if you exclude China, the gains in the lower half are much smaller, and incomes over nearly half the distribution are lower in 2008 than 20 years before. It’s hard to see this as anything but a profoundly negative verdict on the Washington Consensus that has ruled the world over the past generation.
By the way, you cannot interpret this — as I at first wrongly did — as meaning that 40 percent of the world’s people have lower incomes than in 1988. It’s less than that. Faster population growth in poor countries would tend to shift the distribution downward even if every individual’s income was rising.
Does nuclear math add up? Over at Crooked Timber, there’s been an interesting comments-thread debate between Will Boisvert (known around here for his vigorousdefense of nuclear power) and various nuke antis and skeptics. I’m the farthest thing from an expert, I can’t claim to be any kind of arbiter. But personally my sympathies are with Will. One important thing he brings out, which I hadn’t thought about enough until now, is the difference between electricity and most other commodities. Part of the problem is the very large share of fixed costs — as the Crotty-Minsky-Perelman strain of Keynesians have emphasized, capitalism does badly with long lived capital assets. A more distinctive problem is the time dimension — electricity produced at one time is not a good substitute for electricity produced at a different time, even just an hour before or after. Electricity cannot be stored economically at a meaningful scale, nor — given that almost everything in modern civilization uses it — can its consumption be easily shifted in time. This means that straightforward comparisons of cost per kilowatt — hard enough to produce, given the predominance of fixed costs — can be misleading. Regardless of costs, intermittent sources — like wind or solar — have to be balanced by sources that can be turned on anytime — which in the absence of nuclear, means fossil fuels.
Do you believe, as I do, that climate change is the great challenge facing humanity in the next generation? Then this is a very strong argument for nuclear power. Whatever its downsides, they are not as bad as boiling the oceans. Still, it’s not a decisive argument. The big other questions are the costs of power storage and of more extensive transmission networks — since when the sun isn’t shining and the wind isn’t blowing in one place, they probably are somewhere else. (I agree with Will that using the price mechanism to force electricity usage to conform to supply from renewables is definitely the wrong answer.) The CT debate doesn’t answer those questions. But it’s still an example of how informative blog debate can be when there are people both sides with real expertise who are prepared to engage seriously with each other.
On other blogs, other wonders. Here is a fascinating post by Laura Tanenbaum on the end of sex-segregated job ads and the false dichotomy between “elite” and “grassroots” feminism.
This very interesting article by Jose Azar on the extent and economic significance of common ownership of corporate shares deserves a post of its own.
Here’s a nice little think piece from Bloomberg wondering what, if anything, is meant by “the natural rate” of interest. I’m glad to see some skepticism about this concept in the larger conversation. In my mind, the “natural rate” is one of the key patches covering over the disconnect between economic theory and the observable economy.
Bhenn Bhiorach has a funny post on the lengths people will go to to claim that low inflation is really high inflation.
Last week, the Washington Post ran an article by Jim Tankersley on what would happen if Trump got his way and the US imposed steep tariffs on goods from Mexico and China. I ended up as the objectively pro-Trump voice in the piece. The core of it was an estimates from Mark Zandi at Moody’s that a 45% tariff on goods from China and a 35% tariff on goods from Mexico (I don’t know where these exact numbers came from) would have an effect on the US comparable to the Great Recession, with output and employment both falling by about 5 percent relative to the baseline. About half this 5 percent fall in GDP would be due to retaliatory tariffs from China and Mexcio, and about half would come from the US tariffs themselves. As I told the Post, I think this is nuts.
Let me explain why I think that, and what a more realistic estimate would look like. But first, I should say that Tankersley did exactly what one ought to do with this story — asked the right question and went to a respected expert to help him answer it. The problem is with what that expert said, not the reporting. I should also say that my criticisms were presented clearly and accurately in the piece. But of course, there’s only so much you can say in even a generous quote in a newspaper article. Hence this post.
I haven’t seen the Moody’s analysis (it’s proprietary). All I know is what’s in the article, and the general explanation that Tankersley gave me in the interview. But from what I can tell, Zandi and his team want to tell a story like this. When the US imposes a tariff, it boosts the price of imported goods but leads to no substitution away from them. Instead, higher import prices just mean lower real incomes in the US. Then, when China and Mexico retaliate, that does lead to substitution away from US goods, and the lost exports reduce US real incomes further. But only under the most extreme assumptions can you get Zandi’s numbers out of this story.
While this kind of forecasting might seem mysterious, it mostly comes down to picking values for a few parameters — that is, making numerical guesses about relationships between the variables of interest. In this case, we have to answer three questions. The first question is, how much of the tariff is paid by the purchasers of imported goods, as opposed to the producers? The second question is, how do purchasers respond to higher prices — by substituting to domestic goods, by substituting to imports from other countries, or by simply paying the higher prices? Substitution to domestic goods is expansionary (boosts demand here), substitution to imports from elsewhere is neutral, and paying the higher prices is contractionary, since it reduces the income available for domestic spending. And the third question is, how much does a given shift in demand ultimately move GDP? The answer to the first question gives us the passthrough parameter. The answer to the second question gives us two price elasticities — a bilateral elasticity for imports from that one country, and an overall elasticity for total imports. The answer to the third question gives us the multiplier. Combine these and you have the change in GDP resulting from the tariff. Of course if you think the initial tariffs will provoke retaliatory tariffs from the other countries, you have to do the same exercise for those, with perhaps different parameters.
Let’s walk through this. Suppose the US — or any country — increases taxes on imports: What can happen? The first question is, how is the price of the imported good set — by costs in the producing country, or by market conditions in the destination? If conditions in the destination country affect price — if the producer is unable or unwilling to raise prices by the full amount of the tariff — then they will have to accept lower revenue per unit sold. This is referred to as pricing to market or incomplete passthrough, and empirical studies suggest it is quite important in import prices, especially in the US. Incomplete passthrough may result in changing profit margins for producers, or they may be able to adjust their own costs — wages especially — in turn. Where trade is a large fraction of GDP, some of the tax may eventually be translated into a lower price level in the exporting country.
Under floating exchange rates, the tariff may also lead a depreciation of the exporting country currency relative to the currency of the country imposing the tariff. This is especially likely where trade between the two countries is a large share of total trade for one or both of them. In this case, a tariff is more likely to cause a depreciation of the Mexican peso than of the Chinese renminbi, since the US accounts for a higher fraction of Mexico’s exports than of China’s, and the renminbi is actively managed by China’s central bank.
Taking all these effects together, passthrough for US imports is probably less than 0.5. In other words, the majority of a tariff’s impact will probably be on dollar revenue for producers, rather than dollar costs for consumers. So a 10 percent tariff increases costs of imported goods by something less than 5 percent and reduces the revenue per unit of producers by something more than 5 percent.
The fraction of the tax that is not absorbed by lower exporter profit margins, lower wages in the export industry or a lower price level in the exporting country, or by exchange rate changes, will be reflected in higher prices in the importing country. The majority of trade goods for the US (as for most countries) are intermediate and capital goods, and even imported consumption goods are almost never purchased directly by the final consumer. So on the importing side, too, there will be firms making a choice between accepting lower profit margins, reducing wages and other domestic costs, or raising prices. Depending on exactly where we are measuring import prices, this might further reduce passthrough.
Let’s ignore this last complication and assume that a tax that is not absorbed on the exporting-country side is fully passed on to final price of imported goods. Purchasers of imported goods now respond to the higher price either by substituting to domestic goods, or substituting to imported goods from some third country not subject to the tax, or continuing to purchase the imports at the higher price. To the extent they substitute to domestic goods, that will boost demand here; to the extent they substitute to third-country goods, the tax will have no effect here.
These rates of substitution are described by the price elasticity of imports, computed as the ratio of the percentage change in the price, to the resulting percentage change in the quantity imported. So for instance if we thought that a 1 percent increase in the price of imported goods leads to a 2 percent fall in the quantity purchased, we would say the price elasticity is 2. There are two elasticities we have to think about — the bilateral elasticity and the overall elasticity. For example, we might think that the bilateral elasticity for US imports from China was 3 while the overall price elasticity for was 1. In that case, a 1 percent increase in the price of Chinese imports would lead to a 3 percent fall in US imports from China but only one-third of that would be through lower total US imports; the rest would be through higher imports from third countries.
To the extent the higher priced imported goods are purchased, this may result in a higher price of domestic goods for which the imports are an input or a substitute; to the extent this happens, the tax will raise domestic inflation but leave real income unchanged. For the US, import prices have a relatively small effect on overall inflation, so we’ll ignore this effect here. If we included it, we would end up with a smaller effect.
To the extent that the increase in import prices neither leads to any substitution away from the imported goods, nor to any price increase in domestic goods, it will reduce real incomes in the importing country, and leave incomes in the exporting country unchanged. Conversely, to the extent that the tariff is absorbed by lower wages or profit margins in the exporting country, or leads to substitution away from that country’s goods, it reduces incomes in the exporting country, but not in the importing country. And of course, to the extent that there is no substitution away from the taxed goods, government revenue will increase. Zandi does not appear to have explicitly modeled this last effect, but it is important in thinking about the results — a point I’ll return to.
Whether the increase in import prices increases domestic incomes (by leading to substitution to domestic goods) or reduces them, the initial effect will be compounded as the change in income leads to changes in other spending flows. If, let’s say, an increase in the price of Chinese consumer goods forces Americans to cut back purchases of American-made goods, then the workers and business owners in the affected industries will find themselves with less income, which will cause them to reduce their spending in turn. This is the familiar multiplier. The direct effect may be compounded or mitigated by financial effects — the multiplier will be larger if you think (as Zandi apparently does) that a fall in income will be accompanied by a fall in asset prices with a further negative effect on credit and consumption, and smaller if you think that a trade-induced change in income will be offset by a change in monetary (or fiscal) policy. In the case where central bank’s interest rate policy is always able to hold output at potential, the multiplier will be zero — shocks to demand will have no effect on output. This extreme case looked more reasonable a decade ago than it does today. In conditions where the Fed can’t or won’t offset demand impacts, estimates of the US multiplier range as high as 2.5; a respectable middle-of-the-road estimate would be 1.5.
Let’s try this with actual numbers.
Start with passthrough. The overwhelming consensus in the empirical literature is that less than half of even persistent changes in exchange rates are passed through to US import prices. This recent survey from the New York Fed, for instance, reports a passthrough of about 0.3:
following a 10 percent depreciation of the dollar, U.S. import prices increase about 1 percentage point in the contemporaneous quarter and an additional 2 percentage points over the next year, with little if any subsequent increases.
The factors that lead to incomplete passthrough of exchange rate movements — such as the size of the US market, and the importance exporters of maintaining market share — generally apply to a tariff as well, so it’s reasonable to think passthrough would be similar. So a 45% tariff on Chinese goods would probably raise prices to American purchasers by only about 15%, with the remainder absorbed by profits and/or wages at Chinese exporters.
Next we need to ask about the effect of that price on American purchases. There is a large literature estimating trade price elasticities; a sample is shown in the table below. As you can see, almost all the import price elasticities are between 0.2 and 1.0. (Price elasticities seem to be greater for US exports than for imports; they also seem to be higher for most other countries than for the US.) The median estimates is around 0.5 for overall US imports. Country-specific estimates are harder to find but I’ve seen values around 1.0 for US imports from both China and Mexico. Using those estimates, we would expect a 15% increase in the price of Chinese imports to lead to a 15% fall in imports from China, with about half of the substitution going to US goods and half going to imports from other countries. Similarly, a 10% increase in the price of goods from Mexico (a 35% tariff times passthrough of 0.3) would lead to a 10% fall in imports from Mexico, with half of that being a switch to US goods and half to imports from elsewhere.
Finally, we ask how the combination of substitution away from imports from Mexico and China, and the rise in price of the remaining imports, would affect US output. US imports from China are about 2.7 percent of US GDP, and imports from Mexico are about 1.7 percent of GDP. So with the parameters above, substitution to US goods raises GDP by 7.5% x 2.7% (China) plus 5% x 1.7% (Mexico), or 0.29% of GDP. Meanwhile the higher prices of the remaining imports from China and Mexico reduce US incomes by 0.22 percent, for a net impact of a trivial one twentieth of one percent of GDP. Apply a standard multiplier of 1.5, and the tariffs boost GDP by 0.08 percent.
You could certainly get a larger number than this, for instance if you thought that passthrough of a tariff would be substantially greater than passthrough of exchange rate changes. And making US import demand just a bit less price-elastic is enough to turn the small positive impact into a small negative one. But it would be very hard to get an impact of even one percent of GDP in either direction. And it would be almost impossible to get a negative impact of the kind that Zandi describes. If you assume both that the tariffs are fully passed through to final purchasers, and that US import demand is completely insensitive to price then with a multiplier of 1.5, you get a 2.7 percent reduction in US GDP. Since this is close to Zandi’s number, this may be what he did. But again, these are extreme assumptions, with no basis in the empirical literature. That doesn’t mean you can’t use them, but you need to justify them; just saying the magic word “proprietary” is not enough. (Imagine all the trouble Jerry Friedman could have saved himself with that trick!)
And the very low price elasticity you need for this result has some funny implications. For instance, it implies that when China intervenes to weaken their currency, they are just impoverishing themselves, since — if demand is really price-inelastic — they are now sending us the same amount of goods and getting fewer dollars for each one. I doubt Zandi would endorse this view, but it’s a logical corollary of the ultra-low elasticity he needs to get a big cost to the US from the initial tariff. Note also that the low-elasticity assumption means that the tariff creates no costs for China or Mexico: their exporters pass the increased tariff on completely to US consumers, and lose no sales as a result. It’s not clear why they would “retaliate” for this.
Let’s assume, though, that China and Mexico do impose tariffs on US goods. US exports to China and Mexico equal 0.7 and 1.3 percent of US GDP respectively. Passthrough is probably higher for US exports — let’s say 0.6 rather than 0.3. Price elasticity is also probably higher — we’ll say 1.5 for both bilateral elasticities and for overall export elasticity. (In the absence of exchange-rate changes, there’s no reason to think that a fall in exports to China and Mexico will lead to a rise in exports to third countries.) And again, we’ll use a multiplier of 1.5. This yields a fall in US GDP from the countertariffs of just a hair under 1 percent. Combine that with the small demand boost from the tariff, and we get an overall impact of -0.9 percent of GDP.
I admit, this is a somewhat larger hit than I expected before I worked through this exercise. But it’s still much smaller than Zandi’s number.
My preferred back-of-the-envelope for the combined impact of the tariffs and countertariffs would be a reduction in US GDP of 0.9 percent, but I’m not wedded to this exact number. I think reasonable parameters could get you an impact on US GDP anywhere from positive 1 percent to, at the worst, negative 2 percent or so. But it’s very hard to get Zandi’s negative 5 percent. You need an extremely high passthrough for both import and export prices, plus extremely price-inelastic US import demand and extremely price-elastic demand for US exports — all three parameters well outside the range in the empirical literature. At one point a few years ago, I collected about 20 empirical estimates of US trade elasticities, and none of them had a price elasticity for US exports greater than 1.5. But even with 100% passthrough, and a generous multiplier of 2.0, you need an export price elasticity of 4 or so to get US GDP to fall by 5 points.
Still, while Zandi’s 5 percent hit to GDP seems beyond the realm of the plausible, one could perhaps defend a still-substantial 2 percent. Let’s think for a moment, though, about what this would mean.
First of all, it’s worth noting — as I didn’t, unfortunately, to the Post reporter — that tariff increases are, after all, tax increases. Whatever its effect on trade flows, a big increase in taxes will be contractionary. This is Keynes 101. Pick any activity accounting for 5 percent of GDP and slap a 40 percent tax on it, and it’s a safe bet that aggregate income will be lower as a result. The logic of the exercise would have been clearer if the tariff revenue were offset by a cut in some other tax, or increase in government spending. (Maybe this is what Trump means when he says Mexico will pay for the wall?) Then it would be clearer how much of the predicted impact comes from the tariff specifically, as opposed to the shift toward austerity that any such a big tax increase implies. The point is, even if you decide that a 2 percent fall in US GDP is the best estimate of the tariff’s impact, it wouldn’t follow that tariffs as such are a bad idea. It could be that a big tax increase is.
Second, let’s step back for a moment. While Mexico and China are two of our largest trade partners, they still account for less than a quarter of total US trade. Given passthrough of 0.3, the 45/35 percent tariff on Chinese/Mexican goods would raise overall US import prices by about 3 percent. Even with 100 percent passthrough, the tariffs would raise overall import prices by just 10 percent. The retaliatory tariffs would raise US export prices by about half this — 5 percent with full passthrough. (The difference is because these two countries account for a smaller share of US exports than of US imports). Now, let’s look at the movements of the dollar in recent years.
Since 2014, the dollar has risen 15 percent. That’s a 15 percent increase in the price of US goods in all our export markets — three times the impact of the hypothetical Mexican and Chinese tariffs. But before that, from 2002 to 2008, the dollar fell by over 20 percent. That raised the price of US imports by twice as much as the hypothetical Trump tariff. And so on on back to the 1970s. If you believe Zandi’s numbers, then the rise in the dollar over the past two years should already have triggered a severe recession. Of course it has not. It would be foolish to deny that movements of the dollar have had some effect on US output and employment. But no one, I think, would claim impacts on anything like this scale. Still, one thing is for sure: If you believe anything like Zandi’s numbers on the macro impacts of trade price changes, then it’s insane to allow exchange rates to be set by private speculators.
So if Zandi is wrong about the macro impact of tariffs, does that mean Trump is right? No. First of all, while I don’t think there’s any way to defend Zandi’s claim of a very large negative impact on GDP of a tariff (or of a more respectable, but economically equivalent, depreciation of the dollar), it’s almost as hard to defend a large positive impact. Despite all the shouting, the relative price of Chinese goods is just not a very big factor for aggregate demand in the US. If the goal is stronger demand and higher wages here, there are various things we can do. A more favorable trade balance with China (or Mexico, or anywhere else) is nowhere near the top of that list. Second, the costs of the tariff would be substantial for the rest of the world. It’s important not to lose sight of the fact that China, over the past generation, has seen perhaps the largest rise in living standards in human history. We can debate how critical exports to the US were in this process, but certainly the benefits to China of exports to the US were vastly greater than whatever costs they created here.
But the fact that an idea is wrong, doesn’t mean that we can ignore evidence and logic in refuting it. Trumpism is bad enough on the merits. There’s no need to exaggerate its costs.
UPDATE: My spreadsheet is here, if you want to play with alternative parameter values.