productivity – J. W. Mason

At Barron’s: Thank Full Employment, Not AI, for Rising Productivity

(I write a monthly opinion piece for Barron’s. This one was published there in September. My previous pieces are here.)

New data about productivity are some of the best on record in recent years. That’s good news for economic growth. But just as important, it offers support for the unorthodox idea that demand shapes the economy’s productive potential. Taking this idea seriously would require us to rethink much conventional wisdom on macroeconomic policy.

Real output per hour grew 2.6% in 2023, according to the Bureau of Labor Statistics, exceeding the highest rates seen between 2010 and the eve of the pandemic. That said, productivity is one of the most challenging macroeconomic outcomes to measure. It is constructed from three distinct series—nominal output, prices, and employment. Short-term movements often turn out to be noise. It’s an open question whether that high rate will be sustained. But if it is, that will tell us something important about economic growth.

Discussions of productivity growth tend to treat it as the result of unpredictable scientific breakthroughs and new technologies, whose appearance has nothing to do with current economic conditions. This view of technological change as “exogenous,” in the jargon, is entrenched in economics textbooks. And it’s reinforced by the self-mythologizing barons of Silicon Valley, who are only too happy to take credit for economic good news.

The economic conditions that lead companies to actually adopt new technologies get much less attention, as does the fact that much productivity growth comes from people shifting from lower-value to higher-value activities without the need for any new technology at all.

A recent New York Times article is typical. It discusses faster productivity growth almost entirely in terms of the new technologies — AI, Zoom, internet shopping — that might, or might not, be contributing. Not until 40 paragraphs in is there a brief mention of the strong labor market, and the incentives that rising wages create to squeeze more out of each hour of labor.

What if we didn’t treat this as an afterthought? There’s a case to be made that demand is, in fact, a central factor in productivity growth.

The economic historian Gavin Wright has made this case for both the 1990s — our modern benchmarks for productivity success stories — and the 1920s, an earlier period of rapid productivity growth and technological change. Wright considers the adoption of general-purpose technologies: electricity in the ‘20s and computers in the ‘90s. Both had existed for some time but weren’t widely adopted until rising labor costs provided the right incentives. He observes that in both periods strong wage growth started before productivity accelerated.

In the retail sector, for instance, it was in the 1990s that IT applications like electronic monitoring of shelf levels, barcode scanning and electronic payments came into general use. None of these technologies were new at the time; what had changed was the tight market for retail employment that made automation worthwhile.

The idea that demand can have lasting effects on the economy’s productive potential – what economists call hysteresis — has gotten attention in recent years. Discussions of hysteresis tend to focus on labor supply — people dropping out of the labor market when jobs are scarce, and re-entering when conditions improve. The effect of demand on productivity is less often discussed. But it may be even more important.

After the 2007-2009 recession, gross domestic product in the U.S. (and most other rich countries) failed to return to its pre-recession trend. By 2017, a decade after the recession began, real GDP was a full 10% below what prerecession forecasters had expected. There is wide agreement that much, if not all, of this shortfall was the result of the collapse of demand in the recession. Former Treasury Secretary Larry Summers at the time called the decisive role of demand in the slow growth of the 2010s a matter of “elementary signal identification.”

Why did growth fall short? If you look at the CBO’s last economic forecasts before the recession, the agency was predicting 6% growth in employment between 2007 and 2017. And as it turned out, over those ten years, employment grew by exactly 6%. The entire gap between actual GDP and the CBO’s pre-recession forecasts was from slower growth in output per worker. In other words, this shortfall was entirely due to lower productivity.

If you believe that slow growth in the 2010s was largely due to the lingering effects of the recession — and I agree with Summers that the evidence is overwhelming on this point — then what we saw in that decade was weak demand holding back productivity. And if depressed demand can slow down productivity growth, then, logically, we would expect strong demand to speed it up.

A few economists have consistently made the case for this link. Followers of John Maynard Keynes often emphasize this link under the name “Verdoorn’s law.” The law, as Keynesian economist Matias Vernengo puts it in a new article, holds that “technical change is the result, and not the fundamental cause of economic growth.” Steve Fazzari, another Keynesian economist, has explored this idea in several recent papers. But for the most part, mainstream economists have yet to embrace it.

This perspective does occasionally make it into the world of policy debates. In a 2017 report, Josh Bivens of the Economic Policy Institute argued that “low rates of unemployment and rapid wage growth would likely induce faster productivity growth.” Skanda Amarnath and his colleagues at Employ America have made similar arguments. In a 2017 report for the Roosevelt Institute, I discussed a long list of mechanisms linking demand to productivity growth, as well as evidence that this was what explained slower growth since the recession.

If you take these sorts of arguments seriously, the recent acceleration in productivity should not be a surprise. And we don’t need to go looking for some tech startup to thank for it. It’s the natural result of a sustained period of tight labor markets and rising wages.

There are many good reasons for productivity growth to be faster in a tight labor market, as I discussed in the Roosevelt report. Businesses have a stronger incentive to adopt less labor-intensive techniques, and they are more likely to invest when they are running at full capacity. Higher-productivity firms can outbid lower-productivity ones for scarce workers. New firms are easier to start in a boom than in a slump.

When you think about it, it’s strange that concepts like Verdoorn’s law are not part of the economics mainstream. Shouldn’t they be common sense?

Nonetheless, the opposite view underlies much of policymaking, particularly at the Federal Reserve. At his most recent press conference, Fed Chair Jay Powell was asked whether he still thought that wage growth was too high for price stability. Powell confirmed that, indeed, he thought that wage gains were still excessively strong. But, he said, they were gradually moving back to levels “associated — given assumptions about productivity growth — with 2% inflation.”

The Fed’s view that price stability requires limiting workers’ bargaining power is a long-standing problem. But focus now on those assumptions. Taking productivity growth as given, unaffected by policy, risks making the Fed’s pessimism self-confirming. (This is something that Fed economists have worried about in the past.) If the Fed succeeds in getting wages down to the level consistent with the relatively slow productivity growth it expects, that itself may be what stops us from getting the faster productivity growth that the economy is capable of.

The good news is that, as I’ve written here before, the Fed is not all-powerful. The current round of rate hikes has not, so far, done much to cool off the labor market. If that continues to be the case, then we may be in for a period of sustained productivity growth and rising income.

Is Productivity Being Undermeasured?

(I am an occasional contributor to roundtables of economists in the magazine The International Economy. This month’s topic was: “What are the policy implications if productivity growth is being under-measured in the official data?” My answer is below.)

How many hamburgers equal one haircut?

In itself, the question doesn’t make sense. They’re just different things. What we can compare, is how much they cost. This is true across the board: The only way we can convert all the endlessly varied objects and activities that make up “the economy” into a single number, is through their market prices. Markets are what let us express all the various products of human labor as a single quantity we call output.

This means that productivity is only meaningful in the context of market prices. There are lots of things that people do that are useful, important, even essential to economic life, from raising children to following the law, that can’t be expressed as output per hour.

So it doesn’t really make sense to ask if the nonmarket effects of technological change mean we are undermeasuring productivity. A new technology may transform our lives in all sorts of ways, but we can’t talk about its effect on productivity except insofar as its products are sold. There’s no other basis on which productivity can even be defined – we have to go by market prices. And what market prices are telling us is that productivity growth is slower than it used to be.

This slowdown is not really surprising. Manufacturing – where the transformation of work by technology has gone farthest, and where productivity growth almost always fastest – is steadily shrinking as a share of the economy.

It is true that we often think of economic growth as something broader than market prices. It’s supposed to describe a more general rise in living standards. So a more meaningful way to ask the question might be: Does measured productivity growth accurately reflect the material improvements in people’s lives?

The answer here is indeed no. But unfortunately, in the rich countries at least, the mismeasurement probably goes the opposite way as the question suggests.

Measures like life expectancy used to be closely linked with economic growth. In poor countries, this is still the case – higher GDP is associated with longer lifespans, lower child mortality, and similar improvements in health and wellbeing. If anything, today’s GDP growth may be associated with even faster improvement than we would expect based on the historical record. But in richer countries the opposite is true – higher GDP no longer translates reliably into better health outcomes. In some places – like the UK, and much of the US – life expectancy is actually falling, even as income per capita continues to rise.

Leisure time is another measure of wellbeing — presumably if people were having an easier time meeting their material needs, they would choose to take more time off work. (Adam Smith once suggested that the amount of leisure people enjoyed was the only meaningful standard of economic value across countries.¹) On this measure too, living standards seem to be falling short of GDP growth rather than running ahead of them. Between the end of World War II and the early 1980s, the average weekly hours of an employed American fell by about 15 percent. But since then, average hours per worker have been essentially flat. This makes the postwar growth performance look even better, and the more recent performance worse, than the headline numbers suggest.

It seems likely that measured productivity overstates, rather than understates, our real improvement in living standards, at least in the US. If so, the policy implications seem clear. Policymakers should worry less about growth, and more about concrete interventions that we know improve people’s lives – things like universal access to childcare and health care, high-quality education, and paid time off for all.

Macroeconomic Lessons from the Past Decade

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.

Continue reading Macroeconomic Lessons from the Past Decade

“Economic Growth, Income Distribution, and Climate Change”

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. ² 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.³

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.

Reading Notes: Demand and Productivity

Here are two interesting articles on demand and productivity that people have recently brought to my attention.

The economic historian Gavin Wright — author of the classic account of the economic logic of the plantation — just sent me a piece he wrote a few years ago on the productivity boom of the 1990s. As he said in his email, his account of the ‘90s is very consistent with the suggestions I make in my Roosevelt paper about how strong demand might stimulate productivity growth.

In this article, Wright traces the idea that high wage regions will experience faster productivity growth back to H. J. Habbakuk’s 1962 American and British Technology in the Nineteenth Century. Then he assembles a number of lines of evidence that rapid wage growth drove the late-1990s productivity acceleration, rather than vice versa.

He points out that the widely-noted “productivity explosion” of the 1920s — from 1.5 percent a year to over 5 percent — was immediately preceded by a period of exceptionally strong wage growth: “The real price of labor in the 1920s … was between 50 and 70 percent higher than a decade earlier.” [1] The pressure of high wages, he suggests, encouraged the use of electricity and other general-purpose technologies, which had been available for decades but only widely adopted in manufacturing in the 1920s. Conversely, we can see the productivity slowdown of the 1970s as, at least in part, a result of the deceleration of wage growth, which — Wright argues — was the result of institutional changes including the decline of unions, the erosion of the minimum wage and other labor regulations, and more broadly the shift back toward “‘flexible labor markets,’ reversing fifty years of labor market policy.”

Turning to the 1990s, the starting point is the sharp acceleration of productivity in the second half of the decade. This acceleration was very widely shared, including sectors like retail where historically productivity growth had been limited. The timing of this acceleration has been viewed as a puzzle, with no “smoking gun” for simultaneous productivity boosting innovations across this range of industries over a short period. But “if you look at the labor market, you can find a smoking gun in the mid-1990s. … real hourly wages finally began to rise at precisely that time, after more than two decades of decline. … Unemployment rates fell below 4 percent — levels reached only briefly in the 1960s… Should it be surprising that employers turned to labor-saving technologies at this time?” This acceleration in real wages, Wright argues, was not the result of higher productivity or other supply-side factors; rather “it is most plausibly attributed to macroeconomic conditions, when an accommodating Federal Reserve allowed employment to press against labor supply for the first time in a generation.”

The productivity gains of the 1990s did, of course, involve new use of information technology. But the technology itself was not necessarily new. “James Cortada [2004] lists eleven key IT applications in the retail industry circa 1995-2000, including electronic shelf levels, scanning, electronic fund transfer, sales-based ordering and internet sales … with the exception of e-business, the list could have come from the 1970s and 1980s.”

Wright, who is after all a historian, is careful not to argue that there is a general law linking higher wages to higher productivity in all historical settings. As he notes, “such a claim is refuted by the experience of the 1970s, when upward pressures on wages led mainly to higher inflation…” In his story, both sides are needed — the technological possibilities must exist, and there must be sufficient wage pressure to channel them into productivity-boosting applications. I don’t think anyone would say he’s made a decisive case , but if you’re inclined to a view like this the article certainly gives you more material to support it.

A rather different approach to these questions is this 2012 paper by Servaas Storm and C. W. M. Naastepad. Wright is focusing on a few concrete episodes in the history of a particular country, which he explores using a variety of material — survey and narrative as well as conventional economic data. Storm and Naastepad are proposing a set of general rules that they support with a few stylized facts and then explore via of the properties of a formal model. There are things to be learned from both approaches.

In this case the model is simple: output is demand-determined. Demand is either positive or negative function of the wage share (i.e. the economy is either wage-led or profit-led). And labor productivity is a function of both output and the wage, reflecting two kinds of channels by which demand can influence productivity. And an accounting identity says that employment growth is qual to output growth less labor productivity growth. The productivity equation is the distinctive feature here. Storm and Naastepad adopt as “stylized facts” — derived from econometric studies but not discussed in any detail — that both parameters are on the order of 0.4: An additional one percent growth in output, or in wages, will lead to an 0.4 percent growth in labor productivity.

This is a very simple structure but it allows them to draw some interesting conclusions:

– Low wages may boost employment not through increased growth or competitiveness, but through lower labor productivity. (They suggest that this is the right way to think about the Dutch “employment miracle of the 1990s.)

– Conversely, even where demand is wage-led (i.e. a shift to labor tends to raise total spending) faster wage growth is not an effective strategy for boosting employment, because productivity will rise as well. (Shorter hours or other forms of job-sharing, they suggest, may be more successful.)

– Where demand is strongly wage-led (as in the Scandinavian countries, they suggest), profits will not be affected much by wage growth. The direct effect of higher wages in this case could be mostly or entirely offset by the combination of higher demand and higher productivity. If true, this has obvious implications for the feasibility of the social democratic bargain there.

– Where demand is more weakly wage-led or profit-led (as with most structuralists, they see the US as the main example of the latter), distributional conflicts will be more intense. On the other hand, in this case the demand and productivity effects work together to make wage restraint a more effective strategy for boosting employment.

It’s worth spelling out the implications a bit more. A profit-led economy is one in which investment decisions are very sensitive to profitability. But investment is itself a major influence on profit, as a source of demand and — emphasized here — as a source of productivity gains that are captured by capital. So wage gains are more threatening to profits in a setting in which investment decisions are based largely on profitability. In an environment in which investment decisions are motivated by demand or exogenous animal spirits (“only a little more than an expedition to the South Pole, based on a calculation of benefits to come”), capitalists have less to fear from rising wages. More bluntly: one of the main dangers to capitalists of a rise in wages, is their effects on the investment decisions of other capitalists.

What Recovery: Reading Notes

My Roosevelt Institute paper on potential output came out last week. (Summary here.) The paper has gotten some more press since Neil Irwin’s Times piece, including Ryan Cooper in The Week and Felix Salmon in Slate. My favorite headline is from Boing Boing: American Wages Are So Low, the Robots Don’t Want Your Jobs.

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. [1] 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. [2] 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.

[1] 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.

[2] I made similar arguments here.

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.

The Big Question for Macroeconomic Policy: Is This Really Full Employment?

Cross-posted from the Roosevelt Institute’s Next New Deal blog. This is a summary of my new paper What Recovery? The Case for Continued Expansionary Policy, also discussed in Neil Irwin’s July 26 article in the Times.

“Right now,” wrote Senator Chuck Schumer in a New York Times op-ed on Monday, “millions of unemployed or underemployed people, particularly those without a college degree, could be brought back into the labor force” with appropriate government policies. With this seemingly anodyne point, Schumer took sides in a debate that has sharply divided economists and policymakers: Is the US economy today operating at potential, with enough spending to make full use of its productive capacity? Or is there still substantial slack, unused capacity that could be put to work if someone — households, businesses or governments — decided to spend more? Is there an aggregate-demand problem that government should be trying to solve?

It’s difficult to answer this question because the economic signals seem to point in conflicting directions. Despite the recession officially ending in June 2009 and the economy enjoying steady growth for the past eight years, GDP is still far below the pre-2008 trend. If we compare GDP to forecasts made before the recession, the gap that opened up during the recession has not closed at all — in fact, it continues to get wider. Meanwhile, the official unemployment rate — probably the most watched indicator for the state of aggregate demand — is down to 4.4%, well below the level that was considered full employment even a few years ago. But this positive performance only partially reflects an increase in the number of Americans with jobs; mostly it comes from a decline in the size of the labor force — people who have or are seeking jobs. The fraction of the adult population employed is down to 60 percent from 63 percent a decade ago (and nearly 65 percent at the end of the 1990s).

Is this decline in the fraction of people employed the inevitable result of an aging population and similar demographic changes, or is it a sign that, despite the low measured unemployment rate, the economy is still far short of full employment? The Federal Reserve — one of the main sites of macroeconomic policy — has already indicated its belief that full employment has been reached by raising interest rates 3 times since December 2016. Fed Chair and Janet Yellen are evidently convinced that the economy has reached its potential — that, given the real resources available, output and employment are as high as can reasonably be expected.

Other policymakers have been divided on the question, in ways that often cut across partisan lines. Senator Schumer’s statement — that the decline in employment is not an inevitable trend but rather a problem that government can and should solve — is a sign of new clarity coming to this murky debate. Along with his call for $1 trillion in new infrastructure spending, it’s an important acknowledgement that, despite the progress made since 2008, the country remains far from full employment.

In a new paper out this week, we at the Roosevelt Institute offer support for the emerging consensus that the economy needs policies to boost demand. The paper reviews the available data on where the economy is relative to its potential. We find that the balance of evidence suggests there is still a great deal of space for more expansionary policy.

We offer several lines of argument in support of this conclusion.

GDP has not recovered from the recession. GDP remains about 10 percent below both the long-term trend and the level that was predicted by the CBO and other forecasts prior to the 2008–2009 recession. There is no precedent in the postwar period for such a persistent decline in output. During the sixty years between 1947 and 2007, growth lost in recessions was always regained in the subsequent recovery.

The aging population does not explain low labor force participation. It is true that an aging population should contribute to lower employment, since older people are less likely to work than younger people. But this simple demographic story cannot explain the full fall in employment. Starting from the employment peak in 2000, aging trends only explain about half the decrease in employment that has actually occurred. And there are good reasons to think that even this overstates the role of demographics. First, during the same period, education levels have increased. Historically, higher education has been associated with higher employment rates, just as a share of elderly people has been associated with less employment; statistically, these two effects should just about cancel out. Second, the post-recession fall in employment rates is not concentrated in older age groups, but among people in their 20s — something that a demographic story cannot explain.

The weak economy has held back productivity. About half the shortfall in GDP relative to the pre-2008 trend is explained by exceptionally slow productivity growth — that is, slow growth in output per worker. While many people assume that productivity is the result of technological progress outside the reach of macroeconomic policy, there are good reasons to think that the productivity slowdown is at least in part due to weak demand. Among the many possible links: Business investment, which is essential to raising productivity, has been extraordinarily weak over the past decade, and economists have long believed that demand is a central factor driving investment. And slow wage growth — a sign of labor-market weakness — reduces the incentive to adopt productivity-boosting technology.

Only a demand story makes sense. The overall economic picture is hard to understand except in terms of a continued demand shortfall. If employment is falling due to demographics, that should be associated with rising productivity and wages, as firms compete for scarce labor. If productivity growth is slow because there aren’t any more big innovations to make, that should be associated with faster employment growth and low profits, as firms can no longer find new ways to replace labor with capital. But neither of these scenarios match the actual economy. And both stories predict higher inflation, rather than the persistent low inflation we have actually encouraged. So even if supply-side stories explain individual pieces of macroeconomic data, it is almost impossible to make sense of the big picture without a large fall in aggregate demand.

Austerity is riskier than stimulus. Finally, we argue that, if policymakers are uncertain about how much space the economy has for increased demand, they should consider the balance of risks on each side. Too much stimulus would lead to higher inflation — easy to reverse, and perhaps even desirable, given the continued shortfall of inflation relative to the official 2 percent target. An overheated economy would also see real wages rise faster than productivity. While policymakers often see this as something to avoid, the decline in the wage share over the past decade cannot be reversed without a period of such “excess” wage growth. On the other hand, if there is still an output gap, failure to take aggressive steps to close it means foregoing literally trillions of dollars of useful goods and services and condemning millions of people to joblessness.

Fortunately, the solution to a demand shortfall is no mystery. Since Keynes, economists have known that when an economy is operating below its potential, all that is needed is for someone to spend more money. Of course, it’s best if that spending also serves some useful social purpose; exactly what that should look like will surely be the subject of much debate to come. But the first step is to agree on the problem. Today’s economy is still far short of its potential. We can do better.

Demand and Productivity

I’m picking up, after some months, the project I was working on over the summer on potential output. Obviously the political context is different now. But the questions of what potential output actually means, how tightly it binds, and how close the economy is to it at any given moment, are not going away. Previous entries: one, two, three, four, and five.

You’ve probably heard the story about Ed Rensi, the former McDonald’s CEO who claimed the company’s move to replace cashier’s with self-serve kiosks was a response to minimum wage increases.

“I told you so,” he writes. “In 2013, when the Fight for $15 was still in its growth stage, I and others warned that union demands for a much higher minimum wage would force businesses with small profit margins to replace full-service employees with costly investments in self-service alternatives.”

Is this for real? Maybe not: The shift toward kiosks has been happening for a while, so it’s not just a response to the recent minimum wage hikes; and it may not end up reducing labor costs anyway.

But let’s say the move is as as Rensi claims. Then we should call it what it is: an increase in labor productivity. With fewer workers McDonald’s will produce just as many hamburgers; in other words, production per worker will be higher. [1]

As I’ve suggested, this sort of thing is a real problem for a certain strand of minimum wage advocacy. Advocates like to point to productivity gains in response to higher wages as an argument in their favor. (The gains are usually imagined in terms of loyalty, motivation, lower turnover, etc. rather than machines, but functionally it’s the same.) But productivity gains can only reduce the job losses from a minimum wage increase if those losses are large; they are not consistent with a story in which employment stays the same. [2]

But at the macro level, this dynamic has different implications. If the McDonald’s case is typical — if higher labor costs regularly lead to higher productivity — then we need to rethink our idea of supply constraints. There is more space for expansionary policy than we usually think.

Let’s start at the beginning. Suppose there is some policy change, or some random event, that boosts desired spending in the economy. It could be more government spending, it could be lower interest rates, it could be a rise in exports. What happens then?

In the conventional story, higher spending normally leads to greater production of goods and services, which in turn requires higher employment. This leaves fewer people unemployed. Lower unemployment increases the bargaining power of workers, forcing employers to bid up nominal wages. [3] These higher wages are passed on to prices, leading to higher inflation. When inflation reaches whatever level is considered price stability, then we say the economy is at full employment, or at potential output. (In this story the two are equivalent.) If spending continues to rise past this point, the responsible authorities (normally the central bank) will intervene to bring it back down.

This is the story you’ll find in any good undergraduate macroeconomics textbook. It’s a reasonable story, as far as these things go. In the strong form it’s usually given in, it implies a hard limit to how much demand can increase before inflation starts rising unacceptably. Once the pool of unemployed workers falls to the “full employment” level, any further increase in employment will lead to rapid increases in money wages, which will be passed on one for one to inflation.

One place this chain can break is that new workers are not necessarily drawn from the ranks of the currently unemployed — that is, if the size of the laborforce is endogenous. Insofar as people counted as out of the laborforce are in fact available for employment (or net immigration responds to demand), an increase in output doesn’t have to reduce the ranks of the officially unemployed. In other words, the official unemployment rate may underestimate the space available for raising output via increased employment. This motivates the question of how much the the fall in laborforce participation since 2007 is due to demographics, and how much is due to weak demand.

The conventional story can also break down at two other places if productivity growth is endogenous. First, output can increase without a proportionate increase in employment. And second, wages can rise without a proportionate rise in prices.

It’s useful to think about this in terms of a couple of accounting identities, which in my opinion should be part of every macroeconomics textbook. [4] The first is obvious (but worth spelling out), the second a little less so:

(1) growth in demand = percent change in labor productivity + percent change in employment + inflation

(2) percent change in nominal wages = percent change in labor productivity + percent change in labor share + inflation

The standard story is that productivity change on its own due to technology, and the labor shared is fixed and can be ignored in this context. If productivity and labor share can be taken as given, then an increase in demand (money spent on final goods and services) must lead to higher inflation if either employment fails to rise, or if it rises only with higher wages. In this story, if nominal wages rise thanks to a lower unemployment rats, that will pass on one for one to inflation. Pick up an advanced undergraduate textbook like Blanchard or Krugman or Carlin and Soskice, and you will find a Phillips curve of exactly this form, with exactly this story behind it. [5] Policy discussions at central banks conducted in same terms.

This is what underlies idea of hard supply constraints. Output growth is dictated by the fixed, exogenous growth of the laborforce and of productivity. If changes in demand push the economy off that fixed trajectory, all you’ll get is higher or lower inflation. Concretely: To keep inflation at 2 percent, unemployment must be such as to generate nominal wage growth 2 points above the technologically-determined growth of productivity.

But an alternative story is that variation in demand can lead to adjustment in one of the other terms. One possibility is that the laborforce adjusts, as participation rates vary in response to demand conditions. This is what is most often meant by hysteresis: persistent deviations in unemployment from the “natural” level lead to people entering or exiting the laborforce. That implies that even when headline unemployment rates are fairly low, further increases in employment may be possible without a rise in wages. Another possibility is that while higher employment will lead to (or require) higher wages, the wage increase is not passed on to prices but comes at the expense of profits instead. This is Anwar Shaikh’s classical Phillips curve; I’ve written about it here before.

A third possibility is that higher wages are accompanied by higher productivity. Again, this appears as a problem when we are talking about wage increases from legislation, union contracts, or similar developments. But it’s not a problem if the wage increases are thanks to low unemployment. In this case, the joint movement of wages and productivity just means that output can rise higher — that supply constraints are softer. That’s what I want to focus on now.

There are a number of reasons why productivity might rise with wages. Some of them simply amount to mismeasurement of employment — it appears that output per worker is rising but really the effective number of workers is. Others are more fundamental. If productivity responds strongly and persistently to demand, it blurs the distinction between aggregate supply and aggregate demand, to the point that it’s not clear what “potential output” even means.

Suppose we do find a consistent pattern where, if demand is strong, unemployment is low, and wages are rising rapidly, then productivity growth is high. What could be happening?

1. Increased hours. If we measure productivity as output per worker, as we usually do, then an increase in average hours worked will show up as an increase in productivity. There is a cyclical component to this — in recessions, employers reduce hours as well as laying off workers. According to the BLS, seasonally adjusted weekly hours fell from 34.4 prior to the recession to a low of 33.7 in summer 2009. While a 2 percent fall in hours might seem small, it’s a big change in less than two years, especially when you consider that real output per worker normally rises by less than 2 percent a year.

2. Workers moving into real jobs from pseudo-employment or disguised unemployment. In any economy there are activities that are formally classified as jobs but are not employment in any substantive sense — you can take these “jobs” without anyone making a decision to hire you, and they don’t come with a wage or any similar claim on any established production process. Joan Robinson’s examples were someone who gathers firewood in a poor country, or sells pencils on streetcorners in a richer one. You could add work in family businesses and various kinds of self-employment and commission-based work to this category. In countries with traditional rural sectors — not the US — work on a family farm is the big item here. These activities absorb people who are unable to find formal jobs; the marginal product of additional workers here is normally very low. So if higher demand draws people from this kind of disguised unemployment back into regular jobs, measured productivity will rise.

3. Workers may be more fully utilized at their existing jobs. Because hiring and firing is costly, business don’t immediately adjust staffing in response to changes in sales. when demand falls, businesses will initially keep some redundant workers because paying them is cheaper than laying them off and replacing them later; and when demand rises, businesses will first try to get more work out of existing employees rather than paying the costs of hiring more. Some of this takes the form of the hours adjustment above, but some of it simply takes the form of hiring “too little” or “too much” labor for the current level of production. These changes in the utilization of existing labor will show up as changes in labor productivity.

4. Higher wages may lead to more capital-intensive production. This is the McDonald’s story: When labor gets more expensive (or scarcer), businesses use more capital instead. This is presumably what people mean when they say “Econ 101” shows that rising wages lead to less employment (assuming they mean anything at all). This may be seen as a negative when it’s a question of raising wages through legislation or unions, but it shouldn’t be when it’s a question of rising wages due to labor scarcity. Insofar as businesses can substitute machines for labor, rising wages will not be passed on to prices, so there is more space to push unemployment down.

5. Productivity-boosting innovations may be more likely when demand is strong and wages rise. This is a variant of the previous story. Now instead of high wage leading business to adopt more capital-intensive techniques from those already available, they redirect innovation toward developing new labor-saving techniques. Conceptually this is not a big difference, but it implies a different signal in the data. In the previous case we would expect the productivity improvements to be associated with higher investment and to be concentrated at the firms actually experiencing higher wages costs; in this case they might not be.

6. The composition of employment may shift toward higher-productivity sectors. This might happen for either of two reasons. First, higher wages will disproportionately raise costs for more labor-intensive sectors; these higher costs may be absorbed by profits or by prices, but either way they will presumably depress growth in those sectors to the benefit of less labor-intensive, more productive ones. Second, it may so happen that the more income-elastic sectors are also higher-productivity ones. In the short run this is presumably true since durables and investment goods are both capital-intensive and income-elastic. Over the longer run, the opposite is more likely — the composition of demand slowly but steadily shifts toward lower-productivity sectors.

7. The composition of employment may shift toward higher-productivity firms. This sounds similar but it’s a different story. Technical change isn’t an ineffable output-raising essence diffusing across society, it’s embodied in specific new production processes and new businesses — Schumpeter’s new plant, new firms, new men. This means that productivity increases often require new or growing firms to attract workers away from established ones. Given the “frictions” in the labor market, this will require offering a wage significantly above the going rate. And on the other side the fact that the least productive firms can’t afford to pay higher wages will cause them to decline or exit, which also raises average productivity. When wages are flat, on the other hand, low-productivity firms can continue operating. In this sense, higher wages are an integral part of productivity growth. [6]

8. There may be increasing returns in production. It may literally be the case that output per worker rises — at the firm, industry or economy-wide level — when the number of workers rises. Or this may be a more abstract version of some of the stories above. It’s worth noting that increasing returns is an area where the intuitions of people with economics training diverge sharply from people who look at the economy through other lenses. To almost anyone except an economist, it’s obvious that costs normally fall as more of something is produced. [7]

All of these stories imply that higher demand should lead to higher measured labor productivity. But to figure out how strong this relationship is in reality, we’ll look at different data depending on which of these stories we think it works through.

Another important difference between the stories is they imply different domains over which the relationship should operate. The first three suggest a more or less immediate response of productivity to changes in demand, but also one that cannot continue indefinitely. There’s limits to how much hours per worker can rise and how much additional effort can be extracted from the existing workforce, and a limited pool of disguised unemployment to draw from. (The last is not true in developing countries, where the “latent reserve army” in subsistence agriculture may be effectively unlimited.) The other mechanisms are presumably slower, requiring a sustained “high-pressure economy.” With these stories, increased demand may push the economy up against supply constraints, with rising inflation, bottlenecks, and so on; but if it keeps pushing against them, eventually they’ll give. In this case, potential output is a medium-term constraint — over longer periods it can adjust to actual output, rather than the reverse. So in the opposite of conventional story, a temporary increase in inflation can lead to a permanent increase in output. People like Laurence Ball say exactly this about hysteresis, but they are usually thinking of the longer-run adjustment coming on the laborforce side.

If we follow this a step further, we could even say that in the long run, the big problem isn’t that excessively high wages do lead to the substitution of capital for labor but that excessively low wages don’t. People like Arthur Lewis argue that it’s the low wages of poor countries that have led to low productivity there, and not vice versa; there’s a well-known argument that the reason the industrial revolution happened first in Britain rather than in China or India (or Italy or France) is not that that the necessary technical innovations were present only in Britain. They were present many places; it was the uniquely high cost of British labor that made them profitable to adopt for production.

I think that productivity does respond to demand. I think this is a good reason to doubt whether the US economy close to “potential output” today, and to doubt what, if anything, this concept actually means. But I also think we need to be clearer about how they are linked concretely. If we want to tell a story about productivity responding to demand, it makes a difference which of the stories above we have in mind. Heterodox people, it seems to me, are too quick to just invoke Verdoorn’s law (productivity rises with output), and justify it with some vague comments about how labor is used more efficiently when it is scarce. [8] Does this apparent law work via substitution of machines for labor, or through fuller utilization of existing employees’ times, or through reallocation of labor to more productive firms and/or industries, or through a labor-saving-bias in technical change, or pure increasing returns, or what? If you’re just making a formal model it may not matter. But if we want to connect the model to concrete historical developments, it certainly does.

Personally, I am most interested in the reallocation stories. They shift our idea of the fundamental constraint on capitalist economies from biophysical resources, to coordination. The great difficulty for any program of raise or transform production — industrialization, wartime mobilization, decarbonization — isn’t the limited supply of “real” resources, but the speed at which people’s productive activity can be redirected in a coordinated way. This connects with the historical fact that the more rapid and the larger scale is economic development, the more it requires some form of central planning. And it implies that at the most basic level, what the capitalist provides is not money or means of production, but cooperation.

To tell this story, it would be nice if big shifts in productivity growth took the form of changes in the composition of employment, rather than higher output per worker in given jobs. That may or may not be there in the data. For the more immediate question of how much space there is in the US for further expansion, it doesn’t matter as much which of these stories is at work, as long as we can show that at least some of them are. [9]

In the next post or two — which I hope to write in the next week, but we’ll see — I will ask what we can say about the link between demand and productivity based on historical US data. In particular, it’s fairly straightforward to decompose changes in output per worker into three components: within-industry output per hour, within-industry hours per worker, and shifts in the employment between industries. Splitting up productivity growth this way cannot, of course, directly establish a causal link with demand. but it can help clarify which stories are plausible and which are not.

[1] Throughout this discussion, I use “productivity” to mean labor productivity — output per worker or per hour. There is also “total factor productivity,” which purports to be a measure of output for a given input of labor and capital. This concept, which IMF chief economist Paul Romer memorably called “phlogiston,” is measured as the residual from a production function — the output growth the function does not explain. Since construction of the production function requires several unobseravable parameters, total factor productivity cannot be derived even in principle from economic data. It’s a fun toy for economic theory but useless for describing the behavior of actual economies.

Nonetheless it is widely used — for instance by the CBO as discussed here. As Nathan Tankus pointed out to me the other day, under the ARRA Medicare payments to hospitals are reduced each year based on an estimate of TFP growth for the economy as a whole. It’s a great example of the crackpot wonkery of the law’s authors.

[2] Unless productivity improvements all take the form of higher quality, rather than higher output per worker.

[3] This unemployment-money wages relationship was the original Phillips curve, but it’s better now to refer to it as a wage curve.

[4] It’s a topic for another time, but I think it would be very natural to replace the “aggregate supply” framework of the textbooks with these two identities.

[5] Other textbooks, like Mankiw, base the wage-unemployment relationship on a labor-supply curve rather than a bargaining relationship. Graduate textbooks, of course, replace the institutional detail of workers and employers with a single representative agent, in order to make more space for playing with math.

[6] Andrew Glyn and his coauthors have a good discussion of this in the context of the postwar boom in Capitalism Since 1945 (p. 122-123).

[7] For example, here’s Laurie Winkless in Science and the City, which happens to be sitting nearby:

Bessemer’s system rapidly began to change the world of steel manufacturing, and by 1875, costs had dropped to $32 (£23) per tonne. as always, in the supply-and-demand equation, the availability of cheap, high-quality steel made it immensely popular, leading to another huge drop in the price per tonne.

Winkless has made the mistake of studying the actual history of the steel history. If she were an economist, she would know that in the world of supply and demand, immense popularity makes prices rise, not fall!

[8] In Shaikh’s Capitalism, for example, there are a number of models that rely on the claim that productivity rises with output. It’s a big book and I may well have missed a part where he explains more fully why this is true. But as far as I can tell, all he says is that higher unit labor costs “provide a strong incentive for firms to raise productivity.”

[9] The politics of this question under Trump are for another time. But certainly Jeff Spross is right that we don’t want to oppose Trump’s (dubious) plans for a big stimulus by embracing the politics of austerity. We should not respond to Trump by reflexively insisting that the US is already at full employment, and by mocking “vulgar Keynesians” who think there might still be problems for macro policy to solve.

EDIT: Fixed the footnote numbering, which was garbled before.