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At Age of Economics: How Should an Economist Be?

Published by Anonymous (not verified) on Sat, 24/07/2021 - 2:43pm in

The website Age of Economics has been carrying out a series of interviews with economists about what the purpose of the discipline it is, and what its relationship is to capitalism as a historical social system. I believe there will be 52 of these interviews, one each week over the course of 2021. Earlier this spring, they interviewed Arjun Jayadev and myself. You can watch video of the interview here. I’ve pasted the transcript below.


Q: Why does economics matter?

JWM: The most obvious way that economics matters is that it has an enormous prestige in our society. Economists have a level of respect and authority that no other social scientist, arguably no other academic discipline possesses. An enormous number of policy debates are conducted in the language of economics. There’s an ability of an economist to speak directly in policy settings, in political settings in a way that most academics simply can’t. And so Joan Robinson has that famous line that the reason you study economics is to avoid being fooled by economists.

And there’s some truth to that. Even if you think that the discipline is completely vacuous, it’s worth learning its language and techniques just in order to be able to at least criticize the arguments that other economists are making. But I would say we don’t think that economics is completely worthless and vacuous because we think it does bring some positive ways of thinking to the larger conversation. One thing that is defining of economics is the insistence on formalizing ideas, expressing your thoughts in some highly abstract way, either as a system of equations or a system of diagrams in a way where you’re explicitly stating all of the causal relationships that you think exist in the story that you’re trying to tell.

And that’s a useful habit of thinking that is not necessarily as widespread outside the economics profession. Sometimes you can learn new things just by writing down your assumptions and working through them. The whole debate in the heterodox field about wage led growth versus profit led growth, what are the circumstances where redistribution from profits to wages is likely to boost demand? And what are the situations where it’s likely to reduce demand? There are real insights that come out of trying to write down your vision of the economy as a system of equations.

The notion of balance of payments-constrained growth, where we think that maybe for a lot of countries, the thing that’s fundamentally driving the rate of growth that they can sustain is how responsive, how income-elastic, their exports are versus their imports is another set of ideas that comes out of writing down a formal model in the first case.

So this is a useful discipline that training as an economist gives you, that people with other kinds of backgrounds don’t have. This effort to make explicit the causal connections that you have in mind.

AJ:  It’s also important to realize that economics has come up with some very useful concepts, to make sense of this world around us: concepts like GDP or employment. These are concepts which are well defined and measured, and help us to have an understanding of the system as a whole.

Admittedly, lots of economics education doesn’t pay as much attention to this side of economics as it should. And maybe the question was an implicit critique — when you ask why does economics matter, there are some people who feel that it doesn’t matter because of what’s happened to the discipline. Josh and I both like this particular quote by the economist Trygve Haavelmo. He said that the reason that you learn economics is to – I believe the phrase is – “to be a master of the happenings of real life”.

And that that’s why one should be doing economics, not as an exercise in and of itself, but to understand what’s happening in the world.

JWM: That’s right. The real secret to doing good economics is to start from somewhere other than economics. You may come into economics with a set of political commitments as Arjun and I both did, but you may also come in with a desire to make money in the business world and you’re associating with people who do that, or you come in because you’re focused on a particular set of public debates that you want to clarify your thinking about. If you come in with some other set of concerns that are going to guide you in terms of what’s important, what’s relevant, what’s reasonable, then you’ll find a lot of useful tools within economics.

The problem arises with people – and, unfortunately, this I’d say is the majority of professional economists – who don’t have any independent intellectual or personal base, their intellectual development is entirely within academic economics. And then it becomes very easy to lose sight of the happenings of real life that this field is supposed to be illuminating.

Q: What are the differences between economic science (academic economics) and economic engineering (policymaking)?

JWM: Today there’s a very wide gap between academic economics and what we might call policy economics, particularly in macro. If you’re a labor economist, maybe the terms that are used in academic studies and the terms that are used in policy debates might be might be closer to each other. But there’s a long standing divide between the questions that academic macroeconomists ask and the questions that come in policy debates which has gotten much wider since the crisis.

The unfortunate fact – and people are going to say this is not fair, but I can tell you, I’ve looked at qualifying exams, recent ones from graduate programs in macroeconomics, and this is a fair characterization, what I’m about to say – that the way academic macroeconomics trains people to think is to imagine a representative agent with perfect knowledge of the probabilities of all future events, who is then choosing the best possible outcome for them in terms of maximizing utility over infinite future time under a given set of constraints. That is literally what you are trained to think about if you are getting an academic training in macroeconomics. For people who are not economists listening to this, you have to study this stuff to understand how weird it is.

Unfortunately that aspect of the profession has not changed very much since the financial crisis of a decade ago. On the other hand, the public debate on macroeconomic questions has moved a lot. So there’s a much wider range of perspectives if you look at people in the policy world or the financial press or even in the business world. So in some ways the public debate has gotten much better over the past decade, but that’s widening the gap between the public debate and academic macroeconomics. I don’t know how exactly this will come about, but at some point we’re going to have to essentially throw out the existing graduate macroeconomics curriculum and start fresh, roll back the clock to 1979 or start from somewhere else, because it does seem like the dominant approach in academic macroeconomics is an intellectual dead end.

AJ: We have friends who are doing a lot of good work in labor economics. People like Arin Dube at UMass Amherst, which is one of these places which takes these things seriously, or my colleague Amit Basole where I am at Azim Premji University. And in some fields there is back and forth between the world that exists and policymaking and the craft of economics and academic economics.

It requires also talking to people from outside the discipline to see how far academic economics and macroeconomics has drifted away from policymaking. And this is why I come back to the Haavalmo point. The reason for us to be doing many of the things we are doing is academic macroeconomist is to try to see if we can have an effect on the world, understand the world. And this distinction has become so sharp right now to make it dysfunctional.

Now, the additional problem that comes with it is that because this kind of theory is hard, it’s complex and it’s weird, people spend a lot of time invested in this activity. When I say this activity, I mean basically solving equations, but for some imaginary state. That’s not only limited to macro, but it’s the worst in macro. And as a result, it becomes very hard for people to pull away from that, and say that there’s something wrong. The emperor’s new clothes moment is extremely painful to face.

But it is interesting that one of the advantages of studying macroeconomics is there are always people who want to understand what’s happening in the world. And what you might call concrete policy macroeconomics has got much more open, much more interesting than in the past. There’s an economic science aspect in concrete policy macroeconomics. I wouldn’t want to separate them so sharply as you might have done in the question.

JWM: And to be fair, there are plenty of prominent mainstream macroeconomists who have a lot of interesting and insightful things to say about real economies. The thing is that when they’re talking about the real world, they ignore what they do in their scholarly work. They’re smart enough and they’ve got time and energy that they can they can follow both tracks at once, but they’re still two separate tracks.

But for most people, that’s not practical. And if you get sucked into the theory, then you stop thinking about the real questions. And the other thing, just to be fair, is that in the world of empirical macroeconomics, there’s more interesting work being done. The problem is that there isn’t a body of theory that the empirical work can link up with.

Q: What role does economics play in society? Does it serve the common good?

JWM: You can certainly criticize economists for being ideological. There are very specific assumptions about how the world works that are baked into the theory in a way that is not even visible to the people who are educated in that theory.

But it’s almost impossible to imagine a non-ideological economics. In principle we could study the economy scientifically in the way we study other areas of existence scientifically. But we can’t do it as long as we live in a capitalist economy because the questions are too close to the basic structures of authority and hierarchy of our society. They are too close to the ways that all the inequalities, all the sources of power in our society are legitimated.

They can’t just be scrutinised in a neutral way from the outside. So as long as we live under capitalism, we are never going to have an established scientific study of capitalism. That’s just not possible. In a way, you could even say that the function of a lot of academic economics is not so much to instill a particular ideological view of capitalism, but just to stop people from thinking about it systematically at all. It gives you something else to think about instead.

That doesn’t mean that on an individual level we should not aspire to be scientific in a broad sense in our approach. We should expose our ideas to critical scrutiny. We should systematically consider alternatives and formulate hypotheses and see if the world is giving us reason to think our hypotheses are right or wrong. we should follow that.

But we should also recognize that you’re going to be on the margins as you do this. That’s OK, because the life of a professional economist is pretty good. So the margins of the profession is still a perfectly fine place to be. But that’s where you’re going to be. Or occasionally in moments of deep crisis, when the survival of the system is at stake, then there will be periods where a more rational perspective on it is tolerated.

But the notion that we’re going to persuade people in the economics profession that we have a better set of ideas and we’re going to win out that way, it misses that there is a deep political reason why economics is the way it is. So again, as we were saying at the beginning, if you want to do good scientific work, you have to have a foot outside the profession to give you a base somewhere else.

Hayek is probably not somebody that neither of us agrees with on very much, but he has a nice line about this, he says, “no one can be a great economist who is only an economist.” And that’s very true.

AJ: The question reminds me of the famous story about Keynes when he finishes being the editor of the Economic Journal, where he raises a toast to the economists who are the trustees of the possibility of civilization. There’s a belief among economists that  they are standing apart and guiding the forces of history.

Well, that sounds a little pompous. Keynes could get away with it. Nowadays we wouldn’t say that, but we’d say that we maximizing social welfare, which is in some ways the same thing. One of the things that you ask is, is it serving the common good? One of the things that economics does in its training is posit a common good. And that immediately takes you away from the space of politics. Because there are many situations in the economy in which there are conflicts of interest.

These are not just conflicts of opinions. It’s conflicts around things like the distribution of income and so on. And these questions become unavoidably political. It’s pulling away from that, which, by the way, the Classical economists never did, that allows you to talk about something abstract like social welfare. So I would say the economics can play a role in trying to understand what we would want to have from a democratic, open, egalitarian society. But positing something like the common good can sometimes obscure that.

Q: Economics provides answers to problems related to markets, efficiency, profits, consumption and economic growth. Does economics do a good job in addressing the other issues people care about: climate change and the wider environment, the role of technology in society, issues of race and class, pandemics, etc.?

JWM: We might turn this question around a little bit. Economics does best when it’s focused on urgent questions like climate change. We do better economics when we’re oriented towards towards real urgent live political questions like around race and class. This is what we’re saying: Economics when it’s focused on questions of markets and efficiency in the abstract, doesn’t contribute very much to the conversation. It quickly loses contact with the real phenomena that it’s supposed to be dealing with.

And what focuses our attention is precisely that second set of questions that you raise. Those are the questions that create enough urgency to force people to adopt a more realistic economics. So in that sense, we do a better job talking about markets, we give a better, more useful definition of things like efficiency when we’re focused on concrete questions like climate change. There’s a good reason that modern macroeconomics begins with the Great Depression, because this is a moment when you do need to look at the economy as it is.

Today, it’s obvious that the existing models aren’t working, and there’s a political urgency to coming up with a better set of stories, a better set of tools. The climate crisis has a good chance to be a similar clarifying moment as the 1930s, more so than the financial crisis of a decade ago or whatever the next financial crisis is.

Climate change may force us to rethink some of our broader economic ideas in a more fundamental way. The truth is established economic theory does not give good answers in general to the problems of profits, economic growth and so on. And a focus on climate change can improve the field in that way.

The other thing you bring up is race, class, and gender. The problem here is that nobody has a God’s eye view of the world. Nobody can step out of their own skin and see things from a perfectly objective view. As a middle class white man in the United States, I have a particular way of looking at the world, which is in some ways a limiting one. Economics as a field would be better if we had more diversity, a broader range of backgrounds and perspectives.

AJ: I’d like to add, there is no reason why a particular set of tools that you use in one sphere should automatically be something that you can use in another sphere. The way that modern economics is set up is just a set of maximization problems, it allows people to seamlessly say that they are studying on the one hand buying oranges and apples, and on the other side solving the problems of climate change.

So there is an issue in the way that you posit, that  it is using tools which it may be – I agree with Josh, it’s not very good at – but it may be better than its applications in other spheres. A famous example is the choice of discount rate for climate change. And that’s been such a long-standing disaster in the amount of time we’ve spent to think about this particular issue for which that analysis is completely inappropriate.

So, yes, there are places when it may be more appropriate, but maybe it’s not even very appropriate in those spheres. I would agree with Josh that this current moment and other moments of crisis – you mentioned 2008 – has opened up the space to think much more carefully about specific issues. And when you have a crisis that confronts you, it forces you to come up with a different economics or use other traditions of economics which have better answers than the ones that are there presently.

Q: As we live in an age of economics and economists – in which economic developments feature prominently in our lives and economists have major influence over a wide range of policy and people – should economists be held accountable for their advice?

JWM: As Arjun was saying earlier, this question is almost giving economics as a field too much credit, in the sense that it suggests that a lot of economic outcomes are directly dependent on the advice given by economists. Economics, as we’ve said, has an enormous prestige in terms of the presence of economists in all sorts of public debates. But a lot of times if you look at how views change, it’s not the economists who are leading the way. It’s the politicians or the broader public who’ve shifted. And then the economist come in to justify this after the fact.

There’s a certain sense, as a concrete example, where a lot of the development in macroeconomic theory over the past generation has been an after-the-fact effort to justify the policies that central banks were already following. Like a way of demonstrating that what central banks were already doing in terms of inflation target, using something like the Taylor Rule was the socially optimal thing. And that generalizes pretty widely.

So I’m not sure that we should be blaming or crediting economists for policy outcomes that they probably do more to legitimate or help with the execution of than to shift. The other reason I don’t personally see this as a particularly productive direction to go in is: who’s going to impose the accountability, who’s going to step in and say, all right, you were wrong and that had consequences and now you’re going to pay a penalty.

There’s no consensus position from which to do that. So we all just have to go on making our arguments the best we can and we’re not going to reach agreement. And so we try to shift the debate our way and somebody else shifts it their way, and there’s never going to be an impartial referee who’s going to come in and say that one side was right and the other was wrong.

AJ: Having been practicing economist for 10-15 years, broadly one has to realize that whatever you say and whatever you think and whatever you do, is strictly circumscribed by what the world is open to at that point of time. That’s something that’s sometimes hard for us to accept. There are many people who for years made the argument that we shouldn’t be so concerned about supply constraints, and it was only after 2012, 13, 14, 15- when the world started to move away from austerity or the costs of austerity became well known, that space was made for these arguments. And it’s always like that.

Spaces are there in some moments and not in other moments. And there are those people who for whatever reason in some universities, in some spaces, seem to capture elite opinion. They’re the ones who you see again and again and again. It doesn’t matter if they’re right or wrong, they’re the ones who are opinion makers.

I don’t think this is distinct from any other kind of marketing. There are always going to be a few people who are opinion and market leaders. Having said that, it would be good to have a list of when people were wrong. And sometimes it would be good to take people down a peg or two.

But again, I don’t think it’s an important thing. I don’t think that we should necessarily valorise economics and economists one way or the other.

6. Does economics explain Capitalism? How would you define Capitalism?

AJ: If you want to think about capitalism as a system, you need to go back to Karl Marx. You don’t have to call yourself a Marxist, but if you want to think about the questions like the ones that you just posed, you have to take him very seriously because his work is the foundation of many of the ways that we think about capitalism. Josh and I are working on a book and we take up this question about what capitalism means, and in our minds it has a clear definition. It has three elements, or three phases.

The first is the conversion of all kinds of human activities and their products into commodities, this thing that you buy and sell, this alienated thing that is sold in markets. That’s the first. The second is the endless accumulation of money as an end to itself. That’s the drive of the system, which seems to be out of human control. And then finally, something which is very critical and which gives it some of its emotional heft, there is the hierarchy in the workplace where people work under the authority of the boss.

All three of these elements are there historically. But their fusion in this incredibly changeable system that we’ve had for 200 years, that has been unique. That’s the central aspect that we want to focus on, the combination of these three things. And it’s the fact that when combined it gives you this dynamism, this ability to transform society, in far reaching ways that seem out of human control. That’s what I would say capitalism is.

JWM: I agree, that’s the correct definition of capitalism as a system. The problem comes when you try to pull out one of those elements in isolation and think that’s what defines the system. It’s the fusion of the three of them.

The other piece, which maybe isn’t quite as defining but historically has been very important, is that the process of endless accumulation has this moment in the middle of it where money is tied up, locked up in long-lived means of production, that you’re not just buying commodity, working it up and then selling it again, but you’ve got machines, you’ve got buildings, you’ve got technology.

So there’s this long gap between the outlay and the final sale. And that’s one of the things that has made this a system that is dynamic and has transformed human productive capacities in ways that we would agree with Marx’s judgment that in the long run, expand the space for human freedom and possibilities because it’s broken up the old, local, simple ways of carrying out productive activity and allowed people to have a much more extensive division of labor, much wider scale cooperation and the development of all of these new ways of transforming the world through technology that didn’t exist before or that were much – let’s not say it didn’t exist, but developed much more slowly in limited ways before.

But this is also where a lot of the conflict comes up, because you build up a business and it exists for its own purposes, it has its own norms, it has its own internal logic. And then at some point, you have to turn the products of that back into money to keep the accumulation process going. And so a lot of the tensions around the system come from that.

The other part of your question is, can economics explain capitalism. From our point of view economics is part of the larger set of social phenomena that grow out of the generalisation of capitalism as a way of organizing human life and productive activity. In that sense, you can’t use the tools of economics to explain capitalism, because economics is within capitalism. The categories of economics are specific to capitalism. If you want to explain the origins of it, you need a different set of tools. It’s a historical question rather than one that you can answer with the tools of economics.

Q: Is Capitalism, or whatever we should call the current system, the best one to serve the needs of humanity, or can we imagine another one?

JWM: We don’t have to imagine other systems, they’re all around us. As Arjun was saying earlier, we all of us experience every day systems where productive activity is organized through some collective decision making process. An enormous amount of our productive work, our reproductive labor that keeps us going individually and collectively, is carried out in the family. Some families are more egalitarian, some families are more hierarchical, but no family is organized on the basis of the pursuit of profit – well, let’s not say none, but a trivially small fraction of them are.

So we all have firsthand experience that this is a way that we can organize our activity. We all know that within the workplace you personally don’t make decisions based on some profit maximizing criteria. And your immediate boss isn’t doing it that way either. Probably they’re just following orders and some bureaucratic system, or perhaps there’s an element of voluntary cooperation going on.

But either way, it’s a different way of organizing our activity than the notion of markets and the pursuit of profit. As academics, we’re fortunate enough to have a collective decision making process that covers a lot of the traditional roles of the capitalist employer. We collectively decide on hiring and we collectively organize our work schedules and so on. 

Obviously, very few workers in the world are as fortunate as academics in that way. But the point is that this is a model that exists. It works. Certainly here in the United States, higher education is one of our big industrial success stories. And it’s organized as a bunch of little worker co-ops!

In any workplace, there are moments when people sit down to make a decision together, where people do stuff because that’s just what makes sense and what they’ve agreed to do, as opposed to somebody making a calculation of self-interest. This is what David Graeber in his wonderful book, Debt, talks about as “everyday communism.” Even in the most traditional workplace if somebody says pass me that hammer or can you do some little favor for me, people do it just as a way of cooperating and not because they’ve been ordered to or because they’re calculating that it will pay off for them.

And then we have a huge public sector in the world as well. We have public schools and public libraries and public transit and fire and police services and so on. So we already have an enormous amount of non-capitalist organization of production around us. We don’t have to imagine it.

The challenge intellectually is to generalize from this stuff, to recognize how these principles can be applied more broadly. We don’t have to create something new, but we do have to bring in general principles. For people on the left, or people who support individual public sector programs or individual non- capitalist ways of organizing particular activities, there’s often a tendency to make the argument in terms of that specific activity: well, here’s why we want public schools and we want better funding for our public schools. As opposed to trying to articulate what is the general principle that makes markets and the pursuit of profit a bad way to organize that. What is the general principle that says teachers should have autonomy?

We want less authority of the boss in the classroom. That’s why we have civil service protection, that’s why we have professions, because we want workers to have autonomy. But we need to be able to say why.

We want to move away from the model of proletarian labor where you’re completely under the authority of the boss. We do that in a lot of specific cases already. The intellectual challenge is to generalize that and see how we can apply it more broadly to the areas of society where it’s not not currently organized that way.

AJ: The question is nicely posed, because most people would broadly agree that capitalism generates a lot of good. But there’s been a sense right from the beginning that it may not be serving the needs of humanity. That the only word that describes this is a drive, an alien drive which sometimes intersects with the need of human beings and very often doesn’t.

When we think about  what happens in farms, for example, and how so many people spend their entire lives working as drones, it’s very tragic history.  Yes, people are richer and healthier as well. But capitalism, the way that it’s developed, has not served the needs of humanity. 

We don’t have to look historically. Let’s look at what’s happening right now with vaccination. The belief that you needed intellectual property and you can only solve this by the genius of a few pharmaceutical companies when in fact, what happened in all of this innovation was that it was the public sector backing all of this, which made certainly some of the vaccines even viable in the first place. And so now you have this perverse situation where some people are prevented from access because we want to maintain whatever capitalist institutions that we’ve built up.

So it’s important to realize that capitalism, while it’s done many great things as Marx and others recognized, it’s never been a force which has very nicely dovetailed with human needs. But that what’s useful now to think about is, as Josh said, we don’t need to imagine an alternative – we have a model and a system that’s already there, that we’re going to replace it with.

This thing will happen incrementally. Maybe this is radical optimism, but we both believe that the domain organized around these arbitrary hierarchies – the market and so on, is shrinking. Maybe in the next few generations with the challenge of climate change, with more crises and with a truly global world, the responses to those will mean that the domain of collective freedom will be much greater in the future than now.

And the domain of capitalism will be smaller. 

JWM:  I want to amplify something Arjun just said — the vaccine is a perfect example of this dynamic. On the one hand, we have a urgent collective problem, this pandemic. And the solution is directed by the public. It’s a collective decision mediated by governments to devote our common resources to solving this problem.

And it’s incredibly effective when you want to solve this problem and you have a political decision to do it. You can work wonders. And it’s carried out by scientists who have a whole set of professional norms around the conduct of science, which is precisely in order to suppress market incentives. We don’t want scientists thinking about how to get rich. Now, we do get that because that’s ubiquitous in our society, but the reason we have a whole set of professional norms around science is precisely because we think that this is the activity that people carry out better when they’re insulated from market incentives.

And then we have a centralized public direction to mobilize their activity. But the problem is that the fruits of that still have to be squeezed into this box of private property. Somebody has to have a property right over all this collective labor and public resources in the form of a patent.

And that then limits the value of this work. It makes the success much less than it could have been. We already are seeing that conflict and we’re going to continue seeing it even more so as we deal with problems like the pandemic and climate change and so on.

When we urgently need to solve a problem, we find we do it by suppressing the logic of the market and making decisions collectively. But then as long as we still have this overarching insistence on organizing our claims on each other in the form of property rights, it creates a conflict, it gets in the way of that. And over time, again, just the necessity of solving our urgent problems is going to force us to move away from the private property model and away from the pursuit of profit, and towards more rational collective ways of dealing with the problems that face us.

At Roosevelt: Reimagining Full Employment

Published by Anonymous (not verified) on Fri, 23/07/2021 - 12:41am in

Mike Konczal, Lauren Melodia and I have a new report out from the Roosevelt Institute, on what true full employment might look like in the United States.

This is part of a larger project of imagining what an economic boom would look like. As Mike and I argued in our recent New York Times op-ed, there’s a real possibility that the coming years could see a historic boom, thanks to the exceptionally strong stimulus measures of the past year and, hopefully, the further expansions of public spending on the way. (Interestingly, the term “boom” is now making it into Biden’s speeches on the economy.) If the administration, Congress and the Fed don’t lose their nerve and stay on the path they’re currently on, we could soon be seeing economic growth and rising wages in a way that we haven’t since at least the late 1990s.

This is going to call for a new way of thinking about economic policy. Over the past decade or more, the macroeconomic policy debate has been dominated by a consensus that is more concerned with the supposed dangers of public debt than stagnation, and sees any uptick in growth or wages as worryingly inflationary. Meanwhile, the left knows how to criticize austerity and bailouts for business, and to make the case for specific forms of public spending, but has a harder time articulating the benefits of sustained growth and tight labor markets.

What we’re trying to do is move away from the old, defensive fights about public debt and austerity and make the positive case for a bigger more active public sector. There’s no reason the Right should have a monopoly on promises faster growth and improvements in peoples material living standards. Post-covid, we’re looking at a new “morning in America” moment, and progressives should be prepared to take credit.

One of the great appeals of the Green New Deal framing on climate change is that it turns decarbonization from a question of austerity and sacrifice into a promise to improve people’s material well being, not decades from now but right now, and in ways that go well beyond climate itself. I think this promise is not just politically useful but factually well-founded, and could just as well be made for other expansions of the public sector.

This is an argument that I and others have been making for years. Of course, any promise of faster growth and higher living standards has to confront the argument, enshrined in macroeconomics textbooks, that the economy is already operating close to potential, at least most of the time — that the Federal Reserve has taken care of the demand problem. In that case, the Keynesian promise that more spending can call forth more production would no longer apply.

We’ve tended to respond to this argument negatively — that there is no evidence that the US now was facing any kind of absolute supply constraint or labor shortage before the pandemic, let alone now. This is fine as far as it goes, and I think our side of the debate has won some major victories — Jay Powell and Janet Yellen both now seem to agree that as of 2019 the US was still well short of full employement. Still, I think it’s legitimate for people to ask, “If this isn’t full employment, then what would be?” We need a positive answer of our own, and not just a negative criticism of the textbook view.

This new paper is an attempt to do just that — to construct an estimate of full employment that doesn’t build in the assumption that recent labor market performance was close to it. One way to do this is to compare the US to other advanced countries, many of which have higher employment-population ratios than the US, even after adjusting for age differences. We chose to take a different approach, one that instead looks at differences in employment rates within the US population.

From the executive summary:

This issue brief argues that potential employment in the US is much higher than we have seen in recent years. In addition to those officially counted in the labor force, there is a large latent labor force, consisting of people who are not currently seeking work but who could reasonably be expected to do so given sustained strong labor demand. This implies much more labor market slack than conventional measures of unemployment suggest.

An important but less familiar sign of labor market slack is the difference in employment rates between groups with more- and less-privileged positions in the labor market. Because less-favored groups—Black workers, women, those with less formal education, those just entering the labor market—are generally last hired and first fired, the gaps between more- and less-favored groups vary systematically over the business cycle. When labor markets are weak and employers can pick and choose among potential employees, the gap between employment rates for more- and less-favored groups widens. When labor markets are tight, and workers have more bargaining power, the gap shrinks.

We use this systematic relationship between overall labor market conditions and employment rates across race, gender, education, and age to construct a new measure of potential employment. In effect, since more-favored workers will be hired before less-favored ones, the difference in outcomes between these groups is a measure of how close hiring has gotten to the true back of the line.

We construct our measure in stages. We start with the fact that changes in employment rates within a given age group cannot reflect the effect of population aging. Simply basing potential employment by age groups on employment rates that have been observed historically implies potential employment 1.7 points higher than the CBO estimates.

Next, we close the employment gaps by race and gender, on the assumption that women and Black Americans are no less able or willing to work than white men of a similar age. (When adjusting for gender, we make an allowance for lower employment rates among parents of young children). This raises potential employment by another 6.2 points.

Finally, reducing the employment gap between more- and less-educated workers in line with the lower gaps that have been observed historically adds another 1.8 points to the potential employment rate.

In total, these adjustments yield a potential employment-population ratio 10 points higher than the CBO estimates, equivalent to the addition of about 28 million more jobs over the next decade.

Adding these 28 million additional jobs over the next decade would require an average annual growth in employment of 2.1 percent. The employment growth that would fully mobilize the latent labor force, as estimated here, is in line with the rate of GDP growth required to repair the damage from the Great Recession of 2007–2009 and return GDP to its pre–2007 trend.

You can read the rest here.

What do two million accounts tell us about the impact of Covid-19 on small businesses?

Published by Anonymous (not verified) on Fri, 16/07/2021 - 6:00pm in

James Hurley, Sudipto Karmakar, Elena Markoska, Eryk Walczak and Danny Walker

Compass on old map

This post is the second of a series of posts about the Covid-19 pandemic and its impact on business activity.

Covid-19 led to a sharp reduction in economic activity in the UK. As the shock was playing out, small and medium-sized businesses (SMEs) were expected to be more exposed than larger businesses. But until now, we have not had the data to analyse the impact on SMEs. In a recent Staff Working Paper we use a new data set containing monthly information on the current accounts of two million UK SMEs. We show that the average SME saw a very large drop in turnover growth and that the crisis played out very differently for different types of SMEs. The youngest SMEs in consumer-facing sectors in Scotland and London were hit hardest.

SMEs are important and have been hit hard by Covid-19

There are 6 million SMEs in the UK, which account for around half of UK economic activity and more than half of employment. They rely heavily on the banking system for their financing and UK banks are heavily exposed to them. Given that SMEs are more likely to operate in in sectors like hospitality and arts and recreation, Covid-19 has hit them particularly hard. They have also been the main beneficiaries of the biggest government support schemes, such as the Coronavirus Job Retention Scheme (CJRS) and the Bounce Back Loan Scheme (BBLS).

But there have been limited data on SME performance through the crisis

Beyond national accounts aggregates and small-scale surveys, data on how SMEs have performed through the Covid-19 crisis have been sparse. The academic literature has used survey data with small sample sizes, private sector data with larger sample sizes but low representativeness or banking sector data that explicitly excludes small businesses. The ONS introduced a timely business survey in the UK but responses are skewed towards larger businesses.

We use a new data set covering 2 million SME bank accounts

To help to address these limitations, we use a novel data set with detailed monthly information on all UK SME accounts held with nine major banking groups, covering current accounts and bank debt of various forms. The data are provided confidentially to the Bank of England via Experian, a private sector information services company, on a monthly basis. The new data set covers almost two million businesses, which gives us near universal coverage of limited company SMEs in the UK. It contains monthly information on account balances and total transactions. We are not aware of any comparable timely information on SME performance in the UK, even on an aggregate basis, although there are some experimental statistics in the US.

We analyse a measure of turnover proxied by total current account inflows, and costs proxied by total outflows. To strip out seasonality we compute a year on year growth rate that takes into account entry and exit. To build confidence in the data, we have run some simple comparisons to growth in GDP and aggregate corporate profits. As shown in Figure 1, the new data tracks macroeconomic aggregates relatively closely, although there are definitional differences and we would expect SMEs to perform differently to larger companies even in normal times.

Figure 1: Comparison of SME turnover growth with aggregate data

We use the new data to document a few simple facts on the impact of the Covid-19 crisis on UK SMEs.

The average UK SME saw a 30 percentage point fall in turnover growth after the pandemic took hold in 2020

Figure 2 presents coefficient estimates and 99% confidence intervals from regressions of year on year turnover and cost growth on a full set of month dummies for all months in 2020, controlling for SME fixed effects. It shows there was a large fall in turnover growth for the average SME from April 2020 onward, relative to the period before 2020, when growth was around zero on average. The average SME had not returned to its January 2020 growth rate by December 2020. Over the period as a whole, turnover growth and cost growth moved roughly in line with one another, implying that the average SME managed to offset the large reduction in turnover by reducing outgoings to maintain cash flows.

Figure 2: Estimated impact of Covid-19 on growth by month in 2020

SMEs in the sectors most exposed to social distancing saw turnover fall by much more than others

Figure 3 shows how the average impact varied across SMEs that operate in different sectors of the economy, based on interaction terms that we included in the regressions. The average SME in the Arts and Recreation sector saw more than a 40 percentage point reduction in turnover growth year on year, compared to only around a 10 percentage point reduction for the average SME in the Agriculture sector. We also created a dummy variable that identifies SMEs that operate in ‘social sectors’, which captures those that are most exposed to social distancing. SMEs in those sectors saw lower turnover growth and relatively smaller reductions in costs than SMEs that were less exposed to Covid-19.

Figure 3: Estimated impact of Covid-19 on growth by sector

There were some regional differences in the scale of the crisis, although much less than for sectors

At one end of the spectrum, the average SME in Scotland or London faced around a 35 percentage point reduction in turnover growth over the April 2020 to December 2020 period compared to the period before the shock. At the other end, the average SME in Northern Ireland faced around a 25 percentage point reduction. There is also some evidence that the turnover shock was slightly more severe in the most affluent parts of the country, where incomes and employment levels are highest, although this effect appears to be small. Data for the US shows a similar picture. This could be because higher income people cut their spending by more than lower income people.

Figure 4: Estimated impact of Covid-19 on growth by region

Younger SMEs saw bigger reductions in turnover growth than older SMEs but they managed to reduce their costs by more

The youngest SMEs in the bottom decile of the age distribution faced around a 45 percentage point reduction in turnover growth relative to the period before the shock. The SMEs in the top decile of the distribution, which are more than 17 years old on average, faced only a 20 percentage point reduction in turnover growth. The gap between the turnover and costs dots on the chart – which can be interpreted as the cash flow impact for the average SME in the category – shows that the youngest SMEs, which are under 2 years old, appear to have had the smallest cash flow hits in relative terms.

Figure 5: Estimated impact of Covid-19 on growth by firm age decile

The very smallest SMEs had slightly less severe reductions in turnover growth

We have used historical data on average annual turnover for each SME to identify smaller and larger SMEs. The smallest SMEs, with annual turnover of less than £100,000, appear to have had the smallest impact on turnover growth during the Covid shock. The largest SMEs have had the largest negative impact, although there are relatively few of these in our data set. The second most severely affected group of SMEs are those in the £100,000 to £1 million turnover bracket.

SMEs with around average turnover hits from Covid were most likely to take out government-guaranteed BBLS loans, along with those in the North and in consumer-facing sectors

We know from aggregate data that around one in four SMEs raised finance through the BBLS government-guaranteed loan scheme, which offered low interest loans of up to £50,000 to all businesses in the UK. In the new data set we can identify borrowing under the scheme, which allows us to analyse the performance of the SMEs that took out government-guaranteed loans. This reveals an inversed U-shape, where the firms in the bottom and top deciles of the growth distribution in 2020 were the least likely to take out BBLS loans. The sectors which were most directly impacted by the lockdowns, namely hospitality and transport, as well as those in the retail sector, were the most likely to have used BBLS. The smallest SMEs, as measured based on their 2019 turnover, were least likely to use the scheme, closely followed by very large SMEs. Firms in the North were the most likely to have taken a BBLS loan, whereas those in Northern Ireland were the least likely. We also found a linear effect of age, with the oldest companies least likely to use the BBLS.

Figure 6: Estimated relationship between turnover growth in 2020 and probability of taking out a BBLS loan

The data we have introduced in this post should be useful for further monitoring and research as we emerge from the Covid-19 crisis

The Covid-19 crisis significantly reduced turnover for the average SME in the UK and there was material heterogeneity across SMEs, with younger SMEs in consumer-facing sectors hardest hit. But cash flows did not decline on average and many SMEs managed to raise funds through the BBLS scheme to help to offset the impacts of the crisis. The data we use in this post will be useful for further monitoring and research on the performance of UK SMEs as we emerge from the Covid-19 crisis.

Appendix: Regression equation used to produce the charts

\begin{equation} turnovergrowth_{i,t} = \beta postMarch_{i,t}*\mathbf{Z_{i,t-1}} + v_{i} + \epsilon_{i,t} \end{equation}

Where i represents an individual firm, t is a month, postMarch is a dummy that takes a value of 1 for months after March 2020, Z is a set of firm characteristics (eg sector dummies, region dummies) and v are firm fixed effects.

James Hurley and Danny Walker work in the Bank’s Macro-financial Risks Division, Sudipto Karmakar works in the Bank’s Stress Testing Strategy Division and Elena Markoska and Eryk Walczak work in the Bank’s Advanced Analytics Division.

If you want to get in touch, please email us at or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Unemployment risk, liquidity traps and monetary policy

Published by Anonymous (not verified) on Tue, 06/07/2021 - 6:00pm in

Dario Bonciani and Joonseok Oh

The Global Financial Crisis in 2008 caused a significant and persistent increase in unemployment rates across major advanced economies. The worsening in labour market conditions increased uncertainty about job prospects, which potentially gave rise to precautionary savings, putting further downward pressure on real economic activity and prices. Moreover, in response to the severe drop in demand, central banks worldwide cut short-term nominal interest rates, which rapidly approached the zero lower bound (ZLB), where they remained for a prolonged time. In a recent paper, we show that committing to keep the interest rate at zero longer than implied by current macroeconomic conditions is particularly effective at easing contractions in demand in the presence of countercyclical unemployment risk and low interest rates.

A model with uninsurable unemployment risk

We study optimal monetary policy conduct through the lens of a Heterogeneous Agents New Keynesian (HANK) model with frictions in the labour market, imperfect unemployment insurance, and an occasionally binding ZLB constraint (ie the interest rate may hit the ZLB during a downturn). The model features two types of households: workers and firm owners, though we abstract from the distributional effects of monetary policy in the paper. Workers face the risk of unemployment and a lower income. The presence of idiosyncratic unemployment risk (the possibility of becoming unemployed, which rises in a downturn) leads to a precautionary savings motive for employed workers. Firm owners, instead, do not face any unemployment risk.

We study the impact of monetary policy in response to a negative demand shock that leads the economy into a liquidity trap. We first analyse the economic outcomes when the central bank only responds to current inflation (strict-inflation targeting), comparing the cases with perfect and imperfect unemployment insurance. Given this benchmark, we then study how the economy responds when the central bank follows the optimal monetary policy and can credibly commit to keeping the interest rate ‘lower for longer’ (often referred to as Odyssean forward guidance).

Finally, we study whether simple policy rules can provide results in line with those under optimal monetary policy. In particular, we consider: (i) a Taylor rule augmented with the lagged value of the shadow policy rate (inertial policy rule); (ii) a Price-Level-Targeting (PLT) rule; (iii) an Average-Inflation-Targeting (AIT) rule.

What we find

Under strict-inflation-targeting, the adverse demand shock has significantly stronger effects under imperfect unemployment insurance (ie when unemployed workers are only partially compensated for their income loss). This is because the fall in demand reduces job creation and raises unemployment risk, which induces households to increase their savings for precautionary reasons. The precautionary-savings effect leads to a stronger fall in inflation and inflation expectations. Since the nominal interest rate is stuck at zero (and there are no other monetary tools in our model), the real interest rate rises, putting further downward pressure on consumption and output.

Under the optimal policy, instead, the central bank responds to the contraction in demand by committing to hold the policy rate at zero longer than implied by current economic conditions. This policy has the effect of increasing inflation expectations and reducing the real interest rate. With the interest rate being kept ‘lower for longer’, agents expect improvements in labour market conditions, which reduces their precautionary saving behaviour in the presence of imperfect unemployment insurance. As a result, market incompleteness (ie imperfect insurance) amplifies the rise in inflation expectations and the reduction in the real interest rate, thereby mitigating the decline in real activity. Specifically, when the central bank sets an optimal path for the policy rate, an adverse demand shock causes smaller contractions in real economic activity under incomplete markets than under perfect unemployment insurance.

Under the three simple rules, there is history dependence in the nominal policy rate: a fall in inflation today leads the policy rate to stay at zero for longer than current conditions alone would imply.  As a result, all three rules are particularly effective under imperfect insurance. However, unlike the optimal-policy case, these rules do not fully neutralise the fall in inflation expectations caused by the rise in unemployment risk and precautionary savings.

Policy implications

The paper shows that, if the central bank can commit to holding interest rates lower for longer, then such a policy can be particularly effective in the presence of precautionary savings due to higher uninsurable unemployment risk. Within our model, optimal monetary policy can completely offset the deflationary spirals arising from an increase in precautionary savings. Under simpler and more realistic policy rules, the central bank is still able to significantly mitigate the fall in demand. We conclude that, in practice, monetary policy and unemployment insurance policies are necessary tools to stabilise output in response to demand contractions at the ZLB. By reducing the fall in income associated with unemployment, such insurance policies reduce the precautionary savings motive, which in turn reduces the amplification of negative shocks and risk of being stuck in a liquidity trap.

Dario Bonciani works in the Bank’s Monetary Policy Outlook Division and Joonseok Oh works at Freie Universität Berlin.

If you want to get in touch, please email us at or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

What types of businesses have used government support during the Covid-19 pandemic?

Published by Anonymous (not verified) on Mon, 05/07/2021 - 6:00pm in

Will Banks, Sudipto Karmakar and Danny Walker

This post is the first of a series of posts about the Covid-19 pandemic and its impact on business activity.

During the pandemic, UK businesses have received unprecedented levels of government support, set to total 9% of GDP. This has mainly been through the Coronavirus Job Retention Scheme (CJRS), under which 1 in 3 employees have been furloughed, and the government-guaranteed loan schemes that were used by 1 in 4 businesses. Despite the scale of this support, little has been said about which businesses received it. In this post we combine data on loan scheme and CJRS usage with a data set on the characteristics of businesses. We find that small, relatively old and sophisticated, labour-intensive businesses in the sectors most vulnerable to the impacts of the pandemic are most likely to have received both types of support.

What do we already know about the schemes?

HMRC has already published statistics on the CJRS and the British Business Bank has published statistics on the loan schemes. These statistics show that in absolute terms, businesses in the accommodation and food sector have been the biggest users of the CJRS. Construction businesses have been the biggest users of the Bounce Back Loan Scheme. Data also shows that the West Midlands has the most furloughed workers and London the most users of the BBLS. But this does not tell us how these interact. Our previous work explores what types of businesses have borrowed under the loan schemes, but some questions around furlough and the interaction between schemes remain unanswered. Has the West Midlands furloughed more workers because they have more accommodation and food businesses? What type of businesses have used both of the schemes?

What can we learn from business-level data?

We have collected the most up-to-date data on the individual businesses that have used the CJRS and the government loan schemes. From this, we find that around 45% of businesses which have used CJRS have also taken out a government loan, and 41% of loan-scheme users furloughed at least one employee from December 2020 to February 2021 (Figure 1). We match this scheme data to a large public data set with information on all limited companies in the UK, based on names and unique identifiers. We then run a series of probit regressions to isolate the effects of sector, region, leverage, age, firm size and capital intensity on a business’ probability of having used the CJRS and the loan schemes, following a similar methodology to this Staff Working Paper. We analyse the marginal effects of each business characteristic controlling for all of the others: giving us the estimated impact of a given characteristic on the probability of using the schemes.

Figure 1: Proportion of businesses using government support measures

What type of business is most likely to have used the support schemes?

A lot of the results of our regressions are intuitive. HMRC stats show that businesses in the accommodation and food sector are most likely to have used the CJRS, and this remains the case when controlling for other factors. That sector was hardest hit at the height of the pandemic. In fact, an accommodation and food business is almost 20 percentage points more likely to have furloughed employees than a manufacturing business (see Figure 2). There is similar sectoral variation in the businesses that have used both schemes. And interestingly, a business that has used the loan schemes is 20 percentage points more likely to have also furloughed employees.

Figure 2: Variation in scheme usage by sector

Reports have shown that there is limited regional variation in the use of the CJRS and the loan schemes, and we find that this holds even if you control for other characteristics. A business in the North East – the region with the highest usage once we control for other factors – is less than 5 percentage points more likely to have used CJRS than a similar business in Scotland, where usage is lowest.

A benefit of the data set we have used is that it provides details on business characteristics that haven’t been examined before in relation to CJRS: their assets, age, leverage and existing bank relationships are all new variables we’ve explored. For example, micro businesses with <£100,000 in assets are most likely to have used the schemes, while very large businesses the least likely (see Figure 3).

Figure 3: Variation in scheme usage by total assets

The balance sheet data that small businesses report publicly is limited, but we have used it to create a proxy measure of how ‘labour or capital intensive’ a business is. We do this based on their assets per employee – a high ratio indicates high capital intensity. The result here is striking (see Figure 4). Businesses in the lowest decile of assets per employee – ie highly labour-intensive businesses – are almost 20 percentage points more likely to have used the CJRS than the average firm, and almost 15 percentage points more likely to have used both CJRS and a government loan.

Figure 4: Variation in scheme usage by capital intensity

The final set of variables we examine relate to how well established businesses are, and their access to finance. We know that even before Covid, a large share of SMEs did not use any external finance. We find that the youngest (1st) quintile of companies are least likely to have used the schemes: compared to them, firms in the 4th age quintile are around 8 percentage points more likely to have used furlough, and 6 percentage points more likely to have used both schemes. Similarly, a firm which has an established bank relationship is 6 percentage points more likely to have used furlough and a government-guaranteed loan scheme than a firm which had not previously accessed bank finance.

What does this mean for the economy?

The furlough scheme is set to end in September 2021, and has cushioned the impact of changes in GDP on the UK labour market. The original government-guaranteed loan schemes closed in March 2021, and resulted in a tightening of credit conditions for the most vulnerable small and medium businesses. Our findings suggest that both types of support have been most likely to help small, relatively old and sophisticated, labour-intensive businesses in the sectors most vulnerable to the impacts of social distancing. We expect many of these businesses to have stopped using furlough since February 2021, and UK businesses have stronger cash positions than a year ago. But it’s possible that some of these businesses will continue to struggle from changes in demand patterns and the ongoing economic effects of Covid-19.

Will Banks works in the Banks Resilience Division, Sudipto Karmakar works in the Stress Testing Strategy Division and Danny Walker works in the Macro-financial Risks Division.

If you want to get in touch, please email us at or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Covid-19 briefing: working from home and worker productivity

Published by Anonymous (not verified) on Fri, 02/07/2021 - 6:00pm in

John Lewis, Andrea Šiško and Misa Tanaka

The Covid pandemic has led to a large enforced shift towards working from home (WFH) as a result of ‘stay-at-home’ policies in many countries. This led to a resurgence in interest in, and new reignited discussion about, the consequences of greater WFH. In this briefing we review the literature on the impact of WFH on productivity. Across a very diverse literature the key lessons are: impacts depend on the nature of tasks, the share of WFH matters, and there is big difference between enforced versus voluntary WFH. And the caveats are important too: cost savings at the firm level don’t automatically translate into economy-wide productivity gains and evidence on long-run effects remains very scarce.

The literature on the topic is large and varied. In this post our focus is on the effect on productivity. We therefore abstract from broader issues such as wellbeing, management style, labour markets, spatial implications, sectoral shifts and other potential impacts.

Effects of WFH on productivity differ significantly across tasks and roles

Perhaps the most well known paper in the literature is Bloom et al (2015). This conducts an experiment to study the impact of WFH on the performance of Chinese call centre workers responsible for airfare and hotel bookings. Workers who volunteered to WFH were randomly assigned to WFH or work in office (WIO), to safeguard against sample selection bias effects. Those assigned to WFH had a 13% performance increase relative to those who were assigned to WIO. Some of this increase is attributed to taking fewer breaks and sick days, and some to quieter working environment which enabled workers to take more calls per minute.

But other studies find that physical separation of workers can reduce productivity for other types tasks, eg when teams need to work together to resolve urgent and complex tasks. Battiston et al (2017) exploit a natural experiment involving 999 call handlers and radio operators in the United Kingdom. They find that performance – measured by the time taken between the creation of the incident by the call handler and the allocation of police officers by the radio operator – is 2% better when teammates are in the same room, and attribute this gain to the benefits of face-to-face communication.

Golden and Gajendran (2019) also find evidence that the impact of WFH on productivity depends on the role. They use matched survey data of employees who WFH voluntarily and their supervisors in an organisation. Overall, the authors find a positive relationship between WFH and job performance. But there was a stronger positive association between performance and the extent of WFH in roles with greater job complexity and less interdependence.

The relationship between WFH and workplace productivity may be non-linear

Using five case studies in two large telecommunication firms, Coenen and Kok (2014) find that WFH improves the speed and quality of new product development, provided that face-to-face contact, which is more likely to lead to trust building and high-quality knowledge sharing, is not completely replaced by virtual contact. Kazekami (2020) studies the influence of WFH on worker productivity in Japan using survey data in 2017 and 2018 and finds that the relationship is hump-shaped: some degree of WFH boosts productivity, but when WFH hours are too long, productivity falls. A meta-analysis of 46 psychological studies by Gajendran and Harrison (2007) also suggests that more than 2.5 days/week of WFH could harm relationships with co-workers. To assess the long-run impact of WFH on GDP, Behrens et al (2021) build a general equilibrium model. They find that 1–2 days (20%–40%) of WFH would maximise the long-run GDP given the current level of ICT development, as higher levels of WFH would reduce productivity.

Home environment relative to office environment is a key determinant of the productivity effects of WFH. Homes are not primarily designed for work and their suitability differs across workers depending on their financial means and living situation. ILO (2020) notes challenges both in setting up an appropriate home-working conditions and in monitoring and controlling compliance with occupational health norms. PWC (2020) points to lack of physical space, privacy and inadequate technology as potential challenges, which may be greater for younger workers (who are more likely to live in smaller dwellings and in households with more working adults) and those who have children at home. Evidence during Covid lockdown also suggests that working while having children at home is productivity reducing, particularly for mothers. Andrew et al (2020) conduct a survey of UK two-parent households with children aged 4–15 during the school closure period of April–May 2020, and find that mothers were doing only a third of uninterrupted paid-work hours of fathers on average. 

But some studies have also shown that certain types of office designs can reduce productivity and collaboration. Smith-Jackson and Klein (2009) study the effect of background noise on task completion rates and find negative consequences of noisier open spaces on performance. Bernstein and Turban (2018) conduct a study of bilateral interactions measured by sociometric devices and find that more openness in offices lowers face-to-face interactions by 70%, as employees sought more privacy. Interestingly, Coenen and Kok (2014), discussed earlier, find that the incidence of WFH increased after hot desking was introduced. A survey-based study by Oxford Economics (2016) points to ability to focus without interruptions, device connectivity, having collaborative areas, and giving employees their own space as the most important features for office design to enhance productivity.

Choice matters

The Bloom et al (2013) study discussed earlier found that about 50% of the initial volunteers changed their minds and decided to work in the office after the end of the experiment, citing social reasons and that it was troublesome for other household members. Around 10% of the initial non-volunteered group opted to work remotely because they saw the success of their peers who worked from home.

Palumbo (2020) and Oakman et al (2020) tended to find that freedom to choose where to work and the perception of autonomy are important factors in explaining the positive outcomes from remote working. But enforced WFH, driven by the desire to cut office space, could lead to a different outcome. For example, Bloom (2020) notes that his oft-cited study drew on a case where employees volunteered to WFH and were only allowed to if they had an office at home where no one else was permitted to enter. He describes enforced WFH in unsuitable spaces as a ‘productivity disaster for firms’.

Broader considerations

Studies on WFH tend to focus on narrower, short-term outcomes rather than longer-term effects. Existing empirical and experimental studies typically look at a narrow, well-defined set of tasks and tightly specified productivity metrics. That literature is relatively silent on longer-term effects and effects that are difficult to measure, such as innovation, employee retention, integration of new colleagues, and team cohesion. 

In addition, current WFH experiences may be affected by the stock of social capital built up from earlier office-working experiences in terms of networks, institutional knowledge and trust building (Haldane (2020)). If shifts to greater WFH affect social capital build-up, the effects may be different to current experiences. Equally, business models and IT may not (yet) have adapted to a world with greater WFH.

It is also important to note that making workers WFH can result in cost-saving by individual firms, but not necessarily increase productivity in the aggregate economy. Measured productivity relates aggregate output to capital and labour inputs. Greater WFH could enable firms to save costs on intermediate inputs and land (office space, heating, lighting etc). But if this simply shifts costs from employers onto workers whilst keeping the underlying ‘production function’ in terms of labour and capital inputs unchanged, it will have no direct effect on productivity. The individual firms’ decision to shift their workers to WFH could result in aggregate productivity gains only if workers can be more productive at home rather than in office, or if companies use WFH to cut office space without damaging their own productivity and the ‘freed-up’ space is then used by others for alternative productive purposes. 

Closing thoughts

A recent survey by Taneja et al (2021) suggests that nearly 80% of UK workers would prefer to work from home at least a few days a week post Covid. It remains to be seen whether the sudden and rapid rise in WFH leads to more permanent changes in working patterns and styles. The diverse literature on WFH has only grown since the pandemic started and debate is likely to persist. The key messages from the work so far suggests heterogeneity in impacts with respect to tasks and workers, the relative quality of home versus office environments matters and that the enforced versus choice dimension is important. But given the rich environment for future studies provided by Covid, and the heightened interest in the topic over the past 12 months, the existing body of literature is likely to be only the start of a much longer and broader-ranging discussion.

John Lewis, Andrea Šiško and Misa Tanaka work in the Bank’s Research Hub.

If you want to get in touch, please email us at or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

A new macroeconomics?

Published by Anonymous (not verified) on Fri, 02/07/2021 - 1:57pm in

UPDATE: The video of this panel is here.

[On Friday, July 2, I am taking part in a panel organized by Economics for Inclusive Prosperity on “A new macroeconomics?” This is my contribution.]

Jón Steinsson wrote up some thoughts about the current state of macroeconomics. He begins:

There is a narrative within our field that macroeconomics has lost its way. While I have some sympathy with this narrative, I think it is a better description of the field 10 years ago than of the field today. Today, macroeconomics is in the process of regaining its footing. Because of this, in my view, the state of macroeconomics is actually better than it has been for quite some time.

I can’t help but be reminded of Olivier Blanchard’s 2008 article on the state of macroeconomics, which opened with a flat assertion that “the state of macro is good.” I am not convinced today’s positive assessment is going to hold up better than that one. 

Where I do agree with Jón is that empirical work in macro is in better shape than theory. But I think theory is in much worse shape than he thinks. The problem is not some particular assumptions. It is the fundamental approach.

We need to be brutally honest: What is taught in today’s graduate programs as macroeconomics is entirely useless for the kinds of questions we are interested in. 

I have in front of me the macro comp from a well-regarded mainstream economics PhD program. The comp starts with the familiar Euler equation with a representative agent maximizing their utility from consumption over an infinite future. Then we introduce various complications — instead of a single good we have a final and intermediate good, we allow firms to have some market power, we introduce random variation in the production technology or markup. The problem at each stage is to find what is the optimal path chosen by the representative household under the new set of constraints.

This is what macroeconomics education looks like in 2021. I submit that it provides no preparation whatsoever for thinking about the substantive questions we are interested in. It’s not that this or that assumption is unrealistic. It is that there is no point of contact between the world of these models and the real economies that we live in.

I don’t think that anyone in this conversation reasons this way when they are thinking about real economic questions. If you are asked how serious inflation is likely to be over the next year, or how much of a constraint public debt is on public spending, or how income distribution is likely to change based on labor market conditions, you will not base your answer on some kind of vaguely analogous questions about a world of rational households optimizing the tradeoff between labor and consumption over an infinite future. You will answer it based on your concrete institutional and historical knowledge of the world we live in today. 

To be sure, once you have come up with a plausible answer to a real world question, you can go back and construct a microfounded model that supports it. But so what? Yes, with some ingenuity you can get a plausible Keynesian multiplier out of a microfounded model. But in terms of what we actually know about real economies, we don’t learn anything from the exercise that the simple Keynesian multiplier didn’t already tell us.

The heterogenous agent models that Jón talks about are to me symptoms of the problem, not signs of progress. You start with a fact about the world that we already knew, that consumption spending is sensitive to current income. Then you backfill a set of microfoundations that lead to that conclusion. The model doesn’t add anything, it just gets you back to your starting point, with a lot of time and effort that you could have been using elsewhere. Why not just start from the existence of a marginal propensity to consume well above zero, and go forward from there?

Then on the other hand, think about what is not included in macroeconomics education at the graduate level. Nothing about national accounting. Nothing about about policy. Nothing about history. Nothing about the concrete institutions that structure real labor and product markets. 

My personal view is that we need to roll back the clock at least 40 years, and throw out the whole existing macroeconomics curriculum. It’s not going to happen tomorrow, of course. But if we want a macroeconomics that can contribute to public debates, that should be what we’re aiming for.

What should we be doing instead? There is no fully-fledged alternative to the mainstream, no heterodox theory that is ready to step in to replace the existing macro curriculum. Still, we don’t have to start from scratch. There are fragments, or building blocks, of a more scientific macroeconomics scattered around. We can find promising approaches in work from earlier generations, work in the margins of the profession, and work being done by people outside of economics, in the policy world, in finance, in other social sciences.  

This work, it seems to me, shares a number of characteristics.

First, it is in close contact with broader public debates. Macroeconomics exists not to study “the economy” in the abstract — there isn’t any such thing — but to help us address concrete problems with the economies that we live in. The questions of what topics are important, what assumptions are reasonable, what considerations are relevant, can only be answered from a perspective outside of theory itself. A useful macroeconomic theory cannot be an axiomatic system developed from first principles. It needs to start with the conversations among policymakers, business people, journalists, and so on, and then generalize and systematize them. 

A corollary of this is that we are looking not for a general model of the economy, but a lot of specialized models for particular questions. 

Second, it has national accounting at its center. Physical scientists spend an enormous amount of time refining and mastering their data collection tools. For macroeconomics, that means the national accounts, along with other sources of macro data. A major part of graduate education in economics should be gaining a deep understanding of existing accounting and data collection practices. If models are going to be relevant for policy or empirical work, they need to be built around the categories of macro data. One of the great vices of today’s macroeconomics is to treat a variable in a model as equivalent to a similarly-named item in the national accounts, even when they are defined quite differently.

Third, this work is fundamentally aggregative. The questions that macroeconomics asks involve aggregate variables like output, inflation, the wage share, the trade balance, etc. No matter how it is derived, the operational content of the theory is a set of causal relationships between these aggregate variables. You can certainly shed light on relationships between aggregates using micro data. But the questions we are asking always need to be posed in terms of observable aggregates. The disdain for “reduced form” models is something we have to rid ourselves of. 

Fourth, it is historical. There are few if any general laws for how “an economy” operates; what there are, are patterns that are more or less consistent over a certain span of time and space. Macroeconomics is also historical in a second sense: It deals with developments that unfold in historical time. (This, among other reasons, is why the intertemporal approach is fundamentally unsuitable.) We need fewer models of “the” business cycle, and more narrative descriptions of individual cycles. This requires a sort of figure-ground reversal in our thinking — instead of seeing concrete developments as case studies or tests of models, we need to see models as embedded in concrete stories. 

Fifth, it is monetary. The economies we live in are organized around money commitments and money flows, and most of the variables we are interested in are defined and measured in terms of money. These facts are not incidental. A model of a hypothetical non-monetary economy is not going to generate reliable intuitions about real economies. Of course it is sometimes useful to adjust money values for inflation, but it’s a bad habit to refer to the result quantities as “real” — it suggests that there is some objective quantity lying behind the monetary one, which is in no way the case.

In my ideal world, a macroeconomics education would proceed like this. First, here are the problems the external world is posing to us — the economic questions being asked by historians, policy makers, the business press. Second, here is the observable data relevant to those questions, here’s how the variables are defined and measured. Third, here are how those observables have evolved in some important historical cases. Fourth, here are some general patterns that seem to hold over a certain range  — and just as important, here is the range where they don’t. Finally, here are some stories that might explain those patterns, that are plausible given what we know about how economic activity is organized.

Well, that’s my vision. Does it have anything to do with a plausible future of macroeconomics?

I certainly don’t expect established macroeconomists to throw out the work they’ve been doing their whole careers. Among younger economists, at least those whose interest in the economy is not strictly professional, I do think there is a fairly widespread recognition that macroeconomic theory is at an intellectual dead end. But the response is usually to do basically atheoretical empirical work, or go into a different field, like labor, where the constraints on theory are not so rigid. Then there is the heterodox community, which I come out of. I think there has been a great deal of interesting and valuable work within heterodox economics, and I’m glad to be associated with it. But as a project to change the views of the rest of the economics profession, it is clearly a failure.

As far as I can see, orthodox macroeconomic theory is basically unchallenged on its home ground. Nonetheless, I am moderately hopeful for the future, for two reasons. 

First, academic macroeconomics has lost much of its hold on public debate. I have a fair amount of contact with policymakers, and in my experience, there is much less deference to mainstream economic theory than there used to be, and much more interest in alternative approaches. Strong deductive claims about the relationships between employment, inflation, wage growth, etc. are no longer taken seriously.

To be sure, there was always a gulf between macroeconomic theory and practical policymaking. But at one time, this could be papered over by a kind of folk wisdom — low unemployment leads to inflation, public deficits lead to higher interest rates, etc. — that both sides could accept. Under the pressure of the extraordinary developments of the past dozen years, the policy conversation has largely abandoned this folk wisdom — which, from my point of view, is real progress. At some point, I think, academic economics will recognize that it has lost contact with the policy conversation, and make a jump to catch up. 

Keynes got a lot of things right, but one thing I think he got wrong was that “practical men are slaves to some defunct economist.” The relationship is more often the other way round. When practical people come to think about economy in new ways, economic theory eventually follows.

I think this is often true even of people who in their day job do theory in the approved style. They don’t think in terms of their models when they are answering real world questions. And this in turn makes our problem easier. We don’t need to create a new body of macroeconomic theory out of whole cloth. We just need to take the implicit models that we already use in conversations like this one, and bring them into scholarship. 

That brings me to my second reason for optimism. Once people realize you don’t have to have microfoundations, that you don’t need to base your models on optimization by anyone, I think they will find that profoundly liberating. If you are wondering about, say, the effect of corporate taxation on productivity growth, there is absolutely no reason you need to model the labor supply decision of the representative household as some kind of intertemporal optimization. You can just, not do that. Whatever the story you’re telling, a simple aggregate relationship will capture it. 

The microfounded approach is not helping people answer the questions they’re interested in. It’s just a hoop they have to jump through if they want other people in the profession to take their work seriously. As Jón suggests, a lot of what people see as essential in theory, is really just sociological conventions within the discipline. These sorts of professional norms can be powerful, but they are also brittle. The strongest prop of the current orthodoxy is that it is the orthodoxy. Once people realize they don’t have to do theory this way, it’s going to open up enormous space for asking substantive questions about the real world. 

I think that once that dam breaks, it is going to sweep away most of what is now taught as macroeconomics. I hope that we’ll see something quite different in its place.  

Once we stop chasing the will-o-wisp of general equilibrium, we can focus on developing a toolkit of models addressed to particular questions. I hope in the years ahead we’ll see a more modest but useful body of theory, one that is oriented to the concrete questions that motivate public debates; that embeds its formal models in a historical narrative; that starts from the economy as we observe it, rather than a set of abstract first principles; that dispenses with utility and other unobservables; and that is ready to learn from historians and other social scientists.

Population growth, and the which way is up problem, in economics

Published by Anonymous (not verified) on Thu, 01/07/2021 - 1:20pm in

Population growth, and the which way is up problem, in economics Dean Baker A recent newspaper column by Paul Krugman commented on data showing continuing…

The post Population growth, and the which way is up problem, in economics first appeared on Economic Reform Australia.

Reasonable Seasonals? Seasonal Echoes in Economic Data after COVID-19

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:37am in

David Lucca and Jonathan Wright

Reasonable Seasonals? Seasonal Echoes in Economic Data after COVID-190

Seasonal adjustment is a key statistical procedure underlying the creation of many economic series. Large economic shocks, such as the 2007-09 downturn, can generate lasting seasonal echoes in subsequent data. In this Liberty Street Economics post, we discuss the prospects for these echo effects after last year’s sharp economic contraction by focusing on the payroll employment series published by the U.S. Bureau of Labor Statistics (BLS). We note that seasonal echoes may lead the official numbers to overstate actual changes in payroll employment modestly between March and July of this year after which distortions flip the other way.

Seasonal Echoes after the Great Recession

Many economic series present periodic patterns within each calendar year, generally referred to as seasonal effects. Statistical agencies apply statistical filters to remove these seasonal effects so that the underlying economic trends can be easily compared over time. Most analysts focus on seasonally adjusted data, without paying much attention to the unadjusted series or the adjustment process itself. It is easy then to miss just how large seasonal swings in the unadjusted economic data can be (for GDP, they are on average as big as a typical business cycle peak-to-trough fluctuation) and that the seasonal statistical filter itself can create spurious variation in the adjusted series.

A pernicious problem with seasonal adjustment comes after a big shock that is not seasonal in its origin. Since the seasonal filter determines the normal pattern for, say, January, by a weighted average of the last few Januaries, an unusual observation will have a big impact on estimated seasonal factors. For example, the worst of the 2007-09 Great Recession was in early 2009. Seasonal filters concluded that the normal employment for this time of year was lower. As a result, for the subsequent few years, an “echo” of the Great Recession took place as economic data kept exceeding the artificially low expectations for that time of year. This contributed to a pattern where economic growth seemed to be strong in the spring only to fade later on in the year, as shown by Wright (2013). The problem could be mitigated by the user making manual adjustments. In fact, the Federal Reserve Board made such an adjustment in the 2010 annual revision in the official industrial production statistics.

Seasonal Echoes after COVID-19: Payroll Employment

Last year’s recession was an order of magnitude bigger than the Great Recession. If the seasonal filter were left to run without any special adjustment, the estimated seasonal factors would be completely dominated by the within-year patterns in 2020. Agencies doing seasonal adjustment were well aware of the problem and applied manual adjustments. Agencies dislike making these ad hoc adjustments because they want the data process to be transparent. The COVID-19 recession was so extreme that such interventions were necessary as discussed by the BLS commissioner.

Do these adjustments mean that we will not see seasonal echoes of COVID-19 in the economic series in the future? We take as a case study, total nonfarm payrolls in the Current Employment Statistics (CES), produced by the BLS. This measure is perhaps the most widely watched monthly economic indicator. The BLS also posts extensive documentation of its seasonal adjustment procedure.

Seasonal adjustment in the CES is done at the disaggregate sectoral level. The BLS made manual adjustments first by switching many series from having “multiplicative” to “additive” factors, but also by hardcoding that a particular month for a particular disaggregate was to be treated as an “additive outlier.” Within the X-13 statistical filter, which is used by U.S. agencies for seasonal adjustment, this means that the series will be ignored for the purposes of computing the seasonal factor. X-13 also has some automatic outlier detection, which could mitigate the problem of extreme observations. But this depends on whether the automated procedure detects the outlier. Manually designating the observation as an outlier forces X-13 to exclude the presumed outlier. The chart below shows the ratio of the level of total employment in the CES in sectors that are manually treated as additive outliers to the level of total employment in all sectors, for each month since the start of 2020.

Reasonable Seasonals? Seasonal Echoes in Economic Data after COVID-19

In April 2020, about 90 percent of employment was in sectors that the BLS manually treated as outliers. Since then, the BLS has very slowly reduced the overall fraction of employment that is treated as additive outliers. But even as of February 2021, most employment is in sectors that are receiving this special treatment. Naturally, the sectors that BLS is labelling as outliers are the ones most heavily influenced by the effects of COVID-19, such as airline transportation, for which every month since April 2020 has been hardcoded as an additive outlier.

If the seasonal filter were run without manual adjustment, the seasonal factors for late spring and summer would have plunged in 2020, setting up a huge seasonal echo effect. The manual adjustments greatly reduced—but did not eliminate—this echo effect. The only way of preventing the timing of COVID-19 from disrupting seasonals would be to treat every single component as an additive outlier from March 2020 onward, at least until the effects of COVID-19 are in the rearview mirror. This approach would essentially amount to projecting seasonal factors for March 2020 onward only using earlier data.

To show the possible echo effect we run an exercise of taking the BLS model specification files for seasonal adjustment in the CES and then doing the seasonal adjustment treating every single sectoral employment series as an additive outlier from March 2020 onward, while keeping everything else unchanged. For example, for series where the BLS uses a multiplicative seasonal factor, we used a multiplicative factor. We then computed seasonally adjusted total nonfarm payrolls by month and compared these with the official seasonally adjusted total nonfarm payrolls.

Reasonable Seasonals? Seasonal Echoes in Economic Data after COVID-19

The chart shows the level of official seasonally adjusted payrolls less our alternative. A positive number means that the distortion is driving the seasonal factor down and making the data look better than it really is. We see that the distortions are positive in the spring and summer and negative in the fall and winter. The effects are meaningful, but won’t completely distort the data. For some months, the distortion to the level is over 100,000 payrolls and we can expect these distorted seasonals to carry over into 2021.

Most attention is given to monthly changes in payrolls, rather than the level. Our results would say that in March and April, payroll changes will be overstated by roughly 90,000 jobs per month and that there will continue to be an overstatement in payroll changes until late summer, when the distortion flips the other way. Although this echo effect is important, it is small relative to the effects of COVID-19 and the monthly job growth that would be required to get the economy back to full employment.

There are no easy answers to seasonal adjustment in this environment. The virus changed the economy and seasonal patterns, in some cases temporarily and perhaps permanently in other cases. As in our exercise, it might be desirable to treat every observation as an outlier until the economy is back to normal, or a “new normal,” and then use a “level shift” dummy to restart the estimation of seasonal factors at a new level of the economic variable. This approach would have the advantage of avoiding the echo effect, but the disadvantage that it would take longer for new seasonal patterns to be controlled for.

Seasonal Echoes after COVID-19 beyond Payroll Employment

Our numerical exercise suggests that, yes, we will see some echo effect in payroll numbers, but that this effect was greatly reduced by the interventions made by BLS. What about other series? For data released by the Bureau of Economic Analysis (BEA) in the National Income and Product Account data, such as GDP, the seasonal adjustment is done by different agencies that provide the underlying data to the BEA. Unfortunately, the process is not publicly documented and cannot be reproduced. In fact, until a few years ago, the BEA did not publish nonseasonally adjusted data. While we expect that some manual adjustments were made either by the BEA or other agencies that contribute data to the BEA, it is very hard to tell how big the seasonal echoes in important statistics, such as GDP, may be. Only time will tell.

David LuccaDavid Lucca is a vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Jonathan Wright is a professor of economics at Johns Hopkins University.

How to cite this post:

David Lucca and Jonathan Wright, “Reasonable Seasonals? Seasonal Echoes in Economic Data after COVID-19,” Federal Reserve Bank of New York Liberty Street Economics, March 25, 2021,


The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

The New York Fed DSGE Model Forecast—March 2021

Published by Anonymous (not verified) on Thu, 17/06/2021 - 12:37am in

William Chen, Marco Del Negro, Shlok Goyal, Alissa Johnson, and Andrea Tambalotti

This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. The model projects solid growth over the next two years, with core inflation slowly rising toward 2 percent. Uncertainty for both output and inflation forecasts remains large.

As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.

Modifying the Model for the Pandemic and the New Monetary Policy Framework

The key driver of the model’s forecast over both the short and the medium runs is the response of the economy to the COVID-19 pandemic. To capture the massive and abrupt macroeconomic effects of the virus, as well as their faster retreat compared to standard business cycle dynamics, the model is augmented with several transitory demand and supply shocks starting in 2020:Q1 (the model description on the GitHub page covers these changes in some detail). Starting in 2020:Q2, the COVID-19 shocks are also partly anticipated one quarter ahead. This anticipation captures the fact that shortly after the onset of the crisis, expectations had adjusted to factor in a persistent pandemic. The standard deviations of these new transitory pandemic shocks are drawn from a relatively uninformative prior distribution, since the decline in economic activity might reflect shortfalls in either supply or demand, or both.

Another key source of uncertainty about the future evolution of the economy concerns the persistence of the effects of the COVID-19 shocks. To capture this uncertainty, we consider two scenarios. In the baseline scenario, the role of standard business cycle shocks during the first half of 2021 is limited. As a result, the decline in activity is mostly explained by the transitory pandemic shocks, leading to a faster rebound. In the alternative scenario, the more persistent standard business cycle shocks are more prominent. The result is a more pessimistic forecast in both the short and medium runs, which captures some of the potentially long-lasting economic scars left by the pandemic. This alternative scenario also features more inflation, since it attributes some of these longer-lasting effects to lower productivity growth. The two scenarios are combined using weights of 70 percent on the baseline and 30 percent on the alternative. This weighting is loosely informed by the average probability distribution of year-over-year real GDP growth in 2021 and 2022 from the Philadelphia Fed Survey of Professional Forecasters (SPF) for February.

Starting in 2020:Q4, we assume that monetary policy follows a new reaction function, average inflation targeting (AIT), reflecting the changes in the FOMC monetary policy strategy announced last August. The parameters of the new rule are such that the policy rate lifts off its effective lower bound (ELB) in 2023, increasing very gradually thereafter. Upon its introduction, we assume that agents’ awareness of the new policy is partial but increasing over time. More specifically, expectations are based on a convex combination of the old and new reaction functions, with the weight on the latter converging to 1 over six years. This modelling approach captures the fact that expectations are likely to adjust only gradually to the introduction of the new policy strategy, especially with the policy rate stuck at the ELB, since agents cannot directly observe its reaction to macroeconomic developments until lift-off.

The March 2021 model forecast is reported in the table below, alongside the one from December 2020, and depicted in the following charts. The model uses quarterly macroeconomic data released through 2020:Q4, augmented for 2021:Q1 with the median forecasts for real GDP growth and core PCE inflation from the February SPF release, as well as the yields on 10-year Treasury securities and Baa corporate bonds based on 2021:Q1 averages up to February 25.

How do the latest forecasts compare with the ones from December?

  • The forecast for real GDP growth (Q4/Q4) is 4.7 percent in 2021, down from 6.3 percent in December. This difference mostly reflects lower awareness of the new AIT policy in the short run relative to what was assumed in December, which blunts the policy’s stimulative effects on impact. However, awareness is now modelled as increasing overtime, while it was constant In December. As a result, monetary policy is more expansionary on balance over the forecast horizon, leading to stronger growth in 2022 (4.9 versus 3.1 percent) and 2023 (3.5 versus 2.0 percent).
  • Core inflation is projected to reach 1.4 percent in 2021, well above the December forecast of 0.5 percent, partly reflecting stronger recent readings. However, inflation remains subdued throughout the rest of the forecast horizon, returning to 2 percent only in 2024. One reason for this relatively weak inflation forecast is that the new policy strategy has a limited impact on prices, even as it provides a significant boost to the economy, due to a very flat estimated Phillips curve.
  • Estimates of the real natural rate and its future evolution are higher than in December, reflecting stronger fundamentals, with the natural rate becoming positive toward the end of 2022.

The New York Fed DSGE Model Forecast—March 2021

The New York Fed DSGE Model Forecast—March 2021

The New York Fed DSGE Model Forecast—March 2021

The New York Fed DSGE Model Forecast—March 2021

William ChenWilliam Chen is a senior research analyst in the Bank’s Research and Statistics Group.

Marco Del NegroMarco Del Negro is a vice president in the Bank’s Research and Statistics Group.

Shlok GoyalShlok Goyal is a senior research analyst in the Bank’s Research and Statistics Group.

Alissa Johnson is a senior research analyst in the Bank’s Research and Statistics Group.

Andrea TambalottiAndrea Tambalotti is a vice president in the Bank’s Research and Statistics Group.

How to cite this post:

William Chen, Marco Del Negro, Shlok Goyal, Alissa Johnson, and Andrea Tambalotti, “The New York Fed DSGE Model Forecast—March 2021,” Federal Reserve Bank of New York Liberty Street Economics, March 31, 2021,


The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.