I've been thinking a bit lately about theories of the business cycle (a lot of time for reflection in these days of COVID-19). At least, the way some of these theories have evolved over my lifetime and from the perspective of my own training in the field. From my (admittedly narrow) perspective as a researcher and advisor at a central bank, the journey beginning c. 1960 seems like it's taken the following steps: (1) Phillips Curve and some Natural Rate Hypothesis; (2) Real Business Cycle (RBC) theory; (3) New Keynesian theory. It seems like we might be ready to take the next step. I'll offer some thoughts on this at the end, for whatever they're worth.
There's no easy way to summarize the state of macroeconomic thinking, of course. But it seems clear that, at any given time, some voices and ways of thinking are more dominant than others. By the time the 1960s rolled around, there seemed to be a consensus that monetary and fiscal policy should be used to stabilize the business cycle. The main issue, in this regard, revolved over which set of instruments was better suited for the job. (See, for example, this classic debate between Milton Friedman and Walter Heller).
I put "theory" in quotes in the passage above because the theory (explanation) was never clear to me. In particular, while I could see how an increase in the rate of unemployment might depress the level wage, I could not grasp how it could influence the rate of growth of wages for any prolonged period of time. This logical inconsistency was solved by the Phelps-Friedman natural rate hypothesis; see Farmer (2013) for a summary and critique.
The TL;DR version of this hypothesis is that the PC is negatively sloped only in the short-run, but vertical in the long-run. So, while monetary policy (increasing the rate of inflation) could lower the unemployment rate below its natural rate, it could only do so temporarily. Eventually, the unemployment rate would move back to its natural rate at the higher rate of inflation. This hypothesis seemed to provide a compelling interpretation of the stagflation (high inflation and high unemployment) experienced in the 1970s. It also seemed to explain the success of Volcker's disinflation policy in the 1980s. Nevertheless, uneasiness in the state of the theory remained and a new (well, nothing is ever completely new) way of theorizing was on the horizon.
By the time I got to grad school in the late 1980s, "real business cycle theory" was in vogue; see Charles Plosser's summary here and Bob King's lecture notes here.
There was a lot going on with this program. A central thesis of RBC theory is that the phenomena of economic growth and business cycles are inextricably linked. This is, of course, is an old idea in economics going back at least to Dennis Robertson (see this review by Charles Goodhart) and explored extensively by a number of Austrian economists, like Joseph Schumpeter.
The idea that "the business cycle" is to some extent a byproduct of the process of economic development is an attractive hypothesis. Economic growth is driven by technological innovation and diffusion, and perhaps regulatory policies. There is no a priori reason to expect these "real" processes to evolve in a "smooth" manner. In fact, these changes appear to arrive randomly and with little or no mean-reverting properties. It would truly be a marvel if the business cycle did not exist.
The notion of "no mean-reverting properties" is important. It basically means that technology/policy shocks are largely permanent (or at least, highly persistent). If macroeconomic variables like the GDP inherit this property, then a "cycle"--the tendency for a variable to return to some long-run trend--does not even exist (and if you think you see it, it's only a figment of your imagination). For this reason, early proponents of RBC theory preferred the label "fluctuations" over "cycle." This view was supported by the fact that econometricians had a hard time rejecting the hypothesis that the real GDP followed a random walk (with drift). For example, here is Canadian GDP plotted against two realizations of a random walk with drift:
This perspective fermented at a time when the cost of computing power was falling dramatically. This permitted economists to study models that were too complicated to analyze with conventional "pencil and paper" methods. Inspiration was provided by Lucas (1980), who wrote:
Our task, as I see it…is to write a FORTRAN program that will accept specific economic policy rules as ‘input’ and will generate as ‘output’ statistics describing the operating characteristics of time series we care about, which are predicted to result from these policies.”
And so that's what people did. But what sort of statistics were model economies supposed to reproduce? Once again, it was Lucas (1976) who provided the needed guidance. The empirical business cycle regularities emphasized by Lucas were "co-movements" between different aggregate time-series. Employment, for example, is "pro-cyclical" (tends to move in the same direction as GDP) around "trend." These types of regularities can be captured by statistics like correlations. But these correlations (and standard deviations) only make sense for stationary time-series, and the data is mostly non-stationary. So, what to do?
Transforming the data through first-differencing (i.e., looking at growth rates instead of levels) is one way to render (much of) the data stationary. Another approach was made popular by Prescott (1986), who advocated a method that most people employ: draw a smooth line through the data, label it "trend," and then examine the behavior of "deviations from trend." Something like this,
It's important to note that Prescott viewed the trend line in the figure above as "statistical trend," not an "economic trend." To him, there was no deterministic trend, since the data was being generated by a random walk (so, the actual trend is stochastic). Nevertheless, drawing a smooth trend line was a useful way to render the data stationary. The idea was to apply the same de-trending procedure to actual data and simulated data, and then compare statistical properties across model and data.
The point of mentioning this is that no one involved in this program was conditioned to interpret the economy as "overheating" or in "depression." Growing economies exhibited fluctuations--sometimes big and persistent fluctuations. The question was how much of these observed fluctuations could be attributed purely to the process of economic development (technological change), without reference to monetary or financial factors? I think it's fair to say that the answer turned out to be "not much, at least, not at business cycle frequencies." The important action seemed to occur at lower frequencies. Lucas (1988) once again provided the lead when he remarked "Once one starts to think about growth, it is hard to think about anything else." And so, the narrow RBC approach turned its attention to low-frequency dynamics; e.g., see my interview with Lee Ohanian here.
Of course, many economists never bought into the idea that monetary and financial factors were unimportant for understanding business cycles. Allen and Gale, for example, schooled us on financial fragility; see here. But this branch of the literature never really made much headway in mainstream macro, at least, not before 2008. Financial crises were something that happened in history, or in other parts of the world. Instead, macroeconomists looked back on its roots in the 1960s and embedded a version of the PC into an RBC model to produce what is now known as the New Keynesian framework. Short-run money non-neutrality was achieved by assuming that nominal price-setting behavior was subject to frictions, rendering nominal prices "sticky." In this environment, shocks to the economy are not absorbed efficiently, at least, not in the absence of an appropriate monetary policy. And so, drawing inspiration from John Taylor and Michael Woodford, the framework added an interest rate policy rule now known as the Taylor rule. Today, the basic NK model consists of these three core elements:
[1] An IS curve: Relates aggregate demand to the real interest rate and shocks.[2] An Phillips Curve: Relates the rate of inflation (around trend) to the output gap.
[3] A Taylor Rule: Describes how interest rate policy reacts to output and inflation gaps.
Of the three basic elements of the NK model, I think the NK Phillips Curve (which embeds the natural rate hypothesis) has resulted in the most mischief; at least, from the perspective of advising the conduct of monetary policy. The concept is firmly embedded in the minds of many macroeconomists and policymakers. Consider, for example, Greg Mankiw's recent piece "Yes, There is a Trade-Off Between Inflation and Unemployment."
Today, most economists believe there is a trade-off between inflation and unemployment in the sense that actions taken by a central bank push these variables in opposite directions. As a corollary, they also believe there must be a minimum level of unemployment that the economy can sustain without inflation rising too high. But for various reasons, that level fluctuates and is difficult to determine.
The Fed’s job is to balance the competing risks of rising unemployment and rising inflation. Striking just the right balance is never easy. The first step, however, is to recognize that the Phillips curve is always out there lurking.
The Phillips curve is always lurking. The message for a central banker is "sure, inflation and unemployment may be low for now, but if we keep monetary policy where it is and permit the unemployment rate to fall further, we will risk higher inflation in the future." I'm not sure if economists who write in this manner are aware that they're making it sound like workers are somehow responsible for inflation. Central banker to workers: "I'm sorry, but we need to keep some of you unemployed...it's the inflation, you see."
There is evidence that this line of thinking influenced the FOMC in 2015 in its decision to "lift off" and return the policy rate to some historically normal level; see my post here explaining the pros and cons in the lift-off debate. By the start of 2014, there was considerable pressure on the Fed to begin "normalizing" its policy rate. By mid 2014, the expectation of "lift off" likely contributed to significant USD appreciation and the economic weakness that followed. If I recall correctly, Vice Chair Stan Fischer started off the year by announcing that four rate hikes for 2015 were in order (as it turned out, the Fed only raised rates once--in December). To some observers, this all seemed very strange. After all, the unemployment rate was still above its estimated "natural" rate (5%) and inflation continued to undershoot its 2% target. What was going on?
What was going on was the Phillips curve. Here is Chair Yellen at the March 17-18, 2015 FOMC meeting (transcript available here):
If we adopt alternative B, one criterion for an initial tightening is that we need to be reasonably confident that inflation will move back to 2 percent over the medium term. For the remainder of this year, my guess is that it will be hard to point to data demonstrating that inflation is actually moving up toward our objective. Measured on a 12-month basis, both core and headline inflation will very likely be running below 1½ percent all year. That means that if we decide to start tightening later this year, a development that I think is likely, we will have to justify our inflation forecasts using indirect evidence, historical experience, and economic theory.
The argument from history and economic theory seems straightforward. Experience here and abroad teaches us that, as resource utilization tightens, eventually inflation will begin to rise. To me, this seems like a simple matter of demand and supply. So the more labor and product markets tighten, the more confident I’ll become in the inflation outlook. Because of the lags in monetary policy, the current high degree of monetary accommodation, and the speed at which the unemployment rate is coming down, it would, to my mind, be imprudent to wait until inflation is much closer to 2 percent to begin to normalize policy. I consider this a strong argument for an initial tightening with inflation still at low levels, and it’s one that I plan to make. But I also recognize and am concerned that, at least in recent years, the empirical relationship between slack and inflation has been quite weak.
The plucking model is consistent with the observed cyclical asymmetry in unemployment rate fluctuations. And labor market search models are a natural way to model that asymmetry. In case you're interested, I develop a super-simple (and dare I say, elegant) search model here to demonstrate (and test) the idea: Evidence and Theory on the Cyclical Asymmetry in Unemployment Rate Fluctuations, CJE 1997). See also my blog post here as well as some recent work by Ferraro (RED, 2018) and Dupraz, Nakamura and Steinsson (2019). I like where this is going!
One attractive feature of search models, in my view, is that they model relationship formation. Relationships provide a very different mechanism for coordinating economic activity relative to the canonical economic view of anonymous spot exchange in centralized markets. In a relationship, spot prices do not matter as much as the dynamic path of these prices (and other important aspects) over the course of a relationship (see my critique of the sticky price hypothesis here). The observation that retailers, in the early days of C-19, voluntarily rationed goods instead of raising prices makes little sense in anonymous spot exchange, but makes perfect sense for a merchant concerned with maintaining a good relationship with his or her customers. And merchant-supplier relationships can handle shortages without price signals (we're out of toilet paper--please send more!). In financial markets too, the amount of time that is spent forming and maintaining credit relationships is hugely underappreciated in economic modeling. Search theory turns out to be useful for interpreting the way money and bond markets work too. These markets are not like the centralized markets we see modeled in textbooks--they operate as decentralized over-the-counter (OTC) markets, where relationships are key. One reason why economies sometimes take so long to recover after a shock is because the shock has destroyed an existing set of relationships. And it takes time to rebuild relationship capital.
Notions of "overheating" in this context probably do not apply to labor market variables, although there is still the possibility of an overaccumulation of certain types of physical capital in a boom (what the Austrians label "malinvestment"). Any "overheating" is likely to manifest itself primarily in asset prices. And sudden crashes in asset prices (whether driven by fundamentals or not), can have significant consequences on real economic activity if asset valuations are used to support lines of credit.
Finally, we need a good theory of inflation. The NKPC theory of inflation is not, in my view, a completely satisfactory theory in this regard. To begin, it simply assumes that the central bank can target a long-run rate of inflation (implicitly, with the support of a Ricardian fiscal policy, though this is rarely, if ever, mentioned). At best, it is a theory of how inflation can temporarily depart from its long-run target and how interest rate policy can be used to influence transition dynamics. But the really interesting questions, in my view, have to do with monetary and fiscal policy coordination and what this entails for the ability of an "independent" central bank even to determine the long-run rate of inflation (Sargent and Wallace, 1981).
I know what I've described only scratches the surface of this amazingly deep and broad field. Most of you have no doubt lived through your own process of discovery and contemplation in the world of macroeconomic theorizing. Feel free to share your thoughts below.
Excellent review. Even I could get it.
ReplyDeleteI would like to see a post on how highly intelligent observers of the economic scene, such as Paul Volcker or Martin Feldstein were wrong for 40 years in a row on the direction of interest rates and inflation.
Then consider that before the pandemic, Japan had 150 job openings for every job seeker yet was very close to deflation a situation that has persisted for a couple decades.
If we are generating macroeconomic principles, do they apply only in the US or globally?
Hi Ben, thanks. I'm working on something that might help answer that question. My hypothesis is that economists did not forecast the large secular increase in the demand for U.S. Treasury securities. Keep a look out! David
DeleteMaybe related, M1 has exploded in Japan, especially deposits. The Japanese already keep enormous amounts of cash in circulation, something near the equivalent of $9000.
DeletePerhaps, as a practical matter, governments must migrate to money-financed fiscal programs.
I am curious as to why you don't mention increasing returns to scale as a possible avenue to reforming macroeconomics in your post. Increasing returns to scale is a phenomena that we know exists for many firms and that we know changes macroeconomic results in important ways (see for example https://www.jstor.org/stable/2078025?seq=1#metadata_info_tab_contents). Given those factors I would think that incorporating increasing returns into macroeconomic models ought to be standard, yet doing so does not seem to be seen as important by many in the field. The possible role of increasing returns to scale in providing microfoundations for "real" keynesian results and for understanding the current lack of inflation seems to barely be discussed.
ReplyDeleteI wonder sometimes if the lack of attention to increasing returns in economics education is responsible for their neglect. Econ 101 and graduate microeconomics focus largely on convex production sets, so are perhaps less prepared to take increasing returns to scale seriously than they incorporated into economics education more often. For example, I think analysis such as what I do in this(https://themountaingoateconomics.com/2020/11/19/the-math-behind-supply-curves/) post, where I make clear that firms with decreasing costs are demand limited, would perhaps go a long way towards getting increasing returns taken seriously in macro.
The standard answer to this question is that global increasing returns at the firms level lead to the counterfactual prediction that a single firm would own all the output in the economy, which is not true. Hence, it's difficult to work mathematically with increasing returns.
DeleteThe other answer is that a bunch of macro models do have increasing returns at the center stage, like Romer (1990). So maybe pick up a textbook on endogenous growth theory and come back to join the discussion afterwards?
That is a bad answer though. The fact that a simplified model gives wrong answers when increasing returns to scale are incorporated is not a reason to ignore them. I discuss that argument and why it is bad here https://themountaingoateconomics.com/2021/01/08/competition-with-increasing-returns-to-scale/
DeleteSure, some models incorporate increasing returns to scale. But since we know they exist, change the results of macroeconomic models, and likely are becoming increasingly important the question is why don't all models incorporate them.
A place to start, I think, is to ask how IRS matters for a specific question you'd like to address. A good way to do this, I think, is to take an existing model with CRS and study its results and how they square with the data. Then, rework the model using IRS. What changes? Does the model fit the data better? Are the policy implications different? Etc. There's only one way to find out -- do it!
ReplyDeleteP.S. I did not include IRS because I didn't think of it. I also didn't include expectation formation, and many other things. Will try next time! Thx.
There is another way to find out of course! Read what other people have done. There is a lot of interesting work in this area already. Unfortunately though none of it appears to really have led to much change in the field: my impression is that most of it has largely been ignored. It would be helpful to know the reasons why to ensure that any work I do won't be ignored for similar reasons. It is also difficult to engage in research on your own without having anyone to run ideas by. Currently I think that increasing returns means that intertemporal maximization and representative agent paradigms don't really make sense, but it is difficult to advance further on my own.
DeleteSo I will continue to work on Post-Keynesian economics and learning about increasing returns will remain a hobby.
I have a question regarding the idea that technological innovations are the (main) cause of economic fluctuations: suppose you have a model, in which the production function of individual firms is fixed (i.e. technological innovations in the strict sense are absent by design), and fluctuations are caused for example by investment spending of firms (e.g. multiplier–accelerator model). A "statistical office" in the model calculates time series for economic variables according to the system of national accounts ("capital stock" for expample is the monetary value of all available capital goods, regardless of their utilization). Suppose further that the cyclical moments of the model generated SNA time series mimic their real world counterparts quite well - including the moments of total factor productivity. We can tell than the latter are nothing more than statistical artifacts.
ReplyDeleteOf course this exercise would demonstrate only the principal possibility that tfp fluctuations are just statistical artifacts, but as nobody has seen them in real life so far (in a meaningful statistical sense)...
Sorry, mistakable: I meant a meaningful link between concrete technological innovations (and regress?) and ups & downs of GDP.
ReplyDeleteThank you for this insightful article. Lots to unpack here and I am sure I will be spending much time reading all of this. One thing that I've always struggled with is the equivalent treatment given to "overheating" and "inflation". Per my understanding, inflation is broad-based and persistent, i.e. it requires a positive feedback loop. Perhaps in the 60s and 70s, the feedback loop was negotiated wage contracts through unions. Fiscal stimulus, even if assumed to be greater than output gap like now, does not in my mind trigger a feedback loop. I mean, I cannot intuitively understand why people and businesses would anticipate prices to go up year on year on year at a higher or accelerated pace than before. Especially now. Right now, we are definitely going through both a demand and supply shock and are unclear about which dominates. Once we approach herd immunity, the most logical course seems to me to be that supply lags demand. For businesses hardest hit by the pandemic, it makes sense to wait for signs of demand to pick up before increasing supply to pre-covid levels and let prices go up, also because the earliest to demand travel/hospitality services upon vaccination will also be the ones most willing to pay a premium for it. How sharp the price increases are depends on how sharp the demand pick up is. But ultimately they will equilibrate at a permanently higher level at best and in the process, push up wages in certain sectors at best. That still is only temporary overheating and not inflation.
ReplyDeleteThought provoking as usual, but perhaps not quite as ecumenical as it might have been - it is a huge area to wander about in.
ReplyDelete"Believe those who are seeking the truth. Doubt those who find it."
Gide might have been on to something. If he was then you are left wondering whether the search for truth is a waste of time, particularly as it might relate to macroeconomics.
Firstly, re the 1970s stagflation you mention. It seems to me the behaviour of the economy in the west is easily explainable - no grand theories required (Lucas made much of it in trying to bury Keynesianism). The economies in the west suffered two shocks. There was the oil price supply side shock which caused the hyperinflation and then there was also a demand side shock which caused the massive transfer of income from the west to oil producing economies which resulted in stagnation in the west.
The mystery of the missing inflation, even in the face of extensive and prolonged QE, of the last twenty years is also easily explainable, I believe. The supply side - the globalization of the last 40 years has resulted in the dislocation of labour and goods markets in the west. Cheap goods based on cheap, pliable labour coming firstly from Japan, then south east Asia and eastern Europe after the collapse of the Soviet Union and now China have flooded western markets. The demand side - secular mal-distribution of income and wealth over the last 50 years has destroyed the purchasing power of the less well off. The combination of QE and this mal-distribution of income have diverted the inflationary forces resulting in the asset bubbles of the last decade.
I am re reading Big Ideas In Macroeconomics by Athreya. Early on in the book Athreya writes of his extensive training in microeconomics and in the next breath mentions macroeconomics as if somehow the two should be/can be related. His book is an apologetic for the 40 wasted years of the New Classical/Neoclassical diversion in macroeconomics.
Microeconomics (i.e. essentially New Classical/Neoclassical economics)is a theory of optimal resource allocation under constraint (given income and full employment of resources)driven by relative prices. How can it explain sub full employment of resources output?
On the other hand, the Keynesian consideration of the level of spending (income) easily accounts for sub full employment output.
I believe the two approaches can be reconciled and synthesized but not in the manner currently conceived.
Henry Rech