371 L E C T U R E S U P P L E M E N T
15-1
How a Real Business Cycle Model Is Constructed
The dynamic AD–AS model developed in this chapter can be used to analyze economic growth by considering how an increase in the natural level of output affects the economy. While this feature can be interpreted as incorporating a long-run growth path for the economy into a model of short-run fluctuations, it can also be interpreted as allowing for elements of the real business cycle approach (discussed in the appendix to Chapter 9) to play a role in short-run business cycle analysis. In this sense, the model of Chapter 15 can be viewed as a hybrid model that includes both Keynesian features that allow money to have short-run real effects and real business cycle elements that influence short-run fluctuations. The more advanced dynamic, stochastic, general equilibrium (DSGE) models mentioned in the text also exhibit these hybrid characteristics. This supplement and the several to follow discuss how real business cycle models are constructed and tested.
Real business cycle models emphasize the role of technology shocks in driving short-run economic fluctuations. These models generally differ from other macroeconomic models, not only in their theoretical explanation of economic fluctuations, but also in the way they are tested with economic data. Typically, economists test a theory by ascertaining an implication of that theory for economic data and then applying statistical and econometric techniques to see whether or not the data are consistent with the theory. The approach of real business cycle theorists, however, has usually been to simulatethe outcomes of their models and to compare those simulations with actual data.
A simple illustration has been provided by the economist Charles Plosser.1 He considered the problem of a representative individual who has to make two decisions at any point in time.2First, the individual must decide how to divide her time between leisure and working; and, second, she must decide how to divide her output between consumption and investment to increase future consumption. The individual makes these choices in order to maximize her expected utility (happiness), which depends upon her consumption and leisure now and at all times in the future.3Plosser assumes that the individual also has access to a Cobb–Douglas production function:
Here, Atrepresents the possibility of random technology shocks.
The first step in this model is calibration, or choosing values for the parameters of the model. In this case, Plosser has to choose values for capital’s share of output (α) and the depreciation rate of capital, as well as for parameters reflecting the individual’s preferences. These parameters are chosen on the basis of other information that we have about the economy.4 Next, Plosser solves the choice problems of the agent—in essence deriving a consumption function and a labor supply function. Finally, Plosser uses the Solow residual
as a measure of technology shocks.
Plosser then simulates the model. This means that he works out how this economy would behave under the assumption that the representative agent sees technology shocks as unpredictable and permanent. Plosser can then find the implied series for GDP, consumption, investment, employment, and real wages. Figures 1 to 5 show how Plosser’s simulations compare with the actual behavior of the U.S. economy.
1C. Plosser, “Understanding Real Business Cycles,” Journal of Economic Perspectives 3, no. 3 (Summer 1989): 51–77.
2If the economy is competitive (as Plosser assumes), then markets will allocate resources efficiently and we are not badly misled by simply imagining that the economy consists of a single individual.
3Specifically, Plosser assumes that the agent maximizes the following utility function: ∞
U= ∑ βt[log(Ct) + ηlog(1 – Lt)]. t= 0
He thus assumes that the agent has one unit of time, so that 1 – Ltcorresponds to leisure. The parameter βmeasures how much the agent values the present relative to the future, and the parameter ηmeasures how much the agent values leisure relative to consumption.
4Specifically, Plosser chooses α= 0.42, β= 0.95, the depreciation rate = 0.1, and the parameter ηsuch that L
t= 0.2 at all times.
372 CHAPTER 15 A Dynamic Model of Aggregate Demand and Aggregate Supply ±4 ±6 ±2 0 2 4 6 1955 1960 1965 1970 1975 1980 1985 Percent Actual Predicted
Annual Growth Rate of Real Output Figure 1 ±4 ±6 6 4 2 0 ±2 1955 1960 1965 1970 1975 1980 1985 Percent
Annual Growth Rate of Real Consumption Figure 2
Predicted Actual Source: Figures 1 to 5 from C. Plosser, “Understanding Real Business Cycles,” Journal of Economic Perspectives3, no. 3 (Summer 1989): 51–77.
5 10 0 10 5 1955 1960 1965 1970 1975 1980 1985 Predicted Percent Actual
Annual Gr owth Rate of Real Investment Figur e 3
Source: C. Plosser, Understanding Real Business Cycles, Journal of Economic Perspectives, Summer 1989.
Source: C. Plosser, Understanding Real Business Cycles, Journal of Economic Perspectives, Summer 1989. 6 4 2 0 2 4 6 1955 1960 1965 1970 1975 1980 1985 Percent
Annual Gr owth Rate of Hours W orked Figur e 4
Predicted
Actual
374 CHAPTER 15 A Dynamic Model of Aggregate Demand and Aggregate Supply
Interpreting these figures is not easy, but they are quite striking. In particular, Plosser’s simulations for output and consumption seem to match up very well with the actual data, although the fit is worse for investment and labor market variables. These pictures indicate that a competitive economy hit by technology shocks can exhibit fluctuations that broadly resemble those experienced by the U.S. economy.
A problem with this line of research is that there has been insufficient formal statistical analysis of what constitutes a good match between simulated results and actual data. Plosser’s simulations look as if they correspond to the U.S. data, but we cannot tell from inspection whether or not there is a good fit in a more formal statistical sense. Also, as discussed in Chap-ter 9 of the textbook, the Solow residual may not be a good measure of technological change.
The methodology followed by Plosser is essentially that pursued by most real business cycle theorists, except that they do not usually assume a specific series (such as the Solow residual) for technical change. Instead, they simply suppose that there are random shocks to the technology drawn from some statistical distribution. Rather than running just one simulation, real business cycle theorists simulate their models many times over. By doing this often enough, they can discover the broad patterns that their models imply for the data (for example, the correlation between output and consumption). They then compare these patterns with those observed in actual data.5 Much modern research in macroeconomics examines refinements of this basic model in an attempt to improve the match between the models and the data. Some researchers are pursuing versions of the model that include the sort of imperfections emphasized by new Keynesian economists. As a result, many economists are hopeful that a synthesis of real business theory and new Keynesian economics is starting to emerge through the development of DSGE models in which money has effects on real variables in the short run alongside the effects of technology shocks.
5See also Supplement 8-3, “Does the Solow Model Really Explain Japanese Growth?” for another use of a real business cycle model. That supplement discusses Christiano’s simulation of a neoclassical growth model to investigate the hypothesis that Japanese saving behavior is explained by post–World War II reconstruction.
6 4 2 0 2 4 6 Percent 1955 1960 1965 1970 1975 1980 1985
Annual Gr owth Rate of Real W age Rate Figur e 5
Actual Predicted
375 L E C T U R E S U P P L E M E N T
15-2
The Microeconomics of Labor Supply
Many economists are unconvinced that real business cycle theory can adequately explain fluctuations in employment. To pursue this further, we start from two features of this theory: first, prices are assumed to be flexible; and, second, shocks to technology are the driving force behind economic fluctuations.
Since prices are flexible, it follows that the labor market is always in equilibrium, so labor demand always equals labor supply. Technology shocks affect the marginal product of labor and so cause the demand for labor to shift. Looking at the effects of shifts in labor demand in Figures 1A and 1B, we see that steep labor supply implies little variation in employment and large variation in the real wage; whereas if labor supply is flat, then real wages would be relatively stable and employment would vary substantially. The data exhibit much employment fluctuation and little real-wage variation. It follows that, to explain the data, real business cycle theories need either a relatively flat labor supply curve or an explanation of why technology shocks might also shift labor supply. We consider each in turn.
Neither theory nor the data provide a great deal of support for a flat labor supply curve. An individual’s labor supply decision is based on the choice between leisure and goods. Individuals have a certain amount of time at their disposal, which they can either take as leisure or else can use for working in order to earn income with which to buy goods. The real wage, w, is the relative price of leisure in terms of goods. The higher the real wage, the more goods must be forgone for an extra hour of leisure. We illustrate this in the standard microeconomic manner in Figure 2.
w w L (A) L (B) Ls Ld Ls Ld 24w 24w ^ Goods Leisure 24 Ls A B Figure 2 Figure 1
We suppose that the individual has 24 hours to allocate between working and leisure. At one extreme, she could not work at all and take all her time as leisure. At the other extreme, the worker could work all 24 hours, have no leisure time, and consume 24wworth of goods. The line connecting these two points is the budget line; any point on this line is a feasible combination of leisure and goods. The optimal combination of goods and leisure is found where the indifference curve is tangent to the budget line.
Now suppose that the real wage rises to w. We can see from Figure 2 that although leisure has now become more expensive, the individual may increase (case A) or decrease (case B) her supply of labor. The reason is that changes in the real wage generate income and substitution effects that act in opposite directions. The substitution effect encourages people to work more (that is, consume less leisure) when the wage goes up. A rise in the real wage, however, increases the income from working, allowing the individual to consume more leisure. Thus, the effect of an increase in the real wage on labor supply is theoretically ambiguous. It is perhaps not surprising that the data show that changes in the real wage do not have strong effects on labor supply. In terms of our original diagrams, therefore, the labor supply curve is steep. Contrary to the data, we would expect to see large changes in the real wage and small changes in employment if the economy is competitive and characterized by changes in labor demand.
We observe a larger change in employment and a smaller change in the real wage if technology shocks affect both labor demand and labor supply in the same direction. This can occur if the interest rate changes or if there is a temporary change in the real wage. For example, if the real wage is high in the present but expected to be low in the future, workers might prefer to work very hard when the wage is high and take much leisure time when the wage is lower. To put the same point a slightly different way, labor supply might be very responsive to short-run fluctuations in the real wage, even if it is not responsive to long-run changes. Similarly, if the interest rate is higher, working today looks relatively attractive.
We can illustrate this in terms of the labor market by putting the currentreal wage on the axis and noting that changes in the expected futurereal wage or the interest rate shift the labor supply curve, as in Figures 3A and 3B.
An increase in the future real wage (wt+ 1) makes the current supply of labor less attractive and so causes the labor supply curve to shift inward. An increase in the interest rate is like a decrease in the future real wage and so shifts the labor supply curve outward.
Now, consider a temporary shock to labor demand (caused perhaps by a temporary shock to the technology). This shock does not affect the future real wage and so leads to a relatively large change in employment. Such a shock is unlikely to have a large effect on the
376 CHAPTER 15 A Dynamic Model of Aggregate Demand and Aggregate Supply
(A) (B) Lt Ld' Ld Ls(wt + 1, r) wt wt Ls(w , r) Ld Ld' Lt Ls' t + 1 Figure 3
Lecture Notes 377
interest rate. On the other hand, a permanent (positive) shock to labor demand leads to expectations of higher real wages in the future, causing the labor supply curve to shift in. This results in a relatively small change in employment.
A focus on temporary changes in the real wage thus allows real business cycle theory to explain fluctuations in employment while recognizing that labor supply need not be very sensitive to real wages in the long run. Microeconomic analyses of individual labor supply, however, are still not very supportive of strong intertemporal substitution effects of this kind—that is, they do not indicate that labor supply is very responsive to temporary real wage changes or to changes in the interest rate.
L E C T U R E S U P P L E M E N T
15-3
Quits and Layoffs
Job separations can occur either because workers voluntarily quit their jobs or because they are laid off (or fired with cause). We can thus write
s= q+ l,
where s is the separation rate (see Chapter 7), q is the quit rate, and l is the layoff rate. Theories of intertemporal substitution argue that employment is lower in recessions because the real wage (or the interest rate) falls and workers are unwilling to work at this lower wage. Such an explanation suggests that quits should be an important component of flows from employment to unemployment, and also that quits should be higher in recessions.
The data reveal, however, that layoffs are much more important than quits in explaining flows into unemployment. Data suggest that less than 15 percent of the unem -ployed become unem-ployed as a result of quitting their job. For example, unemployment in 2005 was 7.6 million. Of these, 3.7 million (48 percent) were unemployed as a result of layoffs, and 0.7 million (9 percent) were new entrants into the labor force. Of the remainder, 2.4 million (31 percent) had been previ ously employed and were reentering the labor force after a spell of nonparticipation. Only 0.9 million (12 percent) were job leavers—that is, individuals who quit their jobs voluntarily.1 Finally, the data indicate that quits are procyclical and layoffs are countercyclical. These data do not support the belief that employment fluctuations over the business cycle are the result of voluntary shifting of labor.
378
L E C T U R E S U P P L E M E N T
15-4
Involuntary Unemployment and Overqualification
Some economists distinguish between two types of unemployment: voluntary and involuntary. According to the usual definition, someone is voluntarily unemployed if, at the existing wage, she does not think it worthwhile to work. A person who is involuntarily unemployed would like to work at existing wages but cannot obtain a job.
Other economists argue that the idea of involuntary unemployment makes no sense. After all, they suggest, an unemployed investment banker or neurosurgeon could always get a job flipping hamburgers or waiting tables. So how can we distinguish between involuntary and voluntary unemployment? Robert Lucas expands on this argument as follows:1
Nor is there any evident reason why one would wantto draw this distinction. Certainly the more one thinks about the decision problem facing individual workers and firms the less sense this distinction makes. The worker who loses a good job in prosperous times does not volunteer to be in this situation; he has suffered a capital loss. . . . Nevertheless the unemployed worker at any time can always find some job at once. . . . Thus there is an involuntary element in all
unemployment, in the sense that no one chooses bad luck over good; there is also a voluntary element in all unemployment, in the sense that however miserable one’s current work options, one can always choose to accept them.
Truman Bewley, an economist at Yale University, interviewed a large number of businesspeople in order to learn more about their decisions about hiring workers. His findings suggest that it may not be quite so easy for unemployed workers to find jobs, even at lower wages2:
Cannot workers find jobs immediately simply by accepting sufficiently low pay? Perhaps the clearest regularity of the survey was that large classes of unemployed workers find it very difficult to obtain work paying substantially less than what they earned before, unless they take temporary jobs or low-paying jobs in the secondary labor market. Most employers offering good permanent jobs shun workers who earned significantly more previously, significantly meaning 20–30 percent more. Employers label such workers as overqualified and fear that they will be discontent, be a threat to their supervisors, and above all, will leave as soon as they find better jobs.
Note that Bewley’s findings do not completely contradict Lucas’s argument. They suggest that the unemployed investment banker could indeed get a job flipping hamburgers. But they also suggest that this unfortunate investment banker probably cannot do much better.
379 1R. Lucas, “Unemployment Policy,” American Economic Review, Papers and Proceedings68 (May 1978): 354.
A D V A N C E D T O P I C
15-5
Why Technology Shocks Are So Important
in Real Business Cycle Models
In any competitive flexible-price model, such as those espoused by real business cycle theorists, labor market clearing implies that the real wage must equal the marginal product of labor. As explained in Chapter 3, the marginal product of labor gives the firm’s demand for labor and depends upon the amount of capital and labor that firms possess. In particular, we expect to see diminishing marginal product: the marginal product of labor will be lower when employment is higher, other things being equal. We write
MPL(K, L) = W/P.
We know that employment is procyclical—not surprisingly, employment is higher in booms and lower in recessions. Other things being equal, we would expect to see the marginal product of labor falling in booms, and hence, if the economy is competitive, we would expect to see the real wage also being lower in booms. But we also know from Case Study 14-1 that the real wage is actually mildly procyclical—real wages are higher in booms and lower in recessions.1
It follows that if we are to reconcile a procyclical real wage with diminishing marginal product of labor in a competitive model, other things are not equal. Something must happen in booms to raise the marginal product of labor even though employment is higher. Since the capital stock changes only slowly and does not vary in any significant way over the business cycle, the only possibility is that the marginal product of labor is higher in booms because of technological improvements. This is why technology shocks are an essential ingredient of real business cycle models.
If the economy is not competitive, these issues need not arise. First of all, the demand for labor may depend upon other factors. For example, when prices are sticky, firms may demand less labor in recessions because they cannot sell all their output (whereas in a competitive model with flexible prices, firms can always sell as much as they want at the market price).
As another example, suppose that the economy is not perfectly competitive but instead exhibits imperfect competition. We can rewrite the earlier equation as
P= W/MPL.
This says that, in a competitive economy, the price of output equals the marginal cost of production (since the wage is the cost of a unit of labor and the marginal product of labor gives the amount of output contributed by the last unit of labor). Under imperfect competition, however, firms set prices as a markup (m) over marginal cost:
P= m×(W/MPL)
⇒ MPL/m= (W/P).
In this case, the real wage can be procyclical even if the marginal product of labor is countercyclical, provided that the markup is also countercyclical. That is, if markups are higher in recessions, then MPL/mwill be lower, and so the real wage may be lower.
1Procyclical real wages are also a necessary ingredient of real business cycle theory. If high employment in booms and low employment in recessions arise from voluntary shifting of labor, it follows that workers are choosing to consume less leisure in booms and more leisure in recessions. But why would they choose to consume less leisure and more consumption goods at the same time? The answer has to be that leisure is relatively more expensive in booms—that is, real wages must be higher.
Chapter Supplements 381
There are reasons for believing that markups may indeed be countercyclical. One reason why markups may fall in booms is that higher profits when the economy is booming cause more firms to enter an industry. The more firms in the industry, the closer it is to being competitive, and so the lower is the markup. Another possibility is that imperfect competition reflects collusion among firms, and firms maintain such collusion by a threat to increase output if other firms cheat. In a boom, demand is high, so the potential gain from cheating is greater and firms can sustain less collusion.2
Mark Bils investigated the behavior of marginal cost and price over the course of the business cycle. He found that marginal costs are strongly procyclical but prices do not vary much over the business cycle. His evidence suggests that the markup—the difference between price and marginal cost—is countercyclical.3
Julio Rotemberg and Michael Woodford investigated a real business cycle model with some imperfect competition and countercyclical markups. They carried out simulations and argue that they were better able to match the U.S. data by their inclusion of imperfect competition.4 This may be an encouraging route for synthesis between real business cycle and new Keynesian theories. Once we introduce imperfect competition, however, there is no longer a presumption that fluctuations are efficient and there may be a case for government intervention to stabilize the economy.
2For theoretical exposition of these ideas, see S. Chatterjee and R. Cooper, “Multiplicity of Equilibria and Fluctuations in Dynamic Imperfectly Competitive Economies,” American Economic Review, Papers and Proceedings79 (May 1989): 353–57; and J. Rotemberg and G. Saloner, “A Supergame-Theoretic Model of Price Wars During Booms,” American Economic Review76 (June 1986): 390–407.
3Mark Bils, “The Cyclical Behavior of Marginal Cost and Price,” American Economic Review77 (December 1987): 838–55.
A D V A N C E D T O P I C
15-6
Real Business Cycles and Random Walks
Real business cycle theory provides a challenge to the traditional explanation of macro -economic fluctuations. One reason why this theory has been so influential is the work of two economists, Charles Nelson and Charles Plosser.
In an important article published in 1982, Nelson and Plosser argued that there is evidence to suggest that U.S. GDP may follow a random walk.1That is, they suggested that the behavior of real GDP over time could be described by the equation
Yt= Yt – 1+ ut, (1)
where ut is a random shock that is zero on average. This equation states that the best prediction of GDP this year is whatever value it had last year.
The conventional view of macroeconomic fluctuations is that the behavior of GDP over time can be decomposed into a long-run natural-rate or trend component and a short-run cyclical component. This approach underlies the models used in the textbook: the Solow growth model explains the long-run behavior of the economy and the aggregate demand–aggregate supply model explains short-run fluctuations. In this view, shocks to the economy will push it away from the natural rate only temporarily; the economy always has a tendency to revert to the natural rate. But the Nelson–Plosser finding challenges this characterization. If GDP does follow a random walk, then shocks to output have permanent effects.
To see this, suppose that at some time (t= 0), GDP is at the value Y0, and that at t = 1 there is a one-unit shock to GDP (u1= 1). Suppose also that there are no further shocks (u2=
u3= . . . = 0). Then Y1= Y0+ 1. Now Y2= Y1+ u2 = Y1 = Y0+ 1. Similarly, Y3= Y0+ 1,
and so on. The shock to GDP in period 1 persists forever. Following this shock, our best prediction about GDP is that it will forever be one unit higher (Figure 1).
The observed fluctuations in GDP, according to this theory, are then fluctuations in the natural rate of output, not cyclical fluctuations of output around the natural rate. Whereas the traditional theory suggests that technological progress is a relatively smooth and gradual process, real business cycle theory suggests that technological progress is irregular and a source of fluctuations. Indeed, if this real business cycle characterization of the data is accurate, then the traditional decomposition of output into cycle and trend does not really make sense.
If GDP does not follow a random walk, then the conclusion is very different. Suppose, for example, that the behavior of GDP can be described by the equation
Yt= 0.9Yt– 1+ ut. (2)
382
1C. Nelson and C. Plosser, “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications,” Journal of Monetary Economics10 (September 1982): 139–67.
Lecture Notes 383
Then, if we carry out the same experiment, we find that the shock raises output by 1 at time
t= 1, as before. Next period, however, output is 0.9 higher as a result of the shock. In period 3, output is only 0.81 (= 0.92) units higher, and so on. In other words, the impact of the shock on GDP gradually dies out. In this representation, shocks to the economy are temporary, not permanent, and output does tend to return to the natural rate following a shock (Figure 2).
So, if the Nelson–Plosser result is right, and GDP can be well described by a random walk, we need to think in terms of models where shocks have permanent effects. In terms of standard aggregate demand–aggregate supply models, this suggests that real or supply shocks, such as to technology, govern the behavior of GDP; aggregate demand shocks do not have permanent effects on output in such models. Demand shocks may, however, have permanent effects on GDP in other models such as the hysteresis models discussed in Chapter 13. Steve Durlauf, however, points out that if GDP follows a random walk, it is also consistent with a world in which coordination failures are important. In this case, demand shocks might push the economy from one equilibrium to another.2
Unfortunately, it is very hard to distinguish in the data between equations (1) and (2), and so we simply are not sure whether the random-walk characterization is accurate. Very different theories will generate very similar predictions for the behavior of GDP. For example, a world with demand shocks and very sticky prices is one in which shocks would exhibit a great deal of persistence, so GDP might appear close to a random walk. On the basis of GDP data alone, it is nearly impossible to distinguish between this economy and an economy governed by real shocks.
Modern macroeconomics is making progress toward a synthesis in which it is recognized that both demand and supply shocks have important effects on output.3In this view, the nat-ural rate of output grows irregularly, as suggested by real business cycle theory, rather than exhibiting the smooth change of the Solow growth model. Nevertheless, demand shocks may still cause the actual level of GDP to differ from the natural rate and so may be an additional source of variability in GDP. In principle, in such a world, there is still room for stabilization policy in order to eliminate inefficient cyclical fluctuations. Eliminating allfluctuations is no longer desirable, however, since some variation in GDP is an efficient response to technology shocks. Although many economists doubt that real business cycle theory completely explains economic fluctuations, most might agree that it teaches the important lesson that some varia-tion in GDP is to be expected and is indeed desirable in a well-funcvaria-tioning economy.
2S. Durlauf, “Output Persistence, Economic Structure and the Choice of Stabilization Policy,” Brookings Papers on Economic Activity2 (1989): 69–136. 3See, for example, O. Blanchard and D. Quah, “The Dynamic Effects of Aggregate Demand and Supply Disturbances,” American Economic Review79
(September 1989): 655–73. Y Figure 1 1 Time Figure 2 1 Y Y Time
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L E C T U R E S U P P L E M E N T
15-7
Inflation Inertia
The Phillips curve used in the dynamic AD–AS model of Chapter 15 can be derived under the assumption that all firms have the ability to set prices and some of those firms set their prices one period in advance. As shown in Chapter 14, this assumption implies a Phillips curve that relates period tinflation to the period t – 1 expectation of period t inflation and the gap between actual and the natural level of output. Introducing adaptive expectations then allows derivation of the DAS curve, which relates period t inflation to period t – 1 inflation and the deviation in output from its natural level. The effect of lagged inflation in the DAScurve is responsible in the model for the gradual adjustment of inflation in response to shocks.
A more sophisticated approach, known as staggered price setting, assumes that firms all set prices in advance for two periods, with half of the firms setting prices in any given period. Staggered price setting makes the overall level of prices adjust gradually, even when individual prices adjust frequently. In other words, the price level will adjust fully to an increase (decrease) in aggregate demand only after a period of time during which output exceeds (falls short of) its natural rate. But a surprising implication of these New Keynesian models of staggered price setting under rational expectations is that inflation—the percent change in prices—does not exhibit inertia. Instead, inflation is expected to decline when output is above its natural rate and vice versa.1
The reason for this result is that when price-setters fix a price for the current and future periods, they consider not only today’s overall price level, but also the price level expected to prevail in the future. The resulting Phillips curve expresses inflation as a function of next period’s inflation and the current output gap. Accordingly, a declining path for inflation is associated with output above its natural rate.
Evidence for the United States and many other countries contradicts this implication and supports the view that inflation is highly persistent. Periods of disinflation across countries are overwhelmingly periods when output is below normal.2And estimates of the inflation process for the United States find that lagged inflation helps explain current inflation.3
Various ways of reconciling New Keynesian models of price dynamics with evidence of inflation inertia have been proposed. These include adding delays in price adjustment, incorporating some backward-looking price-setters, indexing fixed prices to overall inflation between adjustments, and introducing more complex dynamics in costs or markups.
Greg Mankiw and Ricardo Reis have suggested changing the basic framework from one with “sticky prices” to one with “sticky information.”4 Instead of assuming full information with staggered price setting, Mankiw and Reis assume firms can always adjust prices but are limited by the cost of obtaining and processing information. As a result, firms may choose a path for their prices that is set until the next time they update their information. The result leads to a Phillips curve in which past inflation affects current inflation and in which disinflations are associated with below-normal output.
One drawback of the Mankiw-Reis approach is that it does not allow a role for fixed prices, despite evidence of their importance in the economy. In addition, the sort of
1This supplement draws on the discussion in Chapter 6 of David Romer, Advanced Macroeconomics, third edition, (New York: McGraw-Hill/Irwin, 2006).
2See Laurence Ball, “What Determines the Sacrifice Ratio?” in N.Gregory Mankiw, ed., Monetary Policy, (Chicago: University of Chicago Press, 1994): 155-183.
3See Jeffrey Fuhrer, “The (Un)Importance of Forward-Looking Behavior in Price Specifications,” Journal of Money, Credit, and Banking, 29 (August 1997): 338-350.
4N. Gregory Mankiw and Ricardo Reis, “Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve,” Quarterly Journal of Economics, 117 (November 2002): 1295-1398.
Lecture Notes 385
predetermined paths that firms choose in their model do not appear to be widespread in the economy. Furthermore, fixed prices appear essential for explaining why shifts in aggregate demand have smaller and shorter-lasting effects in high-inflation economies, and why the announcement in advance of disinflation policies doesn’t measurably affect the output costs of disinflation. Most likely, a complete framework for explaining inflation dynamics will require both fixed prices and predetermined price paths.
386
A D D I T I O N A L C A S E S T U D Y
15-8
Volatility and Growth
Garey Ramey and Valerie Ramey investigated the connection between the growth and the volatility of GDP in a number of different countries.1 They wished to find out if long-run growth and short-run volatility were related. As a matter of theory, growth and volatility could be directly or inversely related. For example, large fluctuations in output might make firms reluctant to commit to irreversible investment, implying that growth would be lower in countries with highly variable output. Conversely, consumers in a relatively uncertain world might save a lot, which could lead to higher growth.
Figure 1 shows the relationship between volatility and growth in OECD countries, measured over the period 1952–1988.2 There is a strong negative relationship: countries with highly variable output tend to be countries that grow more slowly, and conversely. One implication of Ramey and Ramey’s findings is that the benefit of reducing business cycle fluctuations might therefore be larger than is commonly supposed: stabilization of the economy in the short run might help promote growth in the long run.
Figure 1 CAN IT AL USA AU T NLD GBR BEL PA T DEU GRC CHE IS L LUX ESP FIN DNK NZL IRL TUR 3.82 Standard de viation of output gr ow th
2.03 2.02 AU S SW E JPN FRA NOR 4.07
Mean output gro
wt
h
Source: G. Ramey and V. Ramey, “Cross-Country Evidence on the Link Between Volatility and Growth,” American Economic Review85, no. 5 (December 1995): 1143.
1G. Ramey and V. Ramey, “Cross-Country Evidence on the Link Between Volatility and Growth,” American Economic Review85, no. 5 (December 1995): 1138–51.
2In constructing this figure, Ramey and Ramey controlled for a number of factors that could cause differences in growth rates, including initial real GDP, initial human capital, average investment rates, and population growth rates.
387 L E C T U R E S U P P L E M E N T
15-9
How Long Is the Long Run? Part Four
Macroeconomists traditionally decompose the overall behavior of GDP through time into its long-run growth (or trend) and its short-run fluctuations (or cycle). That is the approach followed in the textbook. Chapters 3, 6, 8, and 9 explain the determination of the natural level of output at a point in time and show how the natural level of output grows through time as the economy’s resources and technology change. Chapters 10 to 14 explain how actual GDP may differ from the natural level in the short run because of shocks to aggregate demand combined with an upward-sloping aggregate supply curve (as a result of price stickiness or information imperfections). Thus, Chapters 3, 6, 8, and 9 explain the trend growth of GDP, whereas Chapters 10 to 14 explain the business cycle.
The simple dynamic model presented in Chapter 15 incorporates elements of both short-run business cycle fluctuations and long-run economic growth into a unified framework. It does so by allowing for growth over time in the natural level of output within a model that has sticky prices in the short run. Economists have developed much more sophisticated models, known as stochastic, dynamic, general equilibrium models, in which this traditional decomposition between trend and cycle can be misleading. Both the “short-run” fluctuations in output and the “long-“short-run” growth of output are, according to this view, in part manifestations of the same phenomenon—the response of the economy to technology shocks. To put it another way, output sometimes fluctuates because the natural level of output fluctuates. But it may also fluctuate because of shifts in aggregate demand arising from changes in the money supply when prices are sticky. Hence, DSGE models are hybrids that combine both Keynesian elements and real business cycle elements into a single approach.
L E C T U R E S U P P L E M E N T
15-10
Additional Readings
The Summer 1989 issue of the Journal of Economic Perspectives 3, no. 3, contains two articles on real business cycle theory: one by Charles Plosser, a proponent of the theory, “Understanding Real Business Cycles,” pages 51–77; and one by Greg Mankiw, who is more skeptical, “Real Business Cycles: A Keynesian Perspective,” pages 79–90. A useful, but more technical, survey is B. McCallum, “Real Business Cycle Models,” in R. Barro (ed.), Modern Business Cycle Theory(Cambridge, Mass.: Harvard University Press, 1989).
The Fall 1986 issue of the Federal Reserve Bank of Minneapolis Quarterly Review 10, no. 4, contains a debate on the topic between Edward Prescott and Lawrence Summers. Rodolfo Manuelli’s introduction is also very useful.
Much work on real business cycles has focused on the labor market. For a survey, see G. Hansen and R. Wright, “The Labor Market in Real Business Cycle Theory,” Federal Reserve Bank of Minneapolis Quarterly Review16, no. 2 (Spring 1992).
There are a number of good surveys of the current state of macroeconomics, including Robert Gordon, “What Is New-Keynesian Economics?” Journal of Economic Literature 28 (September 1990); Bennett McCallum, “Post-War Developments in Business Cycle Theory: A Moderately Classical Perspective,” Journal of Money, Credit, and Banking20 (August 1988); Greg Mankiw, “A Quick Refresher Course in Macroeconomics,” Journal of Economic Literature 28 (December 1990): 1645–60; Greg Mankiw and D. Romer, “Introduction,” in G. Mankiw and D. Romer, eds., New Keynesian Economics (Cambridge, Mass.: MIT Press, 1991). The Mankiw and Romer volumes also contain many of the important papers on new Keynesian economics.
The Journal of Economic Perspectives7, no. 1 (Winter 1993), contains a symposium on “Keynesian Economics Today” that includes articles by avowed new Keynesians David Romer, Bruce Greenwald, and Nobel Prize winner Joseph Stiglitz; self-described old Keynesian and Nobel Prize winner James Tobin; and Robert King, who is skeptical of the new Keynesian approach.
