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Business Cycles
If we were to take snapshots of an economy at different points in time, no two photos would look alike. This occurs for two reasons:
- Many advanced economies exhibit sustained growth over time. That is, snapshots taken many years apart will most likely depict higher levels of economy activity in the later period
- There exist seemingly random fluctuations around this growth trend. Thus given two snapshots in time, predicting the later with the earlier is nearly impossible.
A common way to observe such behavior is by looking at a time series of an economy’s output, more specifically gross national product (GNP). This is just the value of the goods and services produced by a country’s businesses and workers.
Figure 1 shows the time series of real GNP for the United States from 1954-2005. While we see continuous growth of output, it is not a steady increase. There are times of faster growth and times of slower growth. Figure 2 transforms these levels into growth rates of real GNP and extracts a smoother growth trend. A common method to obtain this trend is the Hodrick-Prescott filter. The basic idea is to find a balance between the extent to which general growth trend follows the cyclical movement (since long term growth rate is not likely to be perfectly constant) and how smooth it is. The HP filter identifies the longer term fluctuations as part of the growth trend while classifying the more jumpy fluctuations as part of the cyclical component.
Observe the difference between this growth component and the jerkier data. Economists refer to these cyclical movements about the trend as business cycles. Figure 3 explicitly captures such deviations. Note the horizontal axis at 0. A point on this line indicates at that year, there is no deviation from the trend. All other points above and below the line imply deviations. By using log real GNP the distance between any point and the 0 line roughly equals the percentage deviation from the long run growth trend.
We call relatively large positive deviations (those above the 0 axis) peaks. We call relatively large negative deviations (those below the 0 axis) troughs. A series of positive deviations leading to peaks are booms and a series of negative deviations leading to troughs are recessions.
At a glance, the deviations just look like a string of waves bunched together -- nothing about it appears consistent. To explain causes of such fluctuations may appear rather difficult given these irregularities. However, if we consider other macroeconomic variables, we will observe patterns in these irregularities. For example, consider Figure 4 which depicts fluctuations in output and consumption spending, i.e. what people buy and use at any given period. Observe how the peaks and troughs align at almost the same places and how the upturns and downturns coincide.
We might predict that other similar data may exhibit similar qualities. For example, (a) labor, hours worked (b) productivity, how effective firms use such capital or labor, (c) investment, amount of capital saved to help future endeavors, and (d) capital stock, value of machines, buildings and other equipment that help firms produce their goods. While Figure 5 shows a similar story for investment, the relationship with capital in Figure 6 departs from the story. We need better way to pin down a better story; one way is to look at some statistics.
Stylized Facts
By eyeballing the data, we can infer several regularities, sometimes called stylized facts. One is persistence. For example, if we take any point in the series above the trend (the x-axis in figure 3), the probability the next period is still above the trend is very high. However, this persistence wears over time. That is, economic activity in the short run is quite predictable but due to the irregular long-term nature of fluctuations, forecasting in the long run is much more difficult if not impossible.Another regularity is cyclical variability. Column A of Table 1 lists a measure of this with standard deviations. The magnitude of fluctuations in output and hours of worked are nearly equal. Consumption and productivity are similarly much smoother than output while investment fluctuates much more than output. Capital stock is the least volatile of the indicators.
Yet another regularity is the co-movement between output and the other macroeconomic variables. Figures 4 - 6 illustrated such relationship. We can measure this in more detail using correlations as listed in column B of Table 1. Procyclical variables have positive correlations since it usually increases during booms and decreases during recessions. Vice versa, a countercyclical variable associates with negative correlations. Acyclical, correlations close to zero, implies no systematic relationship to the business cycle. We find that productivity is slightly procyclical. This implies workers and capital are more productive when the economy is experiencing a boom. They aren’t quite as productive when the economy is experiencing a slowdown. Similar explanations follow for consumption and investment, which are strongly procyclical. Labor is also procyclical while capital stock appears acyclical.
Observing these similarities yet seemingly non-deterministic fluctuations about trend, we come to the burning question of why any of this occurs. It’s common sense that people prefer economic booms over recessions. It follows that if all people in the economy make optimal decisions, these fluctuations are caused by something outside the decision-making process. So the key question really is: what main factor influences and subsequently changes the decisions of all actors in an economy?
Real Business Cycle Theory
Economists have come up with many ideas to answer the above question. The one which currently dominates the academic literature was introduced by Finn Kydland and Edward Prescott in their seminal 1982 work “Time to Build And Aggregate Fluctuations.” They envisioned this factor to be technological shocks i.e., random fluctuations in the productivity level that shifted the constant growth trend up or down. Examples of such shocks include innovations, bad weather, imported oil price increase, stricter environmental and safety regulations, etc. The general gist is that something occurs which directly changes the effectiveness of capital and/or labor. This in turn affects the decisions of workers and firms, who in turn affect what is bought and produced and thus eventually effect output. RBC models predict time sequences of allocation for consumption, investment, etc. given these shocks.But exactly how do these productivity shocks cause ups and downs in economic activity? Let’s consider a good but temporary shock to productivity. This momentarily increases the effectiveness of workers and capital. Also consider a world where individuals produce goods they consume. This may seem silly but at the aggregate level, this averages out.
Individuals face two types of tradeoffs. One is the consumption-investment decision. Since productivity is higher, people have more output to consume. An individual might choose to consume all of it today. But if he values future consumption, all that extra output might not be worth consuming in entirety today. Instead, he may consume some but invest the rest in capital to enhance production in subsequent periods and thus increase future consumption. This explains why investment spending is more volatile than consumption. The life cycle hypothesis argues that households base their consumption decisions on expected lifetime income and so they prefer to “smooth” consumption over time. They will thus save (and invest) in periods of high income and defer consumption of this to periods of low income.
The other decision is the labor-leisure tradeoff. Higher productivity encourages substitution of current work for future work since workers will earn more per hour today and less tomorrow. More labor and less leisure results in higher output today. More output means greater consumption and investment today. On the other hand, there is an opposing effect: since workers are earning more, they may not want to work as much today and in future periods. However, given the procyclical nature of labor, it seems that the above “substitution effect” dominates this “income effect.”
Overall, the basic RBC model predicts that given a temporary shock, output, consumption, investment and labor all rise above their long-term trends and hence formulate into a positive deviation. Furthermore, since more investment means more capital is available for the future, a short-lived shock may have an impact in the future. That is, above-trend behavior may persist for some time even after the shock disappears. This capital accumulation is often referred to as an internal “propagation mechanism” since it converts shocks without persistence into highly persistent shocks to output.
It is easy to see that a string of such productivity shocks will likely result in a boom. Similarly, recessions follow a string of bad shocks to the economy. If there were no shocks, the economy would just continue following the growth trend with no business cycles.
Essentially this is how the basic RBC model qualitatively explains key business cycle regularities. Yet any good model should also generate business cycles that quantitatively match the stylized facts in Table 1, our empirical benchmark. Kydland and Prescott introduced calibration techniques to do just this. The reason why this theory is so celebrated today is that using this methodology, the model closely mimics many business cycle properties. Yet current RBC models have not fully explained all behavior and neoclassical economists are still searching for better variations.
It is important to note the main assumption in RBC theory is that individuals and firms respond optimally all the time. In other words, if the government came along and forced people to work more or less than they would have otherwise, it would most likely make them unhappy. It follows that business cycles exhibited in an economy are chosen in preference to no business cycles at all. This is not to say that people like to be in a recession. Slumps are preceded by an undesirable productivity shock which constrains the situation. But given these new constraints, people will still achieve the best outcomes possible and markets will react efficiently. So when there is a slump, people are choosing to be in that slump because given the situation, it is the best solution. This suggests laissez-faire is the best type of government intervention but given the abstract nature of the model, this has been debated.
A pre-cursor to RBC theory was developed by monetary economists Milton Friedman and Robert Lucas in the early 1970s. They envisioned the factor that influenced people’s decisions to be misperception of wages -- that booms/recessions occurred when workers perceived wages higher/lower than they really were. This meant they worked and consumed more/less than otherwise. In a world of perfect information, there would be no booms or recessions.
Criticisms of Real Business Cycle Theory
The original Nobel prize winning research by Kydland and Prescott modeled economic fluctuations in a nonmonetary world (hence the name "real" business cycles) with efficient markets. Many researchers felt that this neglected the impact of monetary policy on business cycles, downplayed the role of market inefficiencies, and minimized the importance of unemployment. A flood of subsequent research has introduced monetary policy, market inefficiencies, and unemployment into modifications of the Kydland and Prescott paradigm. The success or failure of Kydland and Prescott's approach for studying business cycles is still an active subject of economic research. Whatever one's view is on the value of their approach for studying business cycle, it is now widely recognized as being one of those rare paradigm-shifting moments in macroeconomics, just as John Maynard Keynes's "The General Theory of Employment, Interest and Money" and Nobel prize winner Robert E. Lucas's "Expectations and the Neutrality of Money" were.
References
- Cooley, Thomas. F. 1995. Frontiers of Business Cycle Research. Princeton University Press.
- Gomes, Joao, Greenwood, Jeremy, and Sergio Rebelo. 2001. "Equilibrium Unemployment." Journal of Monetary Economics, 48, 109-152.
- Hansen, Gary D. 1985. "Indivisible labor and the business cycle." Journal of Monetary Economics, 16, 309-327.
- (Kydland, Finn E. and Edward C. Prescott. 1982. “Time to Build and Aggregate Fluctuations.” Econometrica, 50, 1345-70.
- Lucas, Robert E., Jr. 1977. “Understanding Business Cycles.” Carnegie-Rochester Conference Series on Public Policy, 1, 19-46.
- Plosser, Charles I. 1989. “Understanding real business cycles.” Journal of Economic Perspectives, 3, 51-77.
See also
Lucas critiqueCicles econòmics reals | リアルビジネスサイクル理論 | Realkonjunkturteori | Realkonjunkturteori
"Real business cycles"