This year the Nobel Prize in economics was awarded to Finn Kydland and Edward Prescott (KP) for their contributions in the design of macroeconomic policy and for the identification of the main causes of business cycle fluctuations. Here I discuss their theory of the business cycle and how it stacks up against the Austrian understanding.
Since the Great Depression of the 1930’s and until the early 1970’s most economists viewed economic fluctuations as the outcome of shocks to aggregate demand. Sudden changes in consumer preferences are said to cause a fall in aggregate demand, which in turn drags the entire economy below a path of stable economic growth. In contrast, a sudden increase in optimism leads to excessive consumer expenditure, which in turn pushes the economy above a stable growth path.
Economists regarded these deviations from stable growth paths as failures of the market economy to coordinate demand and supply. Consequently, it was seen as obvious that the government and the central bank ought to interfere in order to bring the economy onto a stable growth path.
In order to enable policy makers to navigate the economy, various large-scale econometric models were developed. These models served as a pseudo laboratory to examine the effectiveness of various fiscal and monetary policies in smoothing out economic fluctuations. The foundation of these models was the Keynesian framework, which regarded aggregate demand as the driving force of the economy. However, with the onset of the 1970’s oil crisis that was followed by a deep recession and a rise in price inflation most large-scale macro-econometric models had difficulty in explaining what was going on. After all, in the Keynesian framework rising price inflation is not compatible with a fall in economic activity.
Some economists have begun questioning the soundness of the theory behind large-scale macro econometric models. Economists like Robert Lucas (1995 Nobel Laureate) held that in the absence of an explicit modelling of consumers and firms, various equations that comprise large-scale models are suspect and hence cannot be used in economic analysis.1
Whilst Lucas identified what is wrong with large-scale macro-econometric models he didn’t provide details as to how to make his framework operational. In short, he didn’t elaborate as to how one provides a micro foundation for macro economic models. KP undertook this task.2
The KP model, which was devised in order to uncover the secret of economic fluctuations, embraces consumer and firms’ behavior. Additionally, KP have hypothesized that a major factor behind economic fluctuations is the technology shock. In order to assess the importance of this insight, KP have employed the Solow growth model (Robert Solow the 1987 Nobel Laureate) which in turn is based on the Cobb-Douglas production function of the following type:
Y=A*K(1-a)*Na
Where Y is real output, A is a technology factor, K is the capital stock and N the number of workers employed. The a is a parameter.
The introduction of microeconomics into model building has led to complex mathematical issues as far as estimations of various parameters are concerned. To overcome these complexities KP have introduced an innovative technique, which they labelled calibration.
What is calibration all about? Instead of estimating parameters by means of conventional econometric methods KP utilize various studies and data analysis to form a view on the numerical magnitude of a parameter. For instance, using the historical data of wages and income KP have established that the parameter a in the Cobb-Douglas production function is around 0.64. By incorporating the information on a with the information on real GDP and the stock of capital one can now extract the numerical values for the technology factor A.
Once the factor A is extracted it can be employed to assess the effect it has on fluctuations of various key economic data in the model. In the framework of their model KP have demonstrated that a technology-induced shock can explain 70% of fluctuations in postwar US data.
The Question
What is the mechanism in the KP framework that converts the technology shock into boom-bust cycles? A positive technology shock according to KP means that with a given supply of capital and labor the economy can now generate more output. Higher productivity leads to higher wages. This in turn raises workers willingness to work more and reduce their leisure. The higher return on capital gives rise to more capital investment. All this leads to economic boom and prosperity. A recession is caused by a negative technology shock, which lowers the return on labor and capital. This in turn leads workers to work fewer hours and to the decline in capital investment. Consequently, this leads to a fall in real output, i.e., to an economic bust.
The Critique
There is, however, no such thing as a change in technology whilst the stock of capital remains unchanged. The implementation of a new idea can only be made possible through the alteration of the capital stock. For instance, with a simple stick John can pick up 10 apples per hour from an apple tree. An innovative idea, which is translated by introducing a special attachment to the stick, can now double the hourly output of apples.
In other words, the implementation of a new idea here implies the modification of the capital production structure. It follows then that technology cannot be separated in some abstract sense from capital goods. If, then, the technology factor cannot be analytically isolated from capital obviously the conclusions that KP have derived from their model are questionable.
Contrary to KP, an economic boom is not about economic prosperity and wealth generation, but about a mechanism that gives rise to activities that are engaged in consumption which is unbacked by the previous production of real wealth, i.e., nonproductive consumption. These types of activities emerge as a result of the diversion of real resources from wealth generating activities and thereby weaken the process of wealth generation. If for some reason the diversion of resources is arrested various non-productive activities that sprang up as a result of this diversion come under pressure, i.e., an economic bust emerges. According to Mises:
The boom squanders through malinvestment scarce factors of production . . . its alleged blessings are paid for by impoverishment. The depression, on the other hand, is the way back to a state of affairs in which all factors of production are employed for the best possible satisfaction of the most urgent needs of the consumers.3
Therefore the key here is to identify the mechanism that gives rise to the diversion of real resources. The only known mechanism that can set in motion a persistent diversion of real resources from productive to non-productive activities is loose monetary policy of the central bank. Hence whenever the central bank loosens its stance it doesn’t generate economic prosperity but economic impoverishment of wealth producers. Whenever a central bank tightens its stance, the diversion of real resources towards various false, i.e., non-productive activities is curtailed. This in turn leads to their demise, or what is called an economic bust.
Since most economic data is measured in monetary terms, it is obvious then that a loose monetary stance manifests itself through the increase in the yearly rate of growth of the data. The reversal of the monetary stance results in the decline in the growth rate of the data. In short, the increase in the rate of growth of money, which leads to a diversion of real resources, also manifests itself through the increase in various economic data. The decline in the rate of growth of money, which slows down or arrests the diversion of real resources, manifests through falls in the rate of growth of various economic indicators.
The heart therefore of what business cycles are all about is the process of diversion of real resources from productive to non-productive activities, which is set in motion by loose monetary policies of the central bank. Models that are preoccupied with finding a good fit with the fluctuations of economic data have, however, very little to say about this process of diversion.
In this regard, what our Nobel laureates have introduced is not a novel way of understanding the phenomenon of the business cycle but a different method of curve fitting. By means of calibration, various types of imaginary models can now be introduced and assessed against real data. If a particular functional form doesn’t fit the data closely enough, then the function can be modified by the introduction of another heavy dosage of mathematics until the proper fit is established. In short, what we have here is a heavy emphasis on developing sophisticated methods of curve fitting rather than trying to identify the essence of what gives rise to the fluctuations of the data.
Since the KP had adopted apriori the view that what matters as far as boom-bust cycles are concerned is technology shocks, they have selected an appropriate model for their story. Therefore, it is not surprising that the KP model has not been able to identify the importance of monetary pumping in setting boom-bust cycles in motion.
Thus they write: “There is no evidence that either the monetary base or M1 leads the cycle, although some economists still believe this monetary myth. Both the monetary base and M1 series are generally procyclical and, if anything, the monetary base lags the cycle slightly.”4
Conclusion
It seems that rather than advancing our understanding of the causes behind boom-bust cycles, this year Nobel laureates have contributed to a further retrogression of the economic discipline. Rather than probing the essence of the economic phenomena, this year’s laureates have further obscured our understanding through the introduction of more complex mathematical tools. Once, however, the veil of mathematics is removed, one finds very little of substance that modern mathematical economists including KP have made in advancing our understanding of the world of economics. Careful scrutiny of the KP model shows that it is just another sophisticated toy for fitting curves, which has very little to do with the explanation of boom-bust cycles.
- 1Robert Lucas. 1976. “Econometric Policy Evaluation: a Critique.” Journal of Monetary Economics, supplement.
- 2Finn Kydland and Edward C. Prescott. 1982. “Time to Build and Aggregate Fluctuations.” Econometrica Nov. No 6.
- 3Ludwig von Mises. 1966. Human Action. Third Revised Edition, Contemporary Books. P. 575.
- 4Finn E. Kydland and Edward Prescott. 1990. “Business Cycles: Real Facts and Monetary Myth.” Federal Reserve Bank of Minneapolis vol. 14 (Spring) 1990.
