J’ai regroupé dans ce seul post toutes les références confirmant empiriquement la théorie autrichienne des cycles économiques. Les passages, calculs et graphiques importants de ces études sont cités. Au total, une dizaine d’articles et une cinquantaine d’images.
Empirical Evidence on the Austrian Business Cycle Theory (James P. Keeler) [PDF]
Theory and Hypotheses
The expectations hypothesis of the term structure of interest rates is that the interest rate on a long-term loan will equal the geometric average of expected interest rates on short-term loans over the intervening time period. Liquidity preference and related theories add a term premium for risk and liquidity adjustments. If market participants are risk averse and prefer more liquid to less liquid assets, the term premium reflects greater risk and less liquidity in long-term credit markets. The yield curve arrays rates of return by maturity of the financial instrument, and in general equilibrium, the yield curve would be characterized by a positive slope as term premiums increase with maturity. Then a monetary expansion will have a liquidity effect that lowers short-term interest rates to a greater degree than long-term interest rates (Romer 1996:395–396). Long-term interest rates are affected since they are an average of short-term rates, but the effect is moderated. Relative to its position and shape in equilibrium, the yield curve can be expected to shift down as both short-term and long-term rates fall, and to become steeper as short-term rates fall relative to long-term rates as a result of the monetary shock.
Measuring Austrian Macroeconomic Concepts
Real income can be conceptualized as occurring in relation to a natural income, where actual real income rises relative to natural real income and then declines through the cycle. The ratio of actual to natural real GDP allows for change during the cycle and variable growth rates of natural income across cycles. Figure 1 displays quarterly data on the ratio of actual real GDP to an estimate of natural real GDP for each of the eight U.S. cycles since 1950.
An important relative price is that between present and future consumption, expressed in the structure of short-term and long-term interest rates. The yield curve illustrates different interest rates by term, and also the notion of changes in relative prices as the level and slope of the yield curve change. Consider the short-term interest rate as representing the market rate in a short-term credit market and the long-term interest rate as representing the natural rate for the long-term market. Then the slope of the yield curve is offered in this analysis as a measure to capture Wicksell’s concept of the interest rate differential. In the disequilibrium created by the monetary shock, short-term rates differ from the long-term rates by more than the risk and liquidity premiums justify, and the yield curve is steeper than in general equilibrium. […] The yield curve is expected to shift down early in the cycle as both short-term and long-term rates fall, and to shift up in the recession phase as both rates rise.
Figure 2 displays the slope of the yield curve, again with the peaks of the cycles aligned in time on the horizontal scale. Despite the variation across cycles, a pattern emerges that in the expansion phase, the short-term rate is low relative to the long-term rate, and late in the expansion and in the recession, the short-term rate rises relative to the long-term rate.
Most of the cycles in Figure 2 display steep, positively-sloped yield curves initially, which flatten or invert over the course of the cycle. […] Just after the peak, which occurs at quarter 26 in the graph, all the cycles show a sharp increase in the slope of the yield curve. All of the cycles except the 1980–82 cycle and perhaps the 1961–70 cycle have remarkably similar slope values at the beginning and show convergence to a flat or inverted slope at the peak of the cycle. Both Figures 1 and 2 reveal the variety of business cycle experience within these basic patterns, in length, amplitude, and time profile.
Bernanke (1990) surveys several interest rate concepts, and suggests that the changes in short-term rates, and especially the interest rate spread, are dominated by monetary policy and current default risk conditions. His correlations are confirmed by the data set for this study: levels of short-term interest rates are more highly correlated with the measure of money supply growth than are long-term interest rates (0.29–0.33 compared to 0.06–0.20). Bernanke found that two measures of the yield curve, the interest rate spread and the slope of the yield curve, were highly correlated with his measures of monetary policy. For the current data set, the slope of the yield curve shows the highest correlation with money supply growth at 0.55. Short-term rates are volatile and directly influenced by current credit market conditions.
Data for the eight U.S. business cycles show both nominal and real long-term interest rates to be comparatively stable during each cycle. Most cycles show a slight cyclical pattern of a slow and steady rise in the level of the rate through the business cycle peak and then a decline, but three of the eight cycles have long-term rates that are either flat or decrease steadily. Only two cycles exhibit a rise in long-term rates at the end of the cycle. Most cycles show nominal and real long-term rates in a similar range with the exception of the 1980–82 and 1983–91 cycles, which have much higher levels. A variety of inflation adjustments calculates a few negative real long-term rates during the 1950–54, 54–58, 71–75 and 75–80 cycles. The expectations hypothesis implies a pattern of a shift down of the yield curve at the start of the cycle followed by a shift up during the recession, and that is not apparent across the eight U.S. cycles. There is no consistent evidence that long-term rates respond to monetary shocks.
The yield curve slope exhibits more regularity across cycles, in the magnitude of the slope, and patterns of change through the cycle than do the levels of interest rates.
The variable definitions and data sources are presented in Table 2
For each cycle in the Table, the first row presents statistics on the original quarterly data, the second row is for the standardized data with 17 periods for each cycle, and the third row is for data constructed according to the NBER method with 9 observations (or stages) per cycle.
Means for the standardized data are uniformly closer to those of the original data than are the NBER data. As a weighted average, the standardized series exhibits slightly less volatility on every cycle and throughout the sample. In contrast, the NBER reference cycle data are more volatile for cycles 1, 2, 5 and 7 but less volatile for others and for the series overall.
Judged by these summary statistics, the method of standardizing time periods retains more information and performs better than the NBER reference cycle method at preserving the time series characteristics of the original data. The data transformed for a standardized time period are used to describe patterns of relative price changes, resource utilization and income that are common over the eight business cycles.
Descriptive statistics for the variables used in the analysis are given in Table 4, for each cycle as well as the entire sample of 136 observations (eight cycles of 17 periods each). All the variables display significant differences in mean and all except INCOME display significant differences in variance across the eight cycles. No further adjustments were made for these differences. The variations in means for different cycles do not indicate a trend in any of the four variables across the full sample.
Empirical Evidence on the Austrian Business Cycle Facts
Each variable was tested for the presence of a unit root. The null hypothesis of the available tests is that the variable has a unit root, and if true the behavior of the variable would be consistent with a random walk process. Then the changes in the value of the variable from one time period to the next would show non-stationarity. The Augmented Dickey-Fuller test was specified with only a constant since all series exhibit no trend but a non-zero mean, and the Phillips-Perron test was specified with a constant and a trend. Tests were performed with four lags. Results are presented in Table 5, including alternative measures of YIELD. The null hypothesis of a unit root is rejected at the 5% level of significance for all variables. The time trend variable is not significant for any of the variables at the 5% level in either the ADF or Phillips-Perron test. Rejecting the null hypothesis of a unit root implies that the variable does not follow a random walk process. The probability of Type II error is large for these tests, but the results, considered with the correlograms, support the notion that the variables are stationary in the forms presented in Table 2, despite the variation in means and variances across cycles.
Percentage changes in the real money supply are positively and significantly correlated with changes in the slope of the yield curve […] for the current period and next two periods, reflecting a short but strong liquidity effect. The steeper yield curve is correlated with a rise in the capacity utilization in primary processing industries relative to that in advanced processing industries. The correlations between YIELD and CAPACITY are positive and statistically significant for the third period through the eleventh period. Thus the response requires several periods to appear, as might be expected, and then is consistent through the cycle, tapering off in the final five periods. The cross-correlations do not show significant reversals of correlation within the cycle, but a steeper yield curve is consistently correlated with higher capacity utilization in primary production processes.
Table 6 shows that these lags are extended by each relationship. Granger causality tests show few significant F-values and no consistent effects.
The evidence of cross-correlations indicates several patterns. One cause of a business cycle, common to many theories, is a strong liquidity effect, and that is apparent in the relation between money supply growth and the interest rate differential. As in Figure 2, the expansion phase of the cycle is characterized by a steep yield curve and a gap between the market rate and the natural rate of interest. Resource allocation and income respond to price signals, and especially the relative price of current and future consumption expressed in the interest rate term structure. The cross-correlations of YIELD with CAPACITY and with INCOME confirm that relation.
An Error Corrections Model (ECM), using data on short-term and long-term interest rates, provides confirmation of this relation between the interest rate differential and the business cycle.
On the assumption that there is a long-term proportional relation between short-term and long-term interest rates, the change in the short-term rate is modeled as a function of the change in the long-term rate, with a short-term adjustment mechanism based on the difference between the rates, the interest rate spread (SPREAD). The adjustment coefficient may depend on business cycle influences such as the growth rate of the real money supply or the relation between actual and natural real GDP.
Table 7 presents the results for private market and for government securities interest rates. Significant F-values indicate the model contributes to the explanation of short-term rates, though the adjusted R² values show that a substantial portion of the variation is not explained. The simple ECM has statistically significant coefficient estimates for the change in the long-term interest rate and the short-term adjustment component.
Austrian Business Cycle Theory : Evidence from Scandinavia (Rasmus Anker) [PDF]
The empirical analysis of business cycle theory in Scandinavia will include countries Denmark, Norway and Sweden with the economic data collected ranging from first fiscal quarter of 1980 to fourth fiscal quarter of 2010.
First of all, the correlation between the money supply and the consumer price index is 0.91 – close to perfect.
Bisman & Mougeot proposes Keelers (2001) use of fluctuations in aggregate economic activity, with the ratio of the real GDP level relative to its natural level to define business cycles. The natural GDP is defined as the real GDP which would have been observed if it had increased at its long term rate of growth, and is calculated by using the method of Hodrick & Prescott (1997) – the Hodrick-Prescott filter.
In the expansionary phase real GDP rise relative to the natural level and the ratio and moves above 1, whereas in the recession phase the real GDP decreases relative to its natural level and the ratio moves below 1.
A higher interest rate influence consumer patience as it motivates consumers to defer present consumption to the future. A low interest rate removes the incentive for saving as the payoff or reward of doing so is smaller. The data in use are; consumption aggregates as a percentage of total economic activity (consumption) and the investment percentage is calculated as 1 – consumption. The ratio is then calculated as consumption divided by investment.
This ratio also captures the fluctuations in the changes of final goods production and capital goods production, and is an indicator of the production structure distortion through business cycles as proposed by the Hayekian Triangle. The ratio tends to increase during the last part of expansion and tends to decrease at the end of recessions. Figure 4.3 shows the investment pattern most clearly in the later part of the figure compared to figure 4.1. The pattern of the variable characterizes the more roundabout production process with a relative increase in capital goods production in the expansionary phase followed by a rise in consumption goods production. The maxima of the ratio are reached during the recession or during the quarters just after recessions, supporting the Austrian hypothesis that malinvestment liquidation concludes the cycle (Bismans & Mougeot, 2009).
The third approximation is expressed in the ratio of consumption price index to production price index as an indication of relative prices through the cycle caused by monetary shocks. […] Relative prices are distorted by changes in the market interest rate and when the interest rate is lowered, demand for capital goods increase and capital goods prices goes up relative to final good prices (Keeler, 2001). […] The increases or decreases in the ratio do not correspond to with phases of expansion or recession when compared to figure 4.1.
The fourth and last approximation is the term spread between the long term interest rate minus the short term interest rate. The long term interest rate is expressed in the 10 year government bond rate and the short term rate is expressed as the 3-month or 90-day interbank rate. In the case of a credit expansion the distance between the long and short term interest rate increases and the value of the spread is numerically higher. In case of a credit contraction the distance between them is reduced and the value of the spread is smaller.
Credit expansion lowers short term rate relative to the long term rate, and the spread rises at the beginning of the expansion phase and then gradually decreases and turns negative in the quarters just before a recession (Bismans & Mougeot, 2009). This implies that the turning point of aggregate economic activity is when the spread becomes negative. Also, when the spread decreases production becomes less roundabout as resources are allocated away from capital goods to consumption goods.
The remarkable negative spread in figure 4.6 is a consequence of the ERM crisis of 1992 and 1993. More interestingly the figure shows a pattern where shocks increase the spread and then gradually declines and becomes negative, before experiencing another sharp increase. This can be seen most clearly in the period between 2002 and 2010.
The econometric approach to analyze the proposition of the Austrian business cycle theory is made […] by estimating the long run relationship between the fluctuations of economic activity – the ratio of real GDP to natural real GDP (Rev) – and the ratio of consumption expenditure to investment expenditure (Dep), ratio of consumption price index to production price index (Prix_rel) and the yield spread between the long term interest rate minus the short term interest rate (Spread).
The variables; Rev, Dep, and Spread all show significant test statistics implying a rejection of the null hypothesis. These three variables are thereby stationary variables and ready to be included in the fixed effects model. For the variable Prix_rel though, we fail to reject the null hypothesis meaning the variable is non-stationary and dependent of time. To be able to include the variable in the fixed effects model it must be stationary which is taken care of by the method of first order differencing. As can be seen from the table, the variable Prix_rel is stationary when included in first order – denoted by ΔPrix_rel.
According to Austrian Business Cycle Theory, the influence of the two significant variables, Dep and Spread, is expected to have a negative effect which is empirically confirmed by the negative signs of the coefficients in the model. Both significant variables in the fixed effects model have negative coefficients when regressed on the dependent variable. First, an increase in the ratio of consumption expenditure to investment expenditure has a negative impact on the ratio of actual real GDP and natural real GDP. When consumption expenditure grows relative to investment expenditure the difference between actual real GDP and natural real GDP is reduced with economic activity accelerating. An expansionary phase implies a relative increase in capital goods production followed by an increase in consumption goods production.
When the cycle turns into a recession natural GDP then outgrows actual GDP resulting in liquidation of malinvestments. Second, an increase in the term spread also means a decrease in the ratio of actual real GDP to natural real GDP. Monetary expansion lowers the short term interest rate which widens the gap between the long term rate and the short term rate. When the term spread increases it brings real economic activity to accelerate compared to its natural GDP until the short term market interest rate converges to its natural level.
Post-estimation verifies the fixed effects model and the results can be seen in appendix E. The Hausman test for testing the fixed effects model against a random effects model shows a strong significance in favor of the fixed version. Serial correlation test shows that there is no serial correlation in the residuals with the test unsuccessful in rejecting the null hypothesis for just that. Wald tests for testing the linear relationship of the explanatory variables yields significant results for the consumption to investment ratio and the term spread but not for the relative price ratio as expected. Finally, testing for the country specific effects, results in a rejection of the null hypothesis that these are jointly significant.
The consumption to investment ratio and the interest rate spread both have similar significant effects on the dependent GDP ratio variable, while the relative price ratio does not. The coefficients for the two significant variables in the present paper are of higher magnitude, alas both the coefficient of determination and the joint significance statistic are about half of those in the Bismans & Mougeot article. All in all the same conclusion apply for this analysis of the three Scandinavian countries; that monetary shocks have a significant impact on aggregate economic activity as defined by the ratio of actual GDP to natural real GDP.
Austrian business cycle theory: Empirical evidence (Francis Bismans & Christelle Mougeot) [PDF]
The phases of expansion are characterised by rises of the GDP relative to its natural level, whilst the recessions are defined by decreases in the GDP relative to its natural level. For Keeler (2001: 337), “[the ratio of actual to natural real GDP] incorporates the change in the equilibrium and serves as a more appropriate measure of an Austrian concept of aggregate economic activity”.
In this perspective, the present paper uses the ratio of actual real GDP growth rate to natural real GDP growth rate as the indicator of economic fluctuations observed in France, Germany, Great Britain and USA between 1980 and 2006. As Keeler (2001), the real natural GDP is defined as the real GDP which would have been observed if the economic activity would have progressed at a constant growth rate between business cycle peaks.
… At the equilibrium, their ratio is equal to 1. Any deflection compared to 1 corresponds to a change of economic activity through the cycle, as illustrated by Fig. 1.
… The ratio of consumption expenditure to investment expenditure is used as an indicator of the production structure distortion trough the cycle. Figure 2 shows that in each country, this ratio tends to increase during the last steps of expansions and to lower at the end of recessions. This pattern seems to be true to the Austrian hypothesis according to which the beginning of expansion is characterised by a relative increase of capital goods production, whilst the rise of consumption goods production accelerates later. Figure 2 also shows that the maxima of the ratio of consumption expenditures to investment expenditures are often reached during the quarters of recession or during the quarters just after recessions. This observation corroborates the Austrian hypothesis of overinvestment liquidation marking crisis.
Figure 3 shows the evolution of the ratio of consumption price index to production price index. The absence of determined changes in the structure of relative prices through the cycle can be observed. This ratio tends to increase independently of aggregate economic activity fluctuations or production structure changes.
Keeler (2001) and Mulligan (2002, 2006) consider that “observed long-term rates are a good representation of natural interest rates, and especially that the slope of the yield curve represents the Wicksellian notion of the differential between market and natural rates” (p. 340). Since long-term rates are equal to the weighted average of short-term rates plus a risk premium, credit expansion lowers short-term rates higher than long-term rates. So, the term spread — long-term minus short-term interest rates — rises at the beginning of the artificial boom, then gradually decreases and becomes negative during the quarters just preceding a recession. In other words, the term spread inversions mark the turning points of the aggregate economic activity. When the term spread decreases, the structure of production becomes less roundabout as entrepreneurs reallocate resources away from production goods to consumption goods.
The present paper defines the term spread as the difference between the 7/10-year maturity Treasury bond rate and the 3-month market rate. Figure 4 illustrates its evolution which tends to be true to that of Austrian theory. At the beginning of expansions, the short-term interest rate is smaller than the long-term interest rate (the difference is one of the order of two to four points). Then, the difference filled itself and is reversed before recessions.
… Temporary credit expansions involve decreases in short-term interest rate that slightly influence long-term interest rate. There is no consistent evidence that long-term respond to monetary shocks. This hypothesis is empirically confirmed by Keeler (2001: 339): “Levels of short-term interest rates are more highly correlated with the money supply growth than are long-term interest rates (0.29 compared to 0.06)”.
5 Econometrics methodology
The present paper aims to estimate a long-run relationship between the changes of the variable representing the fluctuations of economic activity through the cycle — the ratio of actual real GDP to natural real GDP (noted Rev) — and the term spread (Spread), the ratio of consumption expenditure to investment expenditure (Dep) and the ratio of consumption price index to production price index (Prix_rel).
6.2 Estimated equation
According to the t test and the p value, the coefficients of the constant, Spread and Dep are significantly different from zero. The F test leads to reject the null hypothesis and, so, to accept the alternative hypothesis that these parameters are jointly different from zero.
The present paper proposes a regression of three variables, the term spread (Spread), the ratio of consumption expenditure to investment expenditure (Dep) and the ratio of consumption price index to production price index (Prix_rel) on a variable representing the Austrian business cycle, the ratio of actual real GDP to natural real GDP (Rev). Only the information content of the term spread and of the expenditures structure is significant. […]
The hypotheses concerning the path of the term spread and of the relative expenditures are empirically confirmed. According to the Austrian business cycle theory, their influence is expected to be negative. This result is obtained empirically with the negative sign of the coefficients. … In other words, the growth of the difference between the long-term and the short-term interest rates tends to lower the difference between actual real GDP and natural real GDP. This lowering reflects an acceleration of the economic activity to meet its natural level. This result confirms the main Austrian hypothesis that expansion is created by a decrease of the interest rate under its natural level and lasts until the market interest rate moves towards the natural interest rate.
An Empirical Examination of Austrian Business Cycle Theory (Robert Mulligan) [PDF]
Using 1959–2003 monthly data, the relationship between real consumable output and the interest rate term spread is examined. The term spread used is the ten-year constant maturity Treasury bond rate minus the three-month Treasury bill secondary market rate. This spread is often used as a measure of the real interest rate (Keeler 2001). When the term spread decreases, the structure of production becomes less roundabout as entrepreneurial managers reallocate resources away from producers’ goods toward consumers’ goods.
Whenever interest rates rise, higher rates of return in production are necessary to compete with financial instruments, such as relatively higher-yielding government bonds. This is manifested in a shifting of resources away from early stages of production to later stages, and can be shown as a shortening of the base of the Hayekian triangle.
2. The Austrian Theory of the Business Cycle
Capitalists opt for their own productive activity if they expect the return will be higher than could be realized through lending, and lend out the savings if the market interest rate exceeds the return to their own production. Thus, if the interest rate falls, less money will be lent out and more will be used to finance productive activities, and vice versa. […] In response to lower interest rates, firms shift productive activity into predominantly long-term projects with lower expected returns.
The lower the interest rate, the less consumers save and the more they consume. […] Any policy-induced lowering of the interest rate must simultaneously increase consumption spending, lowering saving, as well as increase investment in productive activities.
When the interest rate rises, capitalists should liquidate their own productive activities to the extent possible, and lend the money out to take advantage of the higher return. However, physical capital comprises illiquid assets, and once savings is invested in productive activities, it cannot be extracted without delay and loss of value. Physical or installed capital is characterized first by its complementarity with other components of an entrepreneurial production plan, and only secondarily by its substitutability in alternative plans […]
Under the influence of an expansionary monetary policy […] the supply of loanable funds is increased above the amount saved by households, by the amount of additional credit injected by the central bank.
As a rule more illustrative than actually descriptive, the need for additional complementary resources for production is approximately proportional to the amount already in use, for example, the amount of physical capital already installed. Thus more capital installed means more additional resources required, so the demand for additional credit accelerates. If the supply of additional credit remains steady as the demand for it increases, the interest rate must rise.
If the term spread is interpreted as a measure of the real interest rate, the cumulative term spread can be interpreted as the real return over time, which is then put in natural logarithms.
6. The Vector Error-correction Model
The OLS estimate also allows for a test of the hypothesis that a lower interest rate accompanies a permanently lowering of the level of real consumable output, the key assertion of Austrian business cycle theory. This interpretation assumes that interest rates fall only due to expansionary monetary policy and not due to general lowering of time preference. The adjusted R square is 97 percent. The intercept and coefficient on the cumulative term spread are both positive and significant. Coefficient values of 6.862 for the intercept and 0.162 for the slope indicate that a one percent increase in interest rates permanently raises consumption expenditure by 955.3 billion chained 1996 dollars each month the higher interest rate persists.
Perhaps more revealingly, a one-percent decrease in the cumulative term spread, such as results from policy induced monetary expansion, has on average decreased real consumable output over the long run by the same amount. The results of the t-test on the cumulative term spread provide strong empirical confirmation of Austrian business cycle theory. This amount is more than great enough to account for any historic recession. Further, the output measure used here, real consumption expenditures, comprises only approximately 70 percent of GDP, thus any impact on real consumption implies a somewhat greater impact on total real output.
The estimate of the vector error correction model (VECM) is reported in Table 5. To facilitate interpretation, the VECM is normalized with respect to and solved for consumption. Estimated coefficients of the cointegrating equation are similar in sign and magnitude to those found by OLS. The VECM intercept and slope coefficients 7.120 and 0.136, indicating a one-percent decrease in the cumulative term spread decreased real consumable output by approximately 1.2 trillion 1996 dollars for every month the term spread falls. (7)
This is significantly greater than the amount indicated by OLS, but the two estimates are reasonably consistent. The t-test on the VECM estimate of the structural equation provides further evidence in support of Austrian business cycle theory’s key assertion that lowering the real interest rate lowers real consumable output over the long run.
(7) The impact of the interest rate on consumption is evaluated by taking the slope coefficient estimate of 0.136, multiplied by 1.0 representing a decrease (increase) of the interest rate by 1 percent for one month, and taking the antilog of 0.136, which equals a 1.15 billion chained 1996 dollar loss (gain) in consumable output (consumption spending) for every month the cumulative term spread is lowered by 1 percent. The longer the interest rate is kept 1 percent below the sustainable market rate, the greater the impact on the cumulative term spread and thus on real output. See table 6.
Table 6 shows the literal implications of the coefficient estimates. The clear implication is that whenever the term spread has been lowered significantly below its average value, real consumable output is permanently lowered by a significant amount.
Graphs of the impulse response functions are presented in Figure 2. The upper-right-hand graph is the one of interest for Austrian business cycle theory. It indicates that over the period studied, a one standard-deviation increase in the term spread has resulted, on average, in an upward adjustment of approximately .004 in the logarithm of consumption, equivalent to 1.004 billion 1996 dollars after eight years or 96 months. A one standard deviation decrease in the yield spread decreased real consumable output by an equivalent amount, on average.
Graphs of the variance decomposition functions are presented in Figure 3. Again, the upper-right-hand graph is the one of interest for Austrian business cycle theory. This graph indicates that after eight years or 96 months, nearly 45–50 percent of the variance in real consumption expenditures has been attributable to variation in the cumulative term spread, over the period studied.
The Austrian Business Cycle: a Vector Error-correction Model with Commercial and Industrial Loans (Robert Mulligan) [PDF]
ABC theory suggests that credit expansion shifts resource allocation away from middle stages of production toward early and late stages (consumption). The suggested interpretation is that the shifting of resources from middle to early stages has little or no net effect on aggregate investment, while the concomitant shifting of resources from middle to late stages, that is, into consumption spending, has a negative impact on investment.
Taken together, the two reallocative movements brought about by credit injection have a negative impact on investment spending, and this negative impact has historically exceeded any net additions to investment coming directly from the injection of newly-created credit.
Adjusted R-squares for the disequilibrium adjustment processes in the cointegrating vector are very low. In spite of the low R-squares, disequilibrium adjustment terms [Θ, Ψ, and Ξ] are significant at the 5% level, only in the disequilibrium adjustment process for consumption.
This is an especially interesting result, which is easy to account for according to ABC theory. Apparently market disequilibria, measured by non-zero residuals in the three structural equations, effect correction chiefly through changes in consumption spending. Below-equilibrium consumption, measured by positive residuals in the consumption equation, results in positive adjustments to consumption accompanied by decreases in industrial output and investment, as indicated by significantly negative coefficients on the disequilibrium adjustment terms.
Consumption itself adjusts upward, as indicated by the significantly positive coefficient. Little or no adjustment occurs through total output or through investment, suggesting that credit-induced increases in consumption generally occur at the expense of investment and output, rather than as additions to them. Consumer behavior is highly responsive to market disequilibria, but producer behavior exhibits much more inertia, likely due to the fixed capital embodied in the production structure.
c. Disequilibrium adjustment
Impulse response functions measure the strength of the disequilibrium adjustment processes working through each variable. In contrast to the structural coefficients of the cointegration space, the disequilibrium adjustment process is a short-run phenomenon. The fact that disequilibrium adjustment is effected downward on output, consumption, and investment, whenever there is a positive shock to commercial and industrial loans, is strong support for ABC theory.
Cumulative impulse response functions illustrate that increases in commercial and industrial loans force a large downward adjustment on output (40 standard deviations after four years), consumption (25 standard deviations after four years), and investment (12 standard deviations after four years). A one standard-deviation increase in the commercial and industrial loans index results in the other variables in the system to adjust to restore equilibrium. Although the investment index rises initially by as much as 0.36 standard deviations after twelve months, it falls drastically from there on.
The index of industrial production remains almost unchanged for about twelve months and then falls dramatically, and the consumption index adjusts downward after only three months. The interpretation suggested by ABC theory is that credit expansion, manifested by exogenous shocks to commercial and industrial loans, causes scarce capital resources to be misallocated over an unsustainably long and low-yielding production structure.
Too much capital is allocated to early and late stages, with too little allocated to the critical middle stages which are necessary to transform early stage goods-in-process into late-stage consumable output. This culture of waste and misallocation permanently shifts the economy into a lower growth trajectory. (10)
(10). Variance decomposition functions indicate that after 48 months, approximately 20% of the variance in industrial production, consumption, and investment is attributable to variation in business and commercial loans over the period studied. Interestingly, while significant variation seems to transfer from industrial production to commercial and industrial loans, very little variation seems to be transmitted from consumption or investment to commercial and industrial loans.
A Hayekian Analysis of the Term Structure of Production (Robert Mulligan) [PDF]
Unit Root and Cointegration Tests
Most economic time series display an increasing trend, and unit root tests were developed to identify this characteristic. Stationary time series are said to have zero roots, or to be integrated of order zero [I(0)]. Nonstationary series may have a unit root or be first-order integrated [I(1)]. Unit root series become I(0) when first-differenced. Regressions estimated with nonstationary data will not have the white-noise residuals needed for valid inference.
The regression could be estimated in first-differences, but then any long-term information carried by the levels of the variables is lost. Error-correction models overcome this difficulty by estimating a regression in first-differences augmented by error-correction terms, the lagged differences between the actual and estimated value of the left-hand-side variable, collectively referred to as the error-correction process, or the disequilibrium adjustment process. The coefficients on the first-differenced variables constitute the cointegrating vector or structural relationship. A sufficient number of lagged error-correction terms is added to guarantee white-noise errors and valid inference (Davidson and McKinnon 1993, pp. 720–30; Kennedy 1998, pp. 266–70).
Estimation with error-correction models is of special interest to the Austrian School because under ideal conditions, the technique provides estimates of both a structural or equilibrium process — which adjustment is always effected toward, though it may never be perfectly realized — and the error-correction or disequilibrium adjustment process, through which adjustment is made toward the hypothesized equilibrium. Even if one rejects the reality of any hypothesized equilibrium, estimates of the disequilibrium adjustment process would still carry interest.
Because of the length of the data set, the Johansen-Juselius (1990) procedure was used to identify stable, long-term relationships among the sectoral employment and interest rates.
Table 1 reports Phillips-Perron (1988) unit-root tests for each variable. All are found to have unit roots. When the test regression is estimated with an intercept but no trend, the null hypothesis of a unit root [I(1)] is never rejected at conventional significance levels for employment levels calculated as percentages of the labor force, or for the interest rates. When a linear trend is added to the test regression, the same result is observed, except for mining, where the I(1) null is rejected at the 5-percent level, but not at the 1-percent level.
The finding that mining may be I(0) indicates it might be deleted from the vector error-correction model, but mining was retained in the model because it remains a part of the nation’s employment statistics, though declining in importance throughout the sample period, and because there is some support for the conclusion that mining is I(1). The null hypothesis of a unit root is always rejected for the first-differenced series, demonstrating all are integrated of order one [I(1)] and not of higher order.
Somewhat surprisingly, the interest rates are found to be I(1). A priori, interest rates are expected to be I(0). This would not present any difficulty for interpreting vector error-correction models that include I(0) interest rates and I(1) employment rates, because in order for the I(1) employment rates to enjoy stable, long-run relationships with any I(0) series, the employment rates would have to be cointegrated. Since more than five cointegrating vectors are found, there must be some cointegration among the employment rates.
Table 2 reports Johansen-Juselius tests for cointegration. The data matrix includes the nine sectoral employment rates and the five interest rates, a total of 14 variables. Results provide very strong evidence of cointegration and indicated very strong, very stable, cointegrated relationships among 13 cointegrating vectors.
Likelihood ratio tests were unable to determine the optimal lag length for the disequilibrium adjustment process. Thirty lags were used to ensure white-noise residuals and unbiased estimates of the long-run structural process measured by the cointegrating vectors. Because the estimate may have used shorter than optimal lag structure, the estimate of the disequilibrium adjustment process is merely suggestive. Attention therefore focuses on the structural or equilibrium process.
The Cointegration Space
From a Hayekian perspective, two sets of cointegrating vectors are of interest:
1. Five cointegrating vectors normalized with respect to the five interest rates. These five equations indicate how employment in each of the nine industrial sectors responds to changes in each interest rate.
2. Nine cointegrating vectors normalized with respect to sectoral employment. These nine equations would indicate how changes in the different interest rates affect employment in each sector.
The five-equation model, where the dependent variables are interest rates, is reported in Table 3. Each cointegrated vector is normalized with respect to, and solved for, different interest rates with maturities ranging from six months to five years. Cointegrated vectors are solved for the interest rate to which they are normalized. This facilitates interpretation of the coefficients by producing opposite signs for each coefficient, compared to how most statistical softwares represent cointegrated vectors.
Results are unambiguous. No mixed signs are observed for different-maturity interest rates for any employment sector. Many of the coefficients are statistically significant at conventional levels; thus the reported equations and coefficient estimates possess significant information content. The coefficients are interpreted as inverse elasticities of employment with respect to interest.
Hayekian Classification of Industrial Sector, by Stage of Production
A positive relationship between an employment rate and an interest rate indicates a late stage of production; a negative relationship indicates an early stage. Table 3 indicates that mining; transportation and utilities; retail trade industry; and wholesale trade industry are late stages of production, and that manufacturing; construction; finance, insurance, and real estate; government; and services, are early stages. The one odd finding is that mining is late stage of production.
Cointegrating vectors always have negative coefficients on employment in manufacturing; construction; finance; government; and services, indicating employment in these sectors falls whenever interest rates rise.
Negative inverse-elasticities of employment indicate early stages of production. For manufacturing, none of the negative inverse-elasticities are significant at conventional levels. This result suggests early-stage production but does not provide convincing evidence.
In contrast, for construction and government, all the negative inverse-elasticities are statistically significant, providing strong evidence that these are early stages. For services, negative inverse-elasticities are significant for three-year and five-year interest rates, but not for shorter maturities. This indicates early-stage production, but also indicates that employment in services responds only to long-term interest rates, which is not surprising for early-stage production, which must be more roundabout by definition.
For finance, insurance, and real estate, negative inverse-elasticities are significant for three-month and six-month interest rates, but not for longer maturities. This outcome is very interesting. Statistical evidence supports the characterization of the financial sector as an early stage of production, but in contrast to the service sector, resource employment in the financial sector responds only to short-term interest rates.
One reason for the difference between services and finance, which are both early stages of production, may be the lack of capital intensity in the financial sector. Service-sector producers maintain large and expensive capital stocks and are less free to adjust the size of their workforces.
Financial, insurance, and real estate employers are less dependent on capital equipment and do adjust workforce size very quickly to accommodate changing business climates. In addition, finance employers face demand that responds more directly to short-term interest rates than demand faced by service employers.
Transportation, wholesale trade industry, retail trade industry, and mining have positive inverse-elasticities, which are always significant at conventional levels, across all maturities. This indicates unambiguously that these four sectors are late stages of production. The result is not surprising for transportation, retail, and wholesale, but it is somewhat so for mining.
If any sector could be identified a priori as early stage of production, it would be mining. However, many high-value mining activities like petroleum production and field services are clearly late stages, temporally if not conceptually.
If less aggregated employment data were readily available for mining, it would be possible to test whether this accounts for the late-stage finding and whether the data confirm that refining is later-stage than mining strictly defined.
Mining consists of slow and fast activities, just as slow-growing trees represent higher-order, earlier-stage production, and fast-growing trees are lower-order and late-stage. For example, coal may be delivered and burned to heat homes relatively quickly after being mined, with a minimum of intermediate processing, which would constitute late-stage production.
The same mine could also produce coal for steel mills and other sophisticated concerns that use the coal in complex and time-consuming industrial processes, a form of early-stage production. Thus the same production can be either early or late stage depending on whether the output is sold to a final consumer or another firm.
It may simply be that fast, late-stage mining activities predominate, or, because labor data is examined here, that fast mining activities are merely more labor intensive.
A further possibility may be that when interest rates fall due to credit expansion, mining concerns find it easier to work existing infrastructure to capacity and possibly beyond, increasing both output and labor employment, than to expand the infrastructure. If this were the case, mining firms would act more like late-stage producers, even while producing primarily in early stages. Their behavior would be atypical and largely attributable to the circumstance that the primary way to expand early-stage mining operations would be to construct new mines, not expand existing ones.
Interestingly, standard errors for wholesale are typically over three times greater than for retailing. This supports the common-sense conjecture that retailing is more clearly late stage of production than wholesale.
Is Austrian Business Cycle Theory Still Relevant? (Carilli, Dempster & Rasmussen) [DOC]
Our model represents an attempt to retain the strengths of the Keeler (2001) study, particularly the focus on the relationship between market rates, the natural rate, and output gaps, while avoiding the pitfalls inherent in using actual rates, which can be manipulated by central bank authorities, as proxies for identifying adjustments with respect to the underlying natural rate.
The model can be expressed in reduced form as the following sequence of events:
ΔReserves → Δ(Natural Rate – Market Interest Rate) → ΔOutput.
We obtained quarterly data on consumption, savings, money supply, central bank policy, and Gross Domestic Product (GDP) for Japan and the United States for the periods 1981-1996 (Japan) and 1980-1999 (U.S.). Money supply figures were for M2 in Japan and MZM in the U.S., while the proxies chosen for central bank policy were the Japanese discount rate and the U.S. federal funds rate. Based on the simple model expressed above, we developed tests of the following linkages.
Japan: Discount Rate → Interest Rate Gap (1)
Interest Rate Gap → GDP (2)
U.S.: Federal Funds Rate → Interest Rate Gap (3)
Interest Rate Gap → GDP (4)
The interest rate gap is defined as the difference between the “natural” rate of interest and the actual market interest rate. However, because this difference is unobservable, it is necessary to proxy the gap. We chose the consumption-savings ratio as the appropriate interest rate proxy based on the analysis in Rothbard’s analysis of the relationship between this ratio and the level of time preference (see Rothbard 1993, p. 342).
First, after determining that consumption-savings ratios were trend stationary, we detrended the ratio accordingly to obtain the long-term average (predicted) values. Second, the residuals from this detrended series were indexed to the appropriate money supply series to remove the effects of money supply variation on the ratio. The indexed series represents the natural rate (which is unaffected by variations in money supply). The interest rate gap is defined as the difference between this ratio and the actual (non-indexed) “market” ratio, which is caused to fluctuate by means of central bank-directed monetary policy.
Once we had obtained this measure of the interest rate gap, the methodology of Granger causality was employed to establish whether there was evidence of linkages (1)-(4) in the data.
The basic model for Japan is that changes in the discount rate lead to changes in the interest rate gap, and that the changes in the interest rate gap lead to changes in GDP. Japan’s monetary authorities tend to focus more on discount policy than open market operations, so the discount rate is the appropriate policy variable. […] Thus, we perform a Granger causality test on a VAR (vector auto-regression) to test the null hypothesis that the discount rate does not Granger-cause the interest rate gap. We investigated the lag structure of the discount rate-interest rate gap to eliminate auto-correlation by virtue of examination of the correlogram of residuals. A lag length of one period was determined to be appropriate. The results of the Granger causality test are presented in Table 1:
Null Hypothesis: – Observations – F-Statistic – Probability
Discount Rate does not Granger Cause Interest Rate Gap – 63 – 7.60367 – 0.00758
We can reject the null hypothesis at the 1% level of significance, indicating that Bank of Japan monetary policy causes changes in the interest rate gap. […] If the interest rate gap Granger causes nominal GDP, we can conclude that Japan has experienced fluctuations consistent with the predictions of ABCT. Therefore, we performed a Granger causality test on a VAR with twelve lags to test the null hypothesis that the interest rate gap does not Granger cause nominal GDP. The appropriate lag length for the VAR is established using the Akaike information criterion, and GDP is differenced to produce a stationary variable. The regression results are presented in Table 2:
Null Hypothesis: – Observations – F-Statistic – Probability
Interest Rate Gap does not Granger cause nominal GDP – 52 – 2.68491 – 0.01334
We can reject the null hypothesis at the 5% level of significance, indicating that a change in the market rate relative to the natural rate causes changes in output.
The basic model for the US is that changes in the federal funds rate lead to changes in the interest rate gap and that the changes in the interest rate gap lead to changes in GDP. The Fed affects monetary policy through open market operations which, in turn, influence the interbank borrowing rate. […] We performed a Granger causality test on a VAR with one lag to test the null hypothesis that the federal funds rate does not Granger-cause the interest rate gap. The results are presented in Table 3:
Null Hypothesis: – Observations – F-Statistic – Probability
Federal Funds Rate does not Granger Cause Interest Rate Gap – 79 – 4.30466 – 0.04139
We can reject the null hypothesis at the 5% level of significance, indicating that Federal Reserve monetary policy causes changes in the interest rate gap. […] We thus performed a Granger causality test on a VAR with fourteen lags to test the null hypothesis that the interest rate gap does not Granger cause nominal GDP. We again established the appropriate lag length for the VAR by using the Akaike information criterion. The results are presented in Table 4:
Null Hypothesis: – Observations – F-Statistic – Probability
Interest Rate Gap does not Granger cause nominal GDP – 66 – 3.07333 – 0.00315
We can reject the null hypothesis at the 1% level of significance, indicating that a change in the market rate relative to the natural rate causes changes in output.
Evidence Regarding the Structure of Production (Larry J. Sechrest) [PDF]
The time series for M1, M2, the Austrian measure of money (AUSMS) *, the index of industrial production, and the index of business equipment production are illustrated in Figures 1-5 in the Appendix. In those figures, the peak and trough months for each of the cycles since 1959 are identified by vertical reference lines. The dating of said peaks and troughs are according to the National Bureau of Economic Research. The peaks as given by the NBER are April 1960, December 1969, November 1973, January 1980, July 1981, July 1990, and March 2001. The troughs are February 1961, November 1970, March 1975, July 1980, November 1982, and March 1991. Several additional variables were derived from the various series listed above: NATRATE, a proxy for the natural rate of interest (the 3-month lagged difference between consumer and producer prices); INTDIFF, the difference between the natural rate and the Federal Funds rate; and PRATIO, the ratio of consumer goods’ prices to producer goods’ prices.
* […] the Austrian conception of money is here taken to include currency held by the public, demand deposits, other checkable deposits, U.S. government deposits, and deposits due to foreign banks and foreign official institutions
The adjusted R² numbers for saving versus total industrial production, the production of business equipment, and business loans are .461, .548, and .515, respectively. Those for the three measures of the money stock are much higher, with M2 showing the highest degree of correlation, the Austrian measure second, and M1 third.
Total Industrial Production
Ind. Var. // Adj. R²
SAVRATE // .461
M1 // .879
M2 // .930
AUSMS // .909
Production of Business Equipment
Ind. Var. // Adj. R²
SAVRATE // .548
M1 // .878
M2 // .938
AUSMS // .919
Commercial and Industrial Loans
Ind. Var. // Adj. R²
SAVRATE // .515
M1 // .926
M2 // .984
AUSMS // .948
If one models industrial production as a function of a) the difference between the Federal Funds rate and the proxy for the natural rate, b) the ratio of the CPI to the PPI, and c) the supply of money, one finds high degrees of correlation, as seen in Table 4. Moreover, the algebraic signs of the parameter coefficients are as expected: positive for money and the ratio of prices and negative for the interest rate differential. In addition, all the t-tests indicate statistical significance at the 99% confidence level.
Total Industrial Production
Ind. Variables // Adj. R² // t-statistics
PRATIO, INTDIFF, M1 // .922 // 12.842, -16.823, 16.449
PRATIO, INTDIFF, M2 // .950 // 9.180, -14.638, 26.657
PRATIO, INTDIFF, AUSMS // .956 // 6.972, -23.108, 29.644
Similar results are seen if one substitutes the production of business equipment as the dependent variable (Table 5).
Production of Business Equipment
Ind. Variables // Adj. R² // t-statistics
PRATIO, INTDIFF, M1 // .912 // 12.956, -12.984, 14.466
PRATIO, INTDIFF, M2 // .948 // 8.358, -9.834, 26.812
PRATIO, INTDIFF, AUSMS // .947 // 6.963, -16.770, 26.291
Alternatively, one might argue that some measure of credit should be used as an explanatory variable instead of a measure of the money stock. Commercial and industrial loans were taken as the measure of credit available. The results, shown in Tables 6 and 7 below […] However, using credit instead of money (along with relative prices and interest rate differentials) leads to a noticeably higher degree of correlation with the production of business equipment.
Total Industrial Production
Ind. Variables // Adj. R² // t-statistics
PRATIO, INTDIFF, COMLOANS // .952 // 8.339, -11.033, 27.677
Production of Business Equipment
Ind. Variables // Adj. R² // t-statistics
PRATIO, INTDIFF, COMLOANS // .967 // 5.793, -5.486, 37.654
Therefore, I constructed three composite price indexes (January 1959 = 100) and found the correlation between each and the supply of money. The first, COMPIND1, was made up of the CPI and PPI, weighted equally. The second, COMPIND2, was composed of the CPI, the PPI, the Dow Jones Industrial Average expressed as an index number, and an index of urban real estate rental prices, with the four components weighted equally. The third, COMPIND3, was composed of the CPI, the Dow Jones Industrial Average expressed as an index number, an index of wage rates paid to private-sector, goods-producing workers, and the index of urban real estate rental prices, weighted equally. The regression results appear in Tables 8, 9, and 10. As additional categories of prices are added, the correlation with M1 declines considerably, while the correlation with M2 remains about the same, and the correlation with the Austrian measure of money rises substantially.
The Price Level (CPI and PPI—weighted equally)
Ind. Var. // Adj. R²
M1 // .946
M2 // .952
AUSMS // .893
The Price Level (CPI, PPI, DJIA, and CITYRENT—weighted equally)
Ind. Var. // Adj. R²
M1 // .892
M2 // .946
AUSMS // .947
The Price Level (CPI, DJIA, WAGEIND, and CITYRENT—weighted equally)
Ind. Var. // Adj. R²
M1 // .899
M2 // .954
AUSMS // .954
Austrian Business Cycle Theory and Global Financial Crisis: Some Lessons for Macroeconomic Risk and Financial Stability (Ersan Bocutoğlu & Aykut Ekinci) [PDF]
The table presents correlations between the term spread and the monthly percentage change in industrial production index for the United States. The table presents the contemporaneous correlation between the two variables, as well as correlations at various leads and lags of the term spread relative to growth. The contemporaneous correlation between IPI growth and the term spread is positive and statistically significant. The cross correlations between IPI growth and the term spread lagged from one to six quarters are uniformly positive and statistically significant (indicated by p-values of 0.05 or less).
Thus, the correlations indicate that, in general, the higher the spread is – that is, the more steeply sloped the yield curve is – the higher the rate of future IPI growth is. Similarly, the less steeply sloped the yield curve is, the lower the subsequent rate of IPI growth is.
Firstly, we used the stage of processing which are grouped according to the Producer Price Index (PPI). Then we took the group indexes of “Crude energy materials (CE)”, which define price fluctuations in the first stages of production, and “Crude nonfood materials less energy (CM)”. We also determined the group indexes of “Intermediate energy goods (IE)”, which define price changes in the intermediate stages, and “Intermediate materials less food and energy (IM)”.
Figure 4 shows the deviation index percentage of CM obtained from the IM Index (DEV(CM/IM)%) and the deviation index percentage of CE from the IE Index (DEV(CE/IE)%). Figure 5 displays the Composite Index obtained from the equal combining of the two index series mentioned above. COMPOSITE reveals how much the prices in the first stages of production deviate in percentage from the prices in the intermediate stages. Seen from this angle, the fact that COMPOSITE is high in terms of value and time can be considered as an indicator of instability.
From 2001 to 2007, the CPI, and industrial-inputs, energy, and metal prices increased by about 19%, 113%, 174% and 226% respectively. In the same period, an increase of more than 250% occurred in the Global Dow Jones Index.
The primary focus of ABCT is not the fluctuation in prices of final goods and services. As a matter of fact, while the average rate of CPI inflation in the United States was 3.5% in the period 1981–2001, the average CPI inflation was 2.9% during the 2002–2007 expansionary period. The fact that CPI is not acting as a leading indicator during business cycles is well illustrated by the trends given in figure 8.
VIX Index is the Chicago Board Options Exchange (CBOE) Volatility Index, which shows the market’s expectation of 30-day volatility. This volatility is meant to be forward-looking and is calculated from both calls and puts. The VIX index is a widely used measure of market risk and is often referred to as the “investor fear gauge.”
The negative correlation between SPREAD and risk perception is consistent with the Austrian proposition that credit expansions due to artificially reduced market interest rates lead to underestimation of risk; the increasing SPREAD in 2002–2004 can be said to have lowered the risk perception in financial markets in the years prior to the present crisis.
The volatility of CPI and IPI were measured as the percentage deviation of index from the Hodrick-Prescott trend. We computed this standard deviation by using a rolling window of twelve months. Volatility of output and inflation lagged behind spread.
Financial Market Shocks during the Great Depression (Alycia Chin & Missaka Warusawitharana) [PDF]
The Structure of Production
. In 1986, according to the Department of Commerce study “The Interindustry Structure of the United States,” 43.8 percent of the output of all business units was of “intermediate goods” (or capital goods requiring further work before they are ready for the ultimate consumer). The remaining 56.2 percent were final goods, for purchase by individuals, governments, and other businesses. This 43.8 percent representing intermediate goods does not show up in the Gross National Product accounts at all, which treats capital as consisting only of the final goods purchased by businesses.
The Coming of the Recession
As interest rates go up, new projects not yet started will be canceled. But many of those which are only half finished will also have to be abandoned. One reason is that capital financing is often obtained on a pay-as-you-go basis. As industries compute the payoff for a project started when interest rates were 8 percent, which now must compete for funds at 12 percent, they realize that the project is a loser. They cut their losses, and abandon the enterprise. The workers are laid off, and often, much of the project is a total loss. The reason? Because most capital goods (semifinished goods and facilities) are specific to an industry and have little general usefulness. […] As workers are laid off in higher-stage industries, they reduce their spending for consumer goods. The recession spreads.
The Recession of 1990
The process just described took place during the years from 1981 to 1992. From 1981 to 1986 (and earlier), the Federal Reserve embarked on a massive increase in the money supply which averaged 9.6 percent per year while the GNP in real terms expanded by only 2.6 percent. From 1987 to 1991, the money supply increased by an average of only 4.1 percent per year while, the GNP increased by about 2 percent. The dramatic drop in government money supply expansion is shown by Figure 2.
The money supply expansion from 1981 to 1986 resulted in expanded bank loans to higher-stage industries, while lower stage industries, at first, were unaffected. As more workers were hired by these expanding industries, and others received pay increases and began to spend their pay on increased consumer goods, the lower-stage industry bank borrowing increased. Lower-stage capital expansion is based more on anticipated consumer demand than upon the availability of capital. The precipitous drop in government money supply expansion after 1986 ended the boom in the higher-stage industries. This was the beginning of the recession – although it did not show up for four more years.
Figure 3 depicts long-term bank loans to higher-stage industries (represented by iron and steel, primary metals, and machinery) relative to total-long-term bank loans to all manufacturing industries, superimposed on the annual growth of the M2 money supply. Notice how two of these industries increased their loans, in relation to all manufacturing loans, dramatically during 1982 and 1983. The increase was 250 percent in iron and steel and 150 percent for primary metals. Machinery industries – much lower in the structure of production than primary metals – increased their borrowing only slightly during the expansionary period and were generally below industry averages thereafter.
The fact that the increases in higher stage borrowing from 1981 to 1985 were based on the increased availability of capital funding rather than directly anticipated demand for increased output is shown by the statistics on industrial production during this period. The level of production in the iron and steel industry at the time was far below capacity (estimated at about 63.5 percent from 1981 to 1985), due to the low level of orders. Lower-stage industries, closer to the consumer, showed no such increased borrowing levels during this period of massive money supply increases.
With the benefit of hindsight, we know today that net shipments of steel mill products would never regain their 1981 levels during the following decade. Why did they expand their production? All that industry participants knew at the time was that funding for expansion was available. Iron-ore production, much closer to its direct customers than the iron and steel industry, did not invest in expansion during the period, and, in fact, closed down several of its operating mines.
When the massive money-supply increases came to an end in 1986, the iron and steel industry collapsed. Prices of their product dropped every year as the higher-capacity and more-efficient new facilities competed with the older plants for what was essentially a disappointing demand.
Lower-Stage Industry Long-Term Borrowing
Food, rubber, and textiles are industries that are much closer to consumers than are iron and steel and primary metals. We could have also looked at petroleum, drugs and motor vehicles as representative of lower-stage industries, but for special demand reasons, one could argue that the 1980s were not typical years for these groups.
Drugs were affected by the growth of the Medicare and Medicaid programs which began to be important during this period. American motor vehicles were competing heavily, for the first time, with the Japanese. Petroleum is a vertically-integrated multi-national industry with high capital investments in higher-order (drilling) and lower-order (refineries and gas stations) goods.
Figure 4 depicts the long-term bank borrowing of three lower-stage industries during 1981-1991 as contrasted with total long-term bank lending to manufacturing industries during the period and the growth in the money supply. […] During the period of money supply expansion from 1981 to 1984, they generally maintained their bank loans virtually unaffected by the expansion of loanable funds. Towards the end of the expansion period, textile and rubber industries began to undertake long-term projects that pushed them up to 170 percent and 200 percent of industry averages by 1986. By 1987 consumer spending had driven even food industries to compete for bank-loan funds at a very high rate.
Notice that the peak borrowing for food and rubber came after 1986 – at a time when the money-supply growth rate was being drastically curtailed. Why was that? The previous expansion in higher-stage industries produced an increase in consumer spending which pushed lower-stage manufacturers to borrow to expand their facilities to meet it. 
. From 1981 to 1986, real consumption spending in 1987 dollars increased by 19.7 percent, whereas real GNP increased only 15.7 percent in the same period. Consumers were trying to restore their spending patterns after the recession of 1982.
Figure 5 puts the entire period into perspective. It shows total dollar borrowing by all manufacturing industries adjusted for inflation – the producer’s price index. […]
The dramatic reduction in money supply increases in 1987 did not affect the total borrowing level at all – in fact the average annual increase in borrowing after 1986 was higher than before 1986. What did change was the distribution of that borrowing. […]
Most traditional economists looked on the years from 1986 through 1988 as being boom years. They overlooked what was happening in higher-stage industries. What was going on in iron and steel, for example was this: LTV Corp., Wheeling-Pittsburgh Steel Corp. and Sharon Steel Corp., were all forced to file for bankruptcy following the collapse of steel demand in 1985 and 1986. This collapse accelerated the reduction of U.S. steel-making capacity, and triggered a major restructuring of the iron ore and steel industries on both sides of the US-Canadian border.
Figure 6 shows what happened to steel prices after the Federal Reserve stopped inflating the money supply. Consumption of steel failed to regain its 1981 levels in the subsequent decade. […] Coinciding with the United States money-supply increases which began in 1981, the U.S. copper industry began a major inventory increase to 275 percent of 1979 levels by 1983. Beginning in 1984, inventories began to fall drastically every year until 1988, until they reached an average of about 14 percent of 1981 levels by 1989. 
. Minerals Yearbook 1989, p. 359. Despite this buildup of inventory from 1980-83, world consumption of copper was almost flat from 1980 to 1989. The buildup, therefore, cannot be ascribed to increased demand (p 352).
In figure 7, we contrast long-term borrowing of all manufacturing, retailing, and wholesale firms. Retail borrowing took a nosedive from 1981 to 1982 – recession years – and stayed down all during the period when the Federal Reserve was inflating the money supply. Retail borrowing only accelerated in 1987 – after the inflation of the money supply was over! Why? Because retail firms borrow to meet immediate customer demand. Higher-order firms borrow when financing is available on attractive terms. […] During all of these years, prices were flat or falling. Then, in 1986, prices began to climb.
In the later stages of the boom, consumer spending competes with and overtakes all other types of activity. It is at this point that unused higher-stage capacity materializes because, as Hayek says, “We are unable to use the fixed plant to the full extent because the current demand for consumer’s goods is too urgent to permit us to invest in current productive services in the long processes for which (in consequence of ‘misdirections of capital’) the necessary durable equipment is available.”
The recession and Austrian business cycle theory: An empirical perspective (William N. Butos) [PDF]
As Figure 2 shows, commercial bank’s industrial and commercial loans increased steadily until the end of 1989. Although these loans fall off rather sharply beginning in 1990, which is consistent with the ATBC, until that time they failed to expand as vigorously as might be expected given the increase in bank reserves. Several factors may explain this result.
First, the rapid increase in bank reserves relative to bank credit reflects the Fed’s apparent desire to offset the strong decline in the income velocity of money, defined as the ratio of nominal GDP to the stock of money, which occurred between the end of 1983 and the end of 1986. Income velocity will decline if individuals increase their holdings of money relative to their incomes. This presumably is what happened as falling interest and inflation rates reduced the opportunity cost of holding money, and as an expanding array of interest-earning money assets became more widely available following the 1980 Depository Institutions Deregulation Monetary Control Act (DIDMCA) and the 1982 the Garn-St Germain Depository Institutions Act.
Second, the third-world debt crisis induced banks with third-world loans to substantially increase their loan loss reserves – reserves set aside (not loaned out) to cover nonperforming loans. […]
A third factor concerns the dramatic developments in the real-estate market during the 1980s. The data suggest that a significant element of the 1982-1990 expansion was real estate related. As Table 1 shows, in the 1980s commercial banks increased their real-estate loans at more than double the average rate of the over cyclical upswings. An important factor in accounting for the growth in real-estate loans was the 1981 Economic Recovery Tax Act (ERTA). ERTA provided substantially more favorable tax treatment of investment by accelerating depreciation schedules (15 years under ERTA compared to 40 years under pre-ERTA); reducing the asset lives of plant, equipment, commercial buildings, and rental housing; and providing a more generous investment tax credit on equipment (but not structures).
Second, as Figure 7 indicates, the ratio of total private nonagricultural employment to employment in durable goods-producing industries increases sharply during all the contractions since 1973 – as the ATBC would lead us to expect – with the exception of the 1990-1991 recession. While 1990-1991 may appear to anomalous, a closer look at the data sustains a somewhat different interpretation.
Thus, Figure 8 shows that prior to and during the 1990-1991 recession, employment in durable goods industries dropped significantly in absolute terms and relatively compared with employment in nondurable goods industries. For example, between 1989 and 1991 employment in durable goods industries fell by 10.4 percent, while only by 2.2 percent in nondurable goods industries.
According to the ATBC, new orders and shipments of capital goods should decline during a recession as resources are reallocated back toward less capital-intensive production methods.
Does Austrian Business Cycle Theory Help Explain the Dot-Com Boom and Bust? (Gene Callahan & Roger W. Garrison) [PDF]
1999–Winter 2000: The Height of Madness
Between 1950 and 1992, the personal savings rate had never gone above 10.9 per cent and never fallen below 7.5 per cent, except in three isolated years. But, between 1992 and 2000, it plummeted from 8.7 per cent to -0.12 per cent. …
[B]y 2000, households’ outstanding debt as a proportion of personal disposable income reached the all-time high of 97 per cent, up from an average of 80 per cent during the second half of the 1980s.
During what we will roughly designate as “the boom,” from June 1995 to March 2000, MZM grew 52 percent, well ahead of real GDP growth of 22 percent (Rogers 2002) for the same period. The interest rate on 10-year Treasuries declined from 6.91 percent to 4.53 percent in October 1998, before beginning to rise again. Rates peaked in early 2000, roughly corresponding to the end of the boom. Corporate Aaa bond yields declined from 8.46 percent at the beginning of 1995 to 6.22 percent in at the end of 1998. (All data but Rogers from FRED.)
By late 1999, production of business equipment was up 74 percent and construction up 35 percent over 1992, while production of consumption goods had risen only 18 percent. Among manufacturing goods, durable good production had risen 76 percent while nondurable good production had risen just 13 percent (Federal Reserve 2000). “Annual borrowing by nonfinancial corporations as a percentage of nonfinancial corporate GDP darted from 3.4 per cent in 1994 … to a previously unparalleled 9.9 per cent in the first half of 2000. … As a result, by the first half of 2000, nonfinancial corporate borrowing on an annual basis had more than quadrupled with respect to 1994 and nonfinancial corporate debt as a proportion of nonfinancial corporate GDP had reached 85 per cent, the highest level ever” (Brenner 2002, p. 192).
Even as low interest rates spurred investment in certain capital goods, they led to a collapse in savings. The personal savings rate declined from an already low 2.1 percent (compared to a long-term trend of between 7 percent and 11 percent, as described above) in 1997 to -1.5 percent by 1999 (Bureau of Economic Analysis 1999). Consumers were increasingly leveraged, especially on their homes. “In 1989, about 7 percent of new mortgages had less than a 10 percent down payment, according to Graham Fisher & Co., an investment research firm. By 1999, that was more than 50 percent” (Priest 2001).
Spring 2000: The Tide Turns
The increase [in apartment rents] from $920 per month in the fall of 1995 to $2,080 in the spring of 2000 squeezed San Franciscans. … The rise in rents presumably reflected an increase in demand for housing, stemming from the influx of wealthy dot-commers. (Huffman 2001)
The people needed to staff dot-com companies were also rapidly becoming more expensive. As the boom peaked, Audi (2000) reported: “So many San Francisco lawyers were leaving good jobs in big firms to work for start-up Web companies that to compete, some firms doubled the starting pay to $150,000.”
Kuo (2001, p. 46) tells of how, within weeks of being hired in the summer of 1998, Value America executive Glenda Dorchak demanded, and received, “a lot more stock … a significant raise in pay,” and a promotion.
Covin (2002) noted: “In the late 90s, there was a sudden increase in programmer salaries as a result of the dot-com boom. Programmers who were earning $45,000 in 1995 were making well over $100,000 by the year 2000.”
Can Austrian Theory Explain Construction Employment? (Robert P. Murphy)
As the housing boom intensified, sucking more and more workers into construction, the national unemployment rate steadily fell. Then, as the housing boom tapered off, extra workers stopped getting siphoned into the housing sector, and the national unemployment rate bottomed out. Finally, as construction employment began falling, the national unemployment rate began rising.
Now DeLong or Sumner might object that I’m mixing up causation with correlation. For whatever reason, consumers got yellow in late 2007 and did the unthinkable — they started saving some of their incomes. Thus, everything started dropping at that point, including home prices and construction employment. But there’s nothing “real” in this story corresponding to the Austrians’ worries about “too much housing,” Sumner and DeLong might argue.
Putting Austrian Business-Cycle Theory to the Test (Robert P. Murphy)
In the Austrian framework, construction would typically be the “highest order” of these, because things like office buildings and houses are very capital-intensive and provide a flow of services for decades. Next in line would be durable-goods manufacturing, while nondurable-goods manufacturing would be the “lowest order” of these three categories.
Lecture complémentaire :
Austrian Business Cycle Theory, par Jesús Huerta de Soto
Autres références :
Empirical Evidence for Hayek’s Theory of Economic Fluctuations (Charles E. Wainhouse), cited in the chapter 2, pp. 37-72, of “Money in Crisis” (Barry Siegel) [PDF]
Empirical Testing of the Austrian Business Cycle Theory: Modelling of the Short-run Intertemporal Resource Allocation (Tobias Helmersson & Karl Selleby) [PDF]
Testing Austrian Business Cycle Theory? A Rejoinder to Andrew Young (Robert P. Murphy, William Barnett II & Walter Block) [DOC]
Relative Prices and the Business Cycle (James P. Keeler) [PDF]
An Empirical Application of the Austrian School’s “Stages of Production” (Cameron M. Weber) [PDF]
Austrian persistence? Capital-based business cycle theory and the dynamics of investment spending (Michael R. Montgomery) [PDF]