Very preliminary--quotation is strictly prohibited
J APAN’S GREAT STAGNATION: AN I NTERPRETATION USING VECTOR AUTOREGRESSION MODELS Yasuyuki Iida and Yutaka Harada∗
September 3, 2004
ESRI International Workshop "Beginning of the new growth" September 3, 2004
The Japanese economy has recently undergone a period of steady recovery. Japan’s economic growth, however, has dropped from three percent in the first half of the 1980s to one percent in the 1990s. We will call this decline the Great Stagnation. Then, what caused the Great Stagnation?
First, we briefly surveyed various proposed explanations. Second, we excluded some explanations, and focused on several hypotheses based on economic reasoning and facts. Third, we constructed a VAR (Vector Autoregression) model to test the remaining hypotheses. ∗
Yasuyuki Iida, Assistant Professor, Komazawa University, Visiting Research Officer, Economic and Social Research Institute, Cabinet Office. e-mail:
[email protected]. jp Yutaka Harada, Chief Economist, Daiwa Institute of Research Ltd, Visiting Research Fellow, Economic and Social Research Institute, Cabinet Office. e-mail: y.harada@rc. dir.co.jp The views expressed here do not represent those of the institutes to which we belong. We thank XXX and others for their helpful comments. All errors, needless to say, are our own. 1
The results are preliminary, but basically suggest that monetary factors explain a significant part of the decline of trend growth.
1. Pos sible Explanations
There are at least five key arguments on what caused the Great Stagnation. They are 1) the bubble hypothesis—that the bubble and its burst caused the long slump; 2) the efficiency shock
hypothesis—that certain structural problems decreased the
efficiency of the Japanese economy in the 1990s; 3) the fiscal policy hypothesis—that insufficient government expenditure impeded economic recovery. On the contrary, the other version is that too much government expenditure hinders the recovery though so called “Non-Keynesian effect”. 4) the financial system hypothesis—that the decline of function of the financial system hampered economic growth; and 5) the monetary policy hypothesis—that insufficient monetary expansion caused the slump. The authors who write about the Great Stagnation cover most of these hypotheses. For example, Hamada and Horiuchi ed. (2004), which collected eight papers on this topic, cover all of these hypotheses. Bayoumi (2000), Posen and Kuttner (2001), Hori and Ito (2002), Miyao (2002), Tanaka and Kitano (2002) and Nakazawa, Onishi and Harada (2002) (hereafter NOH), using VAR models, also cover most of these hypotheses.
Before we carefully investigate the proceeding studies, we will briefly check these hypotheses and exclude some of them from serious investigation. This is a needed step to apply a VAR model to this problem, because a VAR model cannot include
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many variables in case that sample size is limited.
1.1 The Bubble and Its Subsequent Burst
Many Japanese economists attribute Japan’s stagnation to the “bubble” and its burst, but many countries have experienced bubbles and bubble bursts. No country, however, has experienced such prolonged stagnation. For example, the Scandinavian countries experienced bubbles and bursts at the end of the 1980s, but all those countries recovered within several years. Chapter 2 of Harada (2003) extensively surveys bubbles and bursts in all the advanced countries using data from the International Monetary Fund, the International Financial Statistics, and found that 11 countries experienced bubbles and bursts in the 1980s as shown in Table1. Moreover, it was found, the scale of stagnation in these countries, defined as the accumulated differences of trend GDP after the burst divided by GDP, was only 6.8 percent on average, while that of Japan is 24.1 percent. This figure also shows that the average scale of bubbles of these 11 countries, defined as the accumulated differences of trend GDP throughout 1970 to 2000 divided by GDP, was 6.0 percent; the figure for Japan was 7.8 percent. That is, Japan’s stagnation after the bubble is much greater than the average, while Japan’s bubble is only slightly larger than the average. This fact suggests that Japan’s sluggish post-bubble economy cannot be explained by the scale of the bubble.
1.2
Efficiency Shock
Many economists also argue that Japan’s low growth rate is best explained by
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efficiency shock of its economy (Hayashi (2003) and Miyagawa (2003) are two examples). That is, they assert that the Japanese economy has had efficiency shock, and that its potential GDP growth rate has declined. No economist, however, can explain what the shock is that caused GDP growth to shrink from three percent to one percent. As an exception, Prescott (1999) points out that the shock that decreased GDP growth rate is the change of market hours that the work week was reduced form 44 hours to 40 hours by law in the early 1990s. His assertion might be right for the early 1990s, but not after the middle of the 1990s. The shortening of labor hours can reduce economic growth rate and the level of real GDP in early 1990s, but it cannot reduce the growth rate after that.
If we look at real GDP per working hours instead of ordinary real GDP, we see a different picture. Chart 2 shows real GDP per working hours and real GDP (both are indexed at 1990 = 100). Real GDP growth has declined sharply since the 1990s, but real GDP per working hours has not significantly declined1 .
More careful growth accounting studies that consider the capital utilization rate also support the contention that GDP per working hours did not decrease. Nakajima et al. (2001), Motohashi (2001), Miyagawa (2002), Jorgenson and Motohashi (2004) unanimously show that TFP (Total Factor Productivity) has not significantly declined. Nakajima and Motohashi show that TFP increased, and Miyagawa shows that TFP declined only slightly in the 1990s. If TFP did not decline, then labor productivity, i.e. 1
The same fact is pointed out by Dr. Barry Bosworth of the Brookings Institution and by
Professor Dale W. Jorgenson at the International Forum, organized by the Economic and Social Research Institute, Japan, on February 17-19, 2003.
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GDP per working hours, does not decline much, either. Inui and Kwan (2004), after their intensive survey of many studies, conclude that Japanese TFP did not significantly decline in the 1990s.
The Structural Problems of the 1990s Were Really Rooted in the 1980s
We don’t intend to assert that the Japanese economy doesn’t have structural problems that decrease its efficiency; it certainly does. Japan’s economy is a dual economy. The export-manufacturing sector, which claims only about 20 percent of GDP, has high productivity, but other sectors have low productivity2 . Our point is that Japan had structural problems not only in the 1990s but also in the 1980s.
Additionally, Japan had already made great structural reform s that gave positive efficiency shock to the Japanese economy leading up to the 1990s. Major public corporations, i.e. the Japan Telephone and Telegraph Corporation and the Japan Railroad Corporation, were privatized in 1985 and 1987 respectively. Moreover, incom e and corporate taxes were reduced drastically before the early 1990s, while the consumption tax was introduced in 1989 and increased in 1997. The ratio of income tax and corporate tax revenue to GDP was 21.9 percent in 1990, but 17.3 percent in 2000. Social security taxes were increased, but overall taxes were reduced in the 1990s. The ratio of income tax + corporate tax + social security tax to GDP was 30.8 percent in 1990, but 27.7 percent in 2000 (National Accounts, Econom ic and Social Research Institute, Cabinet Office). If structural reform was important, this was structural reform. The huge tax reduction, including the marginal tax rate cut – maximum 2
See Table 1, table 2, and Table 3 of Baily and Solow (2001).
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income tax rate was reduced 80 percent (includes local tax) from 1987 to 50 percent in 1997, while the consumption tax rose to 5 percent in 1997 — should have had a great positive impact on the Japanese economy, but we have yet to see that impact.
1.3 Fiscal Policies, Financial Function, and Monetary Policy
To demonstrate the other hypotheses, i.e. the fiscal policy hypothesis, the financial system hypothesis, and the monetary policy hypothesis, we created Chart 3A. This chart shows the growth rates to the same period of the previous year of government expenditure, loans, monetary base, and real output (the definitions and sources of the variables are explained in Section 2 and in the note of the chart). We think that loans express well the state of the financial system and the actual economic situation in Japan.
Loans, monetary base, and output move in the similar way until the early 1990s, but after that these variables move differently—the loans continuously declined, but the monetary base moved prior to the three recoveries and recessions of real output in the 1990s and beyond except 1998. Government expenditure doesn’t seem to be correlated with output, but it moves with the change of output at the end of the 1990s.
The chart, of course, shows that more careful statistical investigation is needed to identify the importance of the above hypotheses.
2. Various Vector Autoregression Models
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2.1 Previous Studies
As discussed above, the bubble burst and the decline of TFP hypotheses cannot explain Japan’s Great Stagnation. We will focus on the latter three hypotheses, that 3) insufficient fiscal policy, 4) the declining function of the financial system, and 5) insufficient monetary expansion caused the long slump. Here, we will construct various VAR models that cover these hypotheses and show what factors are most important in explaining the Great Stagnation.
There are many studies on the Japan’s Great Stagnation. Bayoumi (2000), Kuttner and Posen (2001), Hori and Ito (2002), Miyao (2002), Tanaka and Kitano (2002) and Nakazawa, Onishi and Harada (2002) (hereafter NOH), using VAR models, suggest that monetary shock might be an important factor to explain the fluctuation in the 1980s and 90s. These studies are very different in which points they emphasize.
Bayoumi stresses various factors such as fiscal policy, monetary policy over investment during the bubble, and decline in the financial intermediation function. He thinks that a major factor was disruption in financial intermediation, largely operating through the impact of changes in domestic asset prices on bank lending. Posen and Kuttner (2001) emphasize the role of fiscal policy and the decline of financial function, but they do not discuss monetary policy. Miyao, Tanaka and Kitano, and NOH stress the importance of monetary policy. NOH argue that the decline in the financial intermediation function is also caused by a deflationary monetary policy, which resulted in asset price decline.
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Japanese studies using VAR models tend to stress monetary factors. It might be agreed to some extent among Japanese ec onomists (Kosai, Ito, Arioka (2000)) and Miyao (2002), for example) that monetary policy caused the fluctuations at the end of the 1980s and early 1990s. Few agree, however, that monetary factors are important in explaining economic fluctuations after the latter half of the 1990s. And most of previous studies do not include much data after 1999, in which monetary policy changes drastically, introducing the so-called “zero interest rate policy,” “quantity easing policy,” and other policies. We will therefore focus on the latter half of the 1990s and beyond. To do that, however, we must limit the number of variables. We will first focus on 4) the decline of function of financial system, and 5) insufficient monetary expansion, and then later will discuss the effects of fiscal policy and external demand.
2.2 VAR Models Focusing on Monetary and Financial Factors
A VAR model can statistically identify the importance of various factors to economic fluctuations, but the lack of samples makes it difficult. We will limit the number of variables. To focus on the domestic economy, we define real output as excluding exogenous demands, and real output y is real GDP – external demand – government expenditure. We will then construct five variables – real output, monetary base, loans, real interest rate, and price – for the VAR models.
We use monetary base as a variable expressing monetary policy shock. Traditional studies on Japanese monetary policies consider the call rate as a monetary policy shock
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(for example, Hosono et al. (2001)). This interpretation is reasonable in an ordinary situation, but the call rate in the latter half of the 1990s is, pragmatically, zero3 . We cannot use the call rate as a variable for monetary policy shock. We consider monetary base a variable expressing monetary policy.
As a proxy of the state of 4) the decline of function of financial system, we use loans. We use the real interest rate, which affects monetary base, loans, and real output. The real interest rate is defined as the nominal interest rate minus the expected rate of price increase. The expected rate of price increase is estimated according to Hori and Terai (2004). They estimated the expected rate of price increase from survey data by the Bank of Japan (Tankan) by using the Carlson and Parkin method (Carlson and Parkin (1975)). We follow Hori and Terai (2004), and construct the expected rate of price increase. As a price variable, we use the GDP deflator. To focus on the domestic economy, we define real output y as GDP – net exports – government expenditure.
The variables of the models are as follows. •
Real Output y: real GDP – net exports – government expenditure (final consumption and capital formation of government and public institutes) in 93 SNA data.
•
Monetary base MB: monetary base (reserve requirement rate change adjusted), in Bank of Japan data.
•
Real interest rate r: loan rate (average contracted interest rate on loans and discount for city banks, BoJ) – expected inflation rate calculated according to
Even before the ”zero rate policy,” the call rate was nearly zero. The call rate has been under 1 percent since July 1995. 3
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Hori and Terai (2004). •
Loans: Loans and discounts outstanding (domestic banks, total), BoJ.
•
Price p:
GDP deflator in 93 SNA data.
Additionally, we used consumption tax dummies for 1989Q2 and 1997Q2, a Y2000 dummy for 2000Q1. We also used 1993Q2 dummy for definition of “domestic banks” was changed at that time.
We show the real interest rate and nominal interest rate in Chart 3B, since the real interest rate data are estimated by us,
All the variables are seasonally adjusted except the real interest rate. We took seasonally adjusted data from the original sources, while we seasonally adjusted loans by using X-12 method. The logarithms of all data have been taken, with the exception of the real interest rate. The unit root tests using an augmented Dickey-Fuller test for the above variables are given in NOH (2002) and Iida, Harada, and Hamada (2004). All the variables are generally denied to have unit roots by taking the first differences of the variables. We then use the first differences of the variables. The estimation period is from 1984Q4 to 2003Q4.
To select the rag order, for first to sixth orders, AIC (Akaike Information Criteria) are calculated. For AIC the second lag was selected in most estimations. Here we chose the fourth lag according to Pantula et al (1994). Additionally, Friedman and Kuttner (1992) recommend using data of the previous one-year, i.e. four lags.
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Impulse Response Functions of Reduced Form VAR model (1984-2003)
For the first time, we will estimate the impulse response function, which excludes relations of variables of the same period (this type of VAR model is called “reduced form” (See Stock and Watson (2001)). The results are shown in Chart 4. All the variables are I (1) variables, and take the first differences. Accumulated response is interpreted as a shock to the level of these variables. The dotted line means a 95 percent significance level. Chart 4 shows that monetary base positively affects output at 5 percent significance level, and real interest rate negatively affects output at the 5 percent significance level. Loan and price levels, however, do not significantly affect real output.
Focusing on the relation between monetary policy and loans, monetary base affects loans, but loans do not affect monetary base. This also suggests that the change of loans is induced by monetary policy, and the loans are not the active factor of economic fluctuation.
It is arguable to assume that Japan’s economy did not experience structural change from 1984-2003. Especially, many Japanese economists might still think that that Japan’s Great Stagnation has been caused by some structural change in the 1990s. Miyao (2002) studies stability of VAR models of Japan in the 1990s, and concludes that there was a possibility of structural change in the Japanese economy sometime around 1995. We must study whether our results are maintained by using 1995 to 2003 data instead of 1984 to 2003 data.
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Impulse Response Functions of Reduced Form VAR Model (1995-2003)
Chart 5 shows the same results for the period of 1995 to 2003. The significance level declined, but the general tendency remains the same. Monetary base positively affects output, but is not significant at the 5-percent level. The real interest rate negatively affects output at the 5-percent significance level. Loans and price do not significantly affect output. The relation between monetary base and loans was not significant in the estimation period.
Impulse Response Functions of Recursive VAR Model Assuming that Real Side Is Predetermined (1984-2003)
In the VAR model of the former section, it is assumed that relations of variables of the same period do not exist, but this assumption is not realistic. We will show the impulse response function of other VAR models allowing the relations of variables for the same period.
Although we can assume various models allowing the relations of variables of the same period, we will divide variables into real block(real output and price)and financial block (monetary base, real interest rate, loans). And we think that real block affects financial block during the same period, but financial block affects real block only with lags. Additionally, assuming y is predetermined to p, this block recursive structural VAR model will be the recursive VAR model using Cholesky factorization in the order of y, p, MB, re, loans.
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The results of impulse response are shown in Chart 6. The results are basically same in Chart 4. Chart 6 shows that monetary base positively affects output at the 5-percent significance level, and the real interest rate negatively affects output at 5- percent significance. Loans positively affect output, but are not significant. Price, moreover, does not significantly affect output. The relation between monetary base and loans were same in the former model with the estimation period of 1984-2003.
Impulse Response Functions of Recursive VAR Model Assuming that Real Side Is Predetermined (1995-2003)
According to the former section, we estimated impulse response functions for the period of 1995 to 2003. The results are shown in Chart 7. Monetary base positively affects output at 10-percent significance, and the real interest rate negatively affects output at the 5-percent significance level. Loans and price, however, do not significantly affect output. Monetary base and loans were not significant mutually in this estimation period.
Summary of this Section
We can conclude from the results of Charts 4 to 7 that, generally, monetary base and the real interest rate significantly affect output. Loans and price—which are not explained by monetary factors—on the other hand, do not significantly affect output. Additionally, monetary base affects loans, and loans do not.
Of course, correlation does not necessarily mean causation. Monetary fluctuation
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might be a result of real economic fluctuation. This criticism is held in the case that a change of monetary factor is prior to that of real output as shown in the reduced-form VAR models and the recursive VAR model, assuming real block is predeterm ined to financial block. At least, however, in the middle of the 1980s, monetary policy was intently expansionary. At the end of the 1980s to the early 1990s, the policy was also intentionally shrinking to burst the bubble. Lastly, monetary policy in August 2000 (disuse of “zero interest rate policy”) was intentionally shrinking, and that after March of 2001 (“quantity easing policy”) was intentionally expanding.
The results on the loans are different from those of Bayoumi (2001), and Kuttner and Posen (2001), which stress the impact of financial intermediation function, but their estimate periods are 1986-1998 and 1976-2000. Loans generally decrease from the middle of the 1990s (except 1999), while Japan experienced three economic recoveries during this period.
Additionally, it is notable that prices negatively affect outputs in all the cases, although none of the cases are significant. The price shock in these VAR models is interpreted as price shock, which is not derived from monetary base shock, i.e. the shocks are China’s cheap products, price decline caused by productivity increase, and others. This suggests that China’s cheap products and other productivity shock increase Japan’s output, although the effect of price is absorbed by expected price included in real interest rate.
2.3 VAR Models for Other Possible Explanations: Fiscal Policies and Export
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According to the former section, we can conclude that monetary factors, monetary base and real interest rate, are important, but loans, which express the state of financial function, do not affect real output.
We have not covered 3) fiscal policy, and here we will construct VAR model using government expenditure, exports, output, monetary base, and real interest rate. We include export because it is said that contributions of exports to several economic recoveries in the 1990s cannot to be denied. Then, we add government expenditure (final consumption and capital formation of government and public institutes, in 93 SNA data) and real exports (in 93 SNA data), and exclude loans and price. We then construct five variables - real output, monetary base, real interest rate, government expenditure, and real exports.
Additionally, we used the same dummies in the former model. All the variables are seasonally adjusted and the logarithms are taken, except real interest rate. The unit root tests using an augmented Dickey-Fuller test for new variables are also given in NOH (2002) and others. The new variables are generally denied unit roots by taking the first differences of the variables. We then use these first differences of the variables.
We estimate the VAR model using all the data. We select four rags of the variables according to the same procedure of the former section. Estimation period is from 1984Q4 to 2003Q4.
Impulse Response Functions of Reduced Form VAR Model (1984-2003)
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We estimate impulse response function, which excludes relations of variables of the same period for the data of 1984 to 2003. The results are shown in Chart 8. Government expenditure does not affect output or exports, and monetary base positively affects output at the 5-percent significance level. The real interest rate negatively affects output at 10-percent significance.
Impulse Response Functions of Reduced Form VAR Model (1995-2003)
Next, we will confirm whether similar results are obtained when we use the estimation periods of 1995 to 2003. The results are shown in Chart 9. The significance level generally decreased, since the estimation period becomes half of the former estimation, but the basic results are same. Monetary base is significant at the 5percent significance level, but real interest rate and real exports become insignificant. The directions and magnitudes of these variables, however, are basically the same for the 1994-2003 estimation. Different results for Chart 8 are obtained for government expenditure. The impact of public expenditure becomes large, but it is not statistically significant.
Impulse Response Functions of Recursive VAR Model Assuming that Real Side Is Predetermined (1984-2003)
The results of the former section based on the assumption that the relations of variables of the same period do not exist. This is similar to assuming that public expenditure and exports do not affect real output during the same period. We then
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think about the impulse response function that public expenditure and exports affect real output during the same period.
As we did in the former section, we will divide five variables into real blocks (government expenditure, exports, real output) and financial blocks (monetary base, real interest rate), and assume that real blocks affect financial blocks during the same period, but that financial blocks affect real blocks only with lags. Under this assumption, a block-recursive structural VAR model becomes a recursive VAR model with Cholesky ordering of government expenditure, real exports, real output, monetary base, and real interest rate, as we explained in the former section.
The results for the period of 1984Q1 to 2003Q4 are shown in Chart 10. Monetary base and exports positively affect output at the 5-percent significance level, and the real interest rate negatively affects output at 10-percent significance, but government expenditure does not affect output.
Impulse Response Functions of Recursive VAR Model Assuming that Real Side Is Predetermined (1995-2003)
Chart 11 shows the results for the period of 1994Q1 to 2003Q4. The results are basically the same. Monetary base and exports positively affect output at the 5-percent significance level, and the real interest rate negatively affects output at 10-percent significance. Government expenditure does not affect output at a statistically significant level; the magnitude of the impact becomes larger4 . 4
We confirmed that monetary base was statistically significant while we divided real 17
Additionally, it is notable that impulse responses of monetary base in the models of section 2.2, for the period of 1995-2003, became negative after about 10 quarters. However, the impulses of the models that include government expenditure of this section for the same period do not become negative. The difference is explained by the fact that monetary base is negatively correlated with government expenditure. Though, Chart8 to 10 unanimously show that monetary base negatively affect government expenditure mutually, and the effects are significant at the 5 percent level in the recursive model. This means that the expansion of monetary base induces the cut of government expenditure that negatively affects out put, and so longer-term effect of monetary base tend to be estimated to be negatively.
The negative relation between monetary base and government expenditure is also an interesting finding, and good topic for political economy about interaction of fiscal and monetary authorities. But, this is not the issue of this paper.
Additionally, export negatively affects monetary base in the estimation for the period of 1984-2003, but not in that for the period of 95-2003. This is different from the fiscal policy. Government expenditure negatively affects monetary base in the estimation for both of the periods of 1984-2003 and of 95-2003.This probably means that monetary policy tried to weaken the external demand shock such as export and government expenditure in the 1980s, but monetary policy became to try to do the opposite of fiscal policy 1in the 1990s and beyond. interest rate into nominal interest rate and expected price. But it was, and most of other impulses are, not statistically significant for decline of degree of freedom in the estimation using after 1995 data. 18
Summary of this Section
We can conclude that monetary variables such as monetary base and real interest rate affect real output, despite the difference of estimation periods and variables of the VAR model. Moreover, exports affect output, while government expenditure does not affect output at a statistically significant level. And, the impulse of government expenditure becomes large in the estimate after 1995. This might show that fiscal policy has some effect when output level is considerably below its potential level. These results are generally contradictory to the results of Kuttner and Posen (2001), but they use only fiscal variables in their VAR model.
3. Variance Decomposition
Throughout the former sections, we can conclude that monetary factors are crucial and fiscal policy is not important, but this conclusion is based on qualitative analysis, not on quantitative analysis. We then analyze the extent to which innovations of these five variables—government expenditure, real exports, monetary base, real interest rate, and real output—affect real output by using the method of variance decomposition.
Variance Decomposition (1984-2003)
Variance decomposition attempts to show how a proportionate change of a variable is affected by other variables. Consider change of real output. In variance decomposition
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here, we use the block recursive VAR model of section 2.3, in which externality of government expenditure is assumed. The results of variance decomposition for 1984 to 2003 are shown in Chart 12. We will explain variance of the 12 th quarter. Excluding the impact of output itself, the most important variable is exports (13.3 percent), second is monetary base (12.7 percent), and third is real interest rate (10.1 percent). The effects within one year are remarkable. On the contrary, the impact of government expenditure is negligible.
Variance Decomposition (1995-2003)
The results of 1994 to 2003 are shown in Chart 12. The impact of output to output diminishes. The most important variable becomes monetary base (21.8 percent). Exports affect most within half a year or a year, but the impact becomes less than that of monetary base after one year. This is an interesting finding when considering that the effect of monetary policy typically lags. Also, it is notable that the fiscal impact becomes larger than that in Chart 12.
This probably means that external demand becomes important while real GDP is less than potential GDP, as we stated in section 2.3. The standard error of the impact of government expenditure to real output, however, is large, and the impact is still statistically insignificant. Additionally, the other reason is that the impulse of fiscal policy on output is firstly negative and lately positive (See Chart11). This increases the impact on variance of government expenditure, and in this case, variance decomposition method might not be applicable as the method of judging whether government expenditure explains the steady decline of output or not.
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Conclusions
Japan’s Great Stagnation is basically explained by monetary shocks. Especially, the power of monetary variables becomes larger after the mid-1990s. Loans and price—which are not explained by monetary factors—do not significantly affect output. Of course, as we stated above, correlation does not necessarily mean causation. Monetary fluctuation might be a result of real economic fluctuation. From the middle of 1980 and beyond, however, monetary policy was purposefully expanded and shrunken.
Exports and government expenditure play some role in explaining economic fluctuation, but the impact of government expenditure to output is not statistically significant.
These results suggest the expansion of monetary base after 2001, plays some role in the recent recovery of the Japanese economy.
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Table 1 Magnitudes of Bubbles and Stagnations in Advanced Countries in 1980s (%)
Period of Bubble Japan Australia New Zealand Austria Belgium Denmark Finland France Italy Sweden Switzerland Average
1987 1984 1983 1988 1987 1982 1987 1986 1984 1984 1984
-
1991 1989 1987 1991 1990 1986 1989 1989 1989 1989 1990
Magnitudes of BubblesMagnitudes of Stagnations 7.8 7.2 9.8 4.4 4.1 5.8 5.2 3.6 3.2 4.9 9.9 6.0
-24.1 0.9 -4.6 -6.1 -3.8 -1.7 -9.9 -7.0 -9.4 -3.3 -5.9 -6.8
(Source) Harada(2003)Chapter2, Table2-1, Orijinal data are from International Monetary Fund,
International Financial Statistics
(Note) 1. 11 countries which experienced bubbles are selected from advanced 23 countries in IMF, IFS data according to the following procedures. (1)Real GDP growth rate continuously exceeded trend growth rate (1970-2000 average) by 3 years in middle of 1980s to early 1990s, and real GDP growth rate continuously declined by the trend growth rate for more than 3 years. (2)Stock price increased by 100% in bubble age. (3)Per capita GDP exceed 5000 $ in 1985. 2. Magnitude of Bubbles is accumulated difference of trend GDP. 3. Magnitude of stagnation is accumulated difference of trend GDP.
Chart 2 Real GDP and Real GDP per Working Hour 1990=100
Real GDP per Working Hour 120
100 Real GDP
80
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 (Source) Cabinet Office"SNA,"Ministry of Welfare and Labor"Monthly Labor Survey" (Note) 1. Real GDP per Working Hour= Real GDP/(Workers by Industry
×Working Hours by Industry) 2. 63SNA and Monthly Labor Survey are used before 1989. 93SNA is used after 1990. 3. Real GDP per Working Hours is estimated by "Monthly Labor Survey".
Chart 3 Fiscal Policies, Financial Function, and Monetary Policy
- Growth rates to the same period of the previous year of government expenditure, loans, monetary base and real output 30% 25% 20% 15% 10% 5% 0% -5% -10% M- M- M- M- M- M- M- M- M- M- M- M- M- M- M- M- M- M- M- M84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 Output
Government Expenditure
Loan
Monetary Base
(Source) Real Output : real GDP – net exports – government expenditure, 93 SNA data Monetary base : monetary base (reserve requirement rate change adjusted), Bank of Japan Loans: Loans and discounts outstanding (domestic financial institutions, total), only 1993 fiscal year contain call loan, Bank of Japan Government expenditure: Final consumption and capital formation of government and public institutes, 93 SNA data
Chart4: Accumulated Impulse Responses of Unrestricted VAR model -84Q1 to 03Q4 MB MB
.08
.08
.08
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.04
-.04
-.04
4
6
8
10
12
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.006
.006
.006
.006
.004
.004
.004
.004
.004
.002
.002
.002
.002
.002
.000
.000
.000
.000
.000
-.002
-.002
-.002
-.002
-.002
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.04
-.03
-.04 2
4
6
8
10
12
-.03
-.04 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.016
.016
.016
.016
.012
.012
.012
.012
.008
.008
.008
.008
.008
.004
.004
.004
.004
.000
.000
.000
.000
.000
-.004
-.004
-.004
-.004
-.004
-.008
-.008
-.008
-.008
-.008
-.012 4
6
8
10
12
-.012 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.01
-.03 2
10
-.012 2
.05
-.03
8
.004
-.012 2
6
-.04 2
.012
2
4
-.03
-.04 2
2
.01
.016
-.012
Y
2
.006
-.03
P
Y
.08
2
Loan
P
.08
2
R
Loan
R
-.03 2
4
6
8
10
12
Chart5: Accumulated Impulse Responses of Unrestricted VAR model -95Q1 to 03Q4 MB MB
. 15
.15
. 10
. 10
. 10
. 10
.10
. 05
. 05
. 05
. 05
.05
. 00
. 00
. 00
. 00
.00
-.05
-. 05
-. 05
-. 05
-.05
-.10
-. 10
-. 10
-. 10
-.10
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
. 012
. 012
. 012
.012
.012
. 008
. 008
. 008
.008
.008
. 004
. 004
. 004
.004
.004
. 000
. 000
. 000
.000
.000
-. 004
-. 004
-. 004
-.004
-.004
-. 008 4
6
8
10
12
-. 008 2
4
6
8
10
12
-.008 2
4
6
8
10
12
4
6
8
10
12
. 03
. 03
. 03
.03
. 02
. 02
. 02
. 02
.02
. 01
. 01
. 01
. 01
. 00
. 00
. 00
. 00
.00
-.01
-. 01
-. 01
-. 01
-.01
-.02
-. 02
-. 02
-. 02
-.02
-.03
-. 03
-. 03
-. 03
-.03
-. 04 2
4
6
8
10
12
-. 04 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
. 012
. 012
.012
.012
. 008
. 008
. 008
.008
.008
. 004
. 004
. 004
.004
. 000
. 000
. 000
.000
.000
-. 004
-. 004
-. 004
-.004
-.004
-. 008
-. 008
-. 008
-.008
-.008
-. 012
-. 016
-. 012
-. 016 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
. 03
. 03
. 03
.03
. 02
. 02
. 02
. 02
.02
. 01
. 01
. 01
. 01
. 00
. 00
. 00
. 00
.00
-.01
-. 01
-. 01
-. 01
-.01
-.02
-. 02
-. 02
-. 02
-.02
-. 03
-.04
-. 03
-. 04
-.05 4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
-.04
-. 05 2
4
-.03
-. 04
-. 05 2
2
.01
-. 03
-. 04
-. 05 2
12
-.016 2
. 03
-.03
10
-.012
-.016 2
8
.004
-.012
-. 016 2
6
-.04 2
. 012
-. 012
4
.01
-. 04 2
2
-.008 2
. 03
-.04
Y
Y
. 15
2
P
P
. 15
-. 008
Loan
Loan
. 15
2
R
R
-.05 2
4
6
8
10
12
Chart6: Accumulated Impulse Responses of Recursive VAR model -84Q1 to 03Q4 MB
MB
R
.08
.08
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04 4
6
8
10
12
4
6
8
10
12
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
.006
.006
.006
.006
.004
.004
.004
.004
.004
.002
.002
.002
.002
.002
.000
.000
.000
.000
.000
-.002
-.002
-.002
-.002
-.002
-.004 2
4
6
8
10
12
-.004 2
4
6
8
10
12
-.004 2
4
6
8
10
12
4
6
8
10
12
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.04
-.03
-.04 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.016
.016
.016
.016
.012
.012
.012
.012
.012
.008
.008
.008
.008
.008
.004
.004
.004
.004
.000
.000
.000
.000
.000
-.004
-.004
-.004
-.004
-.004
-.008
-.012
-.008
-.012 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.01
-.03 2
2
-.012 2
.05
-.03
12
-.008
-.012 2
10
.004
-.008
-.012 2
8
-.04 2
.016
-.008
6
-.03
-.04 2
4
.01
-.03
-.04 2
2
-.004 2
.05
-.03
Y
-.04 2
.006
-.004
P
Y
.08
2
Loan
P
.08
-.04
R
Loan
.08
-.03 2
4
6
8
10
12
Chart7: Accumulated Impulse Responses of Recursive VAR model -95Q1 to 03Q4 MB
MB
.12
.12
.12
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
-.08 4
6
8
10
12
4
6
8
10
12
-.08 2
4
6
8
10
12
-.08 2
4
6
8
10
12
.012
.012
.012
.012
.008
.008
.008
.008
.008
.004
.004
.004
.004
.004
.000
.000
.000
.000
.000
-.004
-.004
-.004
-.004
-.004
-.008 2
4
6
8
10
12
-.008 2
4
6
8
10
12
-.008 2
4
6
8
10
12
4
6
8
10
12
.02
.02
.02
.02
.01
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.03
-.03
-.03
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.012
.012
.012
.012
.008
.008
.008
.008
.008
.004
.004
.004
.004
.004
.000
.000
.000
.000
.000
-.004
-.004
-.004
-.004
-.004
-.008
-.012
-.008
-.012 2
4
6
8
10
12
-.008
-.012 2
4
6
8
10
12
4
6
8
10
12
4
6
8
10
12
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.04
-.03
-.04 2
4
6
8
10
12
-.03
-.04 2
4
6
8
10
12
4
6
8
10
12
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
-.03
-.04 2
10
-.012 2
.03
-.03
8
-.008
-.012 2
6
-.04 2
.012
-.008
4
-.03
-.04 2
2
-.008 2
.02
-.04
Y
-.08 2
.012
-.008
P
Y
.12
2
Loan
P
.12
-.08
R
Loan
R
-.04 2
4
6
8
10
12
Chart8: Accumulated Impulse Responses of Unrestricted VAR model -84Q1 to 03Q4 G
G
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02 4
6
8
10
12
4
6
8
10
12
-.02 2
4
6
8
10
12
-.02 2
4
6
8
10
12
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03 4
6
8
10
12
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
4
6
8
10
12
.006
.006
.006
.006
.004
.004
.004
.004
.004
.002
.002
.002
.002
.002
.000
.000
.000
.000
.000
-.002
-.002
-.002
-.002
-.002
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.05
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.01
-.03 2
6
-.04 2
.006
2
4
-.03 2
.06
-.04
2
.01
.00 -.01
2
Y
-.02 2
.04
-.03
R
Y
.03
2
MB
R
.03
-.02
X
MB
X
-.03 2
4
6
8
10
12
Chart9: Accumulated Impulse Responses of Unrestricted VAR model -95Q1 to 03Q4 G
G
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.04
-.04
-.04
-.06
-.06
-.06
-.06
-.08 4
6
8
10
12
4
6
8
10
12
-.06
-.08 2
4
6
8
10
12
-.08 2
4
6
8
10
12
.12
.12
.12
.12
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
-.08 2
4
6
8
10
12
-.08 2
4
6
8
10
12
-.08 2
4
6
8
10
12
4
6
8
10
12
.20
.20
.20
.20
.16
.16
.16
.16
.16
.12
.12
.12
.12
.12
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
-.08
-.08
-.08
-.08
-.08
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.010
.010
.010
.010
.010
.005
.005
.005
.005
.005
.000
.000
.000
.000
.000
-.005
-.005
-.005
-.005
-.005
-.010
-.010 2
4
6
8
10
12
-.010 2
4
6
8
10
12
-.010 2
4
6
8
10
12
4
6
8
10
12
.08
.08
.08
.08
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
-.010 2
.08
-.04
2
-.08 2
.20
2
Y
-.08 2
.12
-.08
R
Y
.04
2
MB
R
.04
-.08
X
MB
X
-.04 2
4
6
8
10
12
Chart10: Accumulated Impulse Responses of Recursive VAR model -84Q1 to 03Q4 G
G
X
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02 4
6
8
10
12
4
6
8
10
12
-.02 2
4
6
8
10
12
-.02 2
4
6
8
10
12
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03 4
6
8
10
12
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
4
6
8
10
12
.006
.006
.006
.006
.004
.004
.004
.004
.004
.002
.002
.002
.002
.002
.000
.000
.000
.000
.000
-.002
-.002
-.002
-.002
-.002
-.004 2
4
6
8
10
12
-.004 2
4
6
8
10
12
-.004 2
4
6
8
10
12
4
6
8
10
12
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03 2
4
6
8
10
12
-.03 2
4
6
8
10
12
4
6
8
10
12
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
.01
-.03 2
8
-.004 2
.05
-.03
6
-.04 2
.006
-.004
4
-.03 2
.06
-.04
2
.01
.00 -.01
2
Y
-.02 2
.04
-.03
R
Y
.03
2
MB
R
.03
-.02
X
MB
.03
-.03 2
4
6
8
10
12
Chart11: Accumulated Impulse Responses of Recursive VAR model -95Q1 to 03Q4 G
G
X
MB
R
Y
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.06
-.04
-.06 2
4
6
8
10
12
-.04
-.06 2
4
6
8
10
12
.02
-.04
-.06 2
4
6
8
10
12
-.06 2
4
6
8
10
12
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
X
-.08
-.08
2
MB
4
6
8
10
12
6
8
10
12
-.08 2
4
6
8
10
12
-.08 2
4
6
8
10
12
.12
.12
.12
.12
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
-.08
-.12
-.08
-.12 2
4
6
8
10
12
-.08
-.12 2
4
6
8
10
12
-.08
-.12 2
4
6
8
10
12
-.12 2
4
6
8
10
12
.006
.006
.006
.006
.006
.004
.004
.004
.004
.004
.002
.002
.002
.002
.002
.000
.000
.000
.000
.000
-.002
-.002
-.002
-.002
-.002
-.004
-.004
-.004
-.004
-.004
-.006
-.006
-.008
-.006
-.008 2
Y
4
.12
-.08
R
-.08 2
4
6
8
10
12
-.006
-.008 2
4
6
8
10
12
-.006
-.008 2
4
6
8
10
12
-.008 2
4
6
8
10
12
.05
.05
.05
.05
.05
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.03
-.03
-.04
-.03
-.04 2
4
6
8
10
12
-.03
-.04 2
4
6
8
10
12
.01
-.03
-.04 2
4
6
8
10
12
-.04 2
4
6
8
10
12
Chart12: Variance Decomposition of DY -84Q1 to 03Q1 100 90 80 70
DY DRINT DMB DX DG
60 50 40 30 20 10 0 1
2
3
4
5
6
7
8
9
10
11
12
Chart13: Variance Decomposition of DY -95Q1 to 03Q4 100 90
DY DRINT DMB DX
80
DG
70 60 50 40 30 20 10 0 1
2
3
4
5
6
7
8
9
10
11
12
