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Japan and the World Economy xxx (2008) xxx–xxx
www.elsevier.com/locate/jwe
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The boom and the bust of the Japanese economy:
A quantitative look at the period 1980–2000§
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Suparna Chakraborty *
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Department of Economics and Finance, Baruch College, CUNY, 55 Lexington Avenue, New York, NY 10010, United States
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Received 3 May 2007; received in revised form 21 December 2007; accepted 7 January 2008
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In this paper we quantitatively investigate the boom and the bust of the Japanese economy during 1980–2000 using the business cycle
accounting technique. This method helps us identify the distortion margins called ‘‘wedges’’ that played a significant role in accounting for the
output fluctuations. Applying our model to Japan, we find that efficiency and investment wedges can almost wholly account for output increases of
the 1980s. Labor wedges by themselves would have caused a recession beginning in late 1980s but was overwhelmed by the positive impact of
efficiency and investment wedges. In the 1990s, efficiency, labor and investment wedges all contributed to the recession. We next extend the
literature by conducting robustness tests to investigate the sensitivity of BCA results to small modifications in methodology.
# 2008 Elsevier Ltd All rights reserved.
JEL Classification: E3; F3; O4; O5
Keywords: Japan; Business cycle; Growth accounting; Stagnation
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1. Introduction
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After the World War II, Japan embarked upon a remarkable
journey of recovery and very soon became one of the most
developed economies of the 20th century, second only to United
States by many accounts. The average growth rate of output in
Japan grew at an unprecedented rate of 9 percent during the
1960s and 1970s when Japan was playing catch-up to the more
advanced economies of United States and Europe. Once Japan
recovered from the impact of war and the oil-price shocks, it
settled into a comfortable average economic growth rate of 3
percent in the late 1970s. By all accounts, this true phoenix
miracle had overcome all odds and emerged as the Asian giant.
However, what happened next resulted in one of the most
important business cycle episodes of the 20th century. During
the mid-1980s the Japanese economy saw once again a
dramatic growth spurt when average growth rate of per capita
output reached 5 percent. Unfortunately, this ‘‘Indian Summer’’
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§
This paper is based on the first chapter of my dissertation at University of
Minnesota.
Q1 * Tel.: +1 646 312 3465.
E-mail address: [email protected].
was short-lived and very soon the Japanese economy started to
slow down. By 2000, the average growth rate of the economy
was only 0.8 percent earning the 1990s in Japan the moniker ‘a
lost decade’.
In this paper, we quantitatively investigate the boom and the
bust of the Japanese economy during 1980–2000.
The business cycle experience of Japan has generated a lot of
interest among scholars and policy makers alike. Most studies
till date have concentrated on the lost decade of the 1990s.
Explanations for the lost decade range from the theories that
hold investment frictions responsible for the recession to the
theories that blame downturn in productivity. Advocates of the
investment friction theory include Hoshi et al. (2004) who hold Q2
weaknesses of the financial institutions responsible for the
economic downturn. Using micro-level data, the authors find
that the practice of evergreening the loans to non-performing
firms often caused deserving and productive firms to lose out on
available credit. Hoshi et al. argue that this phenomenon of
‘‘zombie’’ lending led to the economic downfall of Japan.
There are two alternative theories regarding financial frictions
in Japan. Kasa (1998) argues that falling asset and land prices
eroded value of collateral and thus the borrowing capacity of
firms which affected output and investment. Dekle and Kletzer
(2003) and Barseghyan (2006) in their analysis blames the
0922-1425/$ – see front matter # 2008 Elsevier Ltd All rights reserved.
doi:10.1016/j.japwor.2008.01.001
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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conditions that keep the economy from realizing its first best
outcome. So, one way of modelling the frictions might be as
time-varying taxes in a prototype growth model.
To apply the BCA procedure to Japan, we take a neoclassical
growth model with time varying efficiency, labor income taxes,
taxes on investment expenditure and government consumption.
Next we solve the growth model and use the decision rules from
the model and national income accounts data from Japan to
estimate the time series of the wedges. We then feed in the
wedges (efficiency, labor and investment) one by one and in
various combinations to account for fluctuations in output
during the period 1980–2000.
We find that efficiency and investment wedges jointly can
well explain the fluctuations in output during this period, with
declining investment wedges playing a particularly significant
role in accounting for a booming output during the 1980s.
Feeding in labor wedge alone, we find that our model generated
output starts to fall during the late 1980s and continues to fall
during the 1990s. Thus we conclude that the increase in output
during the 1980s was due to a combination of increases in
productivity and a decline in investment frictions whose effect
overcame the negative impact of an increase in labor frictions.
The falling output during the 1990s happened due to a
combination of three factors: declining productivity as well as
increases in labor market frictions and investment frictions.
Specifically, we find that efficiency wedges play a major role in
the bust of the 1990s (a finding that is consistent with Hayashi Q3
and Prescott, 2002). In addition, we find investment wedges
also played a significant role though the contribution of
investment wedges was less than that of productivity. Labor
wedges also add to the recession. One other study that applies
BCA to Japan is by Kobayashi and Inaba (2006). Our results
differ in that Kobayashi and Inaba finds limited role of
investment frictions. The difference seems to be caused by the
way investment wedges are measured. Kobayashi and Inaba
assumes perfect foresight and uses a deterministic model to
measure the investment wedge as opposed to our method where
we use a stochastic model. We explore the source of the
differences in our robustness analysis and find that the
difference lies not in using a perfect foresight as opposed to
a stochastic version of the business cycle model, but rather due
to the assumptions regarding the steady-state period that play
an important role in calculating the time series of the wedges (in
particular, the investment wedge) and to some extent due to the
differences in our data construction.1
Our BCA results suggest that financial factors and
productivity increases are a promising explanation of the
boom of the 1980s. As for the recession of the 1990s, the fall in
productivity accounts for a significant portion of the drop in
output followed by financial frictions. Labor market frictions
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existence of non-performing loans, combined with a delay in
government’s bailout of the financial sector in crisis (the first
time that government injected funds into the troubled financial
sector was in 1999) for a fall in investment as well as an
endogenous decline in labor and productivity which led to a
significant output drop.
The view that falling productivity during the 1990s brought
about the recession has been forwarded by Prescott and Hayashi
(2002). The authors used a neoclassical model with exogenous
TFP shocks calibrated to the Japanese economy and replicated
the economic experience of Japan in the 1990s. Prescott and
Hayashi found that TFP fluctuations alone almost wholly
account for the output fluctuations in Japan. This relatively new
view has gained ground in recent years. Studies looking at
micro-evidence of productivity changes in Japan include the
study by Jorgenson and Motohashi (2005) who compare
productivity growth in Japan and the US. They find that the
contribution to productivity from the IT sector in Japan and US
is similar though the contribution from the non-IT sector in
Japan lags far behind that of US.
Studies about the growth spurt in the 1980s is limited. The
general consensus is that the government efforts to encourage
liberalization during the late 1970s and early 1980s bore fruit in
the late 1980s. The policies opened up the Japanese firms to
foreign investors which encouraged foreign direct investment in
the economy which in turn encouraged economic growth. One of
the reasons that we are interested in studying the 1980s is because
many academicians believe that the policies that brought about
the 1980s growth spurt might have left the economy more
vulnerable to external shocks and had contributed to the
recession. For example, liberalization of the 1980s encouraged
big manufacturing firms to borrow from foreign investors. The
domestic banks which had a high amount of deposits turned to
small firms and the real estate sector for clients which mostly
offered land as a collateral asset. The subsequent decline of land
prices left most of the banks with a huge proportion of nonperforming loans as the reduced value of collateral was not
capable of covering the loan loss. Given that the seeds of the
1990s recession might lie in the policies that encouraged the
1980s boom, it is in our view necessary to quantitatively
investigate the 1980s to better understand the 1990s.
In this paper we extend the existing literature by conducting
a quantitative analysis for the boom period of the 1980s in
addition to the bust of the 1990s. Furthermore, our results of
applying the BCA procedure to Japan also help us to infer
which of the commonly forwarded explanations of the boom
and bust of the Japanese economy are quantitatively most
promising.
The accounting technique we use is business cycle
accounting (BCA) which was developed by Mulligan (2002)
and Chari et al. (2002a, 2006). The BCA technique is based on
the fundamental observation that in large classes of general
equilibrium models of frictions, the frictions appear as wedges
in the necessary first-order conditions, resulting in distortions
that keep the economy from achieving efficiency. The role
played by frictions is thus similar to the role played by taxes in a
growth model which also appear as wedges in the first-order
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For example, while converting an open economy national income accounts
to that of a closed economy, Kobayashi and Inaba (2006) adds net exports to
investment while we add it to consumption that alters the capital output ratio in
our studies. Also we use the Hayashi and Prescott dataset primarily based on
SNA 68 while Kobayashi and Inaba’s dataset is based on SNA 93.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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2. Business cycle accounting
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To apply the BCA procedure, we use a standard growth
model with four stochastic variables or wedges: efficiency
wedge At , which appears like time varying productivity; the
labor wedge tnt , which acts like a time varying tax on labor
income, the investment wedge t xt , which acts like a tax on
investment expenditure and per capita government expenditure
gt , that is also considered as a ‘wedge’. It should be emphasized
that each of the wedges represents the overall distortion to
relevant first-order conditions and do not identify the primitives
driving these wedges.
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2.1. Theoretical model
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I assume that the economy every period comprises of a
measure N t of identical and infinitely lived agents who are
endowed with one unit of time that can be used for work or
leisure. The economy also consists of measure one of identical
firms that own the production process. For purposes of analysis,
I assume that population grows at a constant rate h every period,
where the population growth rate is exogenous to the model.
There is one output that is produced, invested and consumed in
the economy. The government collects income and investment
taxes from the economic agents and uses the proceeds to
finance government expenditure and makes transfers to the
households such that the budget is balanced. Given the structure
of the economy, we can summarize the problems facing the
agents of the economy as.
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2.1.1. Representative consumer’s problem
The representative consumer in the economy chooses per
period consumption ct and labor lt to maximize present
discounted value of lifetime utility. The consumer receives
income from two sources: labor income and rental income from
capital. In addition, the consumer also receives some transfers
from the government. The proceeds of the income and transfers
are used to finance consumption and investment expenses.
Further, every period, the consumer has to pay income and
investment taxes to the government at an exogenously
determined rate. Thus the representative consumer’s problem
can be written as
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The rest of the paper is arranged as follows. In Section 2 we
outline the model used in our paper. In Section 3 we first present
the results of calculating the realized value of the wedges using
the data and the model decision rules. We next feed in the
wedges one by one and in various combinations in our model
and report the results. Section 4 conducts some robustness
checks while Section 5 concludes.
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which had been worsening since the late 1980s compounded the
recession.
The wedges, as mentioned in earlier paragraphs, appear in a
growth model as time varying taxes. How are these wedges
connected to the effective tax rates in Japan? In other words,
could the changes in tax rates been partly responsible for the
wedges? To answer this question we calculate the correlation
between the wedges and the effective tax rates from Mendoza
et al. (1994) updated till 1996. We divide our analysis between
two subperiods: 1980–1991 and 1991–1996. We find that
during the first subperiod, the labor income tax rate is
significantly positively correlated with labor wedge but during
the later subperiod, the correlation turns negative. This
indicates that increases in labor income taxes could have
significantly contributed to the labor wedge in the 1980s but
during the 1990s, other factors contributed to the labor wedge
that continued to increase despite a decline in labor taxes. For
our analysis, this implies that increasing labor taxes would have
actually dampened the growth of Japan in the 1980s if not
overshadowed by other factors that were conducive to growth.
One such factor is the growing TFP. In the 1990s, labor taxes
fell but were not enough to stop the downslide.
As for correlation between investment wedges and the
effective tax, the correlation is negative in the 1980s but
positive in the 1990s. This leads us to conclude that despite of
increases in taxes, other factors contributed to the decline in
investment wedge. This observation is quite interesting as it
helps us see the distinction between wedges and effective taxes.
Taxes are just one of the many factors that influence the wedges.
As for investment taxes, they were growing during the 1980s
which by itself means increasing cost of investment hence is not
conducive to growth. However, government policies can also
influence investment wedge. For example, 1980s saw an
unprecedented spurt of financial liberalization in Japan which
encouraged investment and growth despite increasing investment taxes. The impact of liberalization would be captured in a
declining investment wedge. During the 1990s, increases in
taxes contributed to increases in investment wedges.
Though we analyze the Japanese case, we wish to emphasize
that the methodology of our paper is applicable to any instances
of business cycle fluctuations and adds to current literature in
the following way: as a business cycle researcher, once we
identify some primitive frictions that we think look promising
in explaining the economic fluctuations, we have a hard time in
figuring out how we can introduce these frictions in a model
such that we can replicate the data. This paper helps us with this
choice by identifying propagation channels that are most
promising in accounting for the economic fluctuations in Japan.
Given the paucity of detailed models of frictions that have been
used to study the Japanese economy, we hope that this paper
would help researchers in constructing such models by giving
them, a priori, an idea as how to model the frictions such that
the model can replicate the data.
Thus our paper can be used as a guide on how to use business
cycle accounting to construct detailed models of frictions such
that the model is successful in numerically accounting for
economic fluctuations.
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1
X
bt uðct ; 1 lt Þ
max
E0
subject to
ð1Þ ct þ ð1 þ txt Þxt wt lt ð1 tnt Þ þ r t kt þ Tr t
ð2Þ ktþ1 ð1 dÞkt þ xt
ð3Þ nonnegativity constraints
t¼0
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2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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2.1.2. Representative firm’s problem
Every period, the representative firm produces a single
output yt using labor and capital to maximize profits. Output is
subject to an exogenously given production technology. Hence
the representative firm’s problem every period is given by
max
subject to
yt wt lt r t kt
yt Fðkt ; At lt Þ
where At denotes productivity. For my analysis I assume that
the production technology is labor augmenting. I further
assume that the long run rate of technical progress is given
by ð1 þ gz Þ.
2.1.3. Government and resource constraints
The government maintains a balanced budget every period
such that
gt þ Tr t ¼ tnt wt lt þ t xt xt
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where gt denotes per capita government expenditure in period t.
The resource constraint in the economy is given by
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c t þ x t þ gt y t
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2.1.4. Equilibrium
The equilibrium in this economy is given by a vector of price
functions fwt ; r t g1
t¼0 and a vector of allocation functions
fct ; lt xt ; yt g1
such
that the price and allocation functions
t¼0
satisfy the first-order conditions given by
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c t þ x t þ gt ¼ y t
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yt ¼ Fðkt ; At lt Þ
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unt ðct ; lt Þ
¼ ð1 tnt ÞF lt ðkt ; At lt Þ
uct ðct ; lt Þ
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bEt uctþ1 ðctþ1 ; ltþ1 ÞfF ktþ1 ðktþ1 ; Atþ1 ltþ1 Þ þ ð1 dÞð1 þ txtþ1 Þg
¼ ð1 þ t xt Þuct ðct ; lt Þ
(4)
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(1)
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For our quantitative analysis, we assume a Cobb–Douglas
production function and a standard monotonically increasing
and strictly concave utility function represented by
uðct ; lt Þ ¼
¼
1s
cat ð1 lt Þ1a
;
1s
a log ct þ ð1 aÞ log ð1 lt Þ;
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(2)
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2.2. Application to Japan
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period. Note that the time varying productivity and taxes on
labor income and investment expenditure distort the first-order
conditions and keep the economy from achieving the first best
outcome.
Note that the wedges represent more than just taxes. Any
friction that leads to a discrepancy between the marginal
product of labor and the marginal rate of substitution between
leisure and consumption is captured by the labor wedge tnt .
Changes in government policy leading to liberalization would
have the effect of lowering costs of investment and would show
up as a decline in t xt : Similarly, all the other wedges capture a
host of other possible distortions. In their analysis, Chari et al.
(2002a, 2006) establish this result theoretically by showing a
host of equivalence relations. For example, input-financing
frictions are shown to map into efficiency wedges, fluctuations
in net exports in an open economy model map into the
government wedge and sticky wages and monetary shocks map
into labor wedges. Financial frictions, like monitoring cost of
lending map into an investment wedge.
For our numerical exercise, the wedges are measured using
data and the first-order conditions of the model so that the
model replicates the data exactly when all the wedges are
jointly fed. The evaluation of the model takes the form of
feeding in the calculated value of the wedges one by one and in
various combinations in the model and identifying the ones that
are needed to best replicate the data, keeping in mind that by
construction, feeding in all the wedges jointly will exactly
replicate the data.2
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where kt denotes capital stock in period t, xt denotes per capita
investment, after-tax labor income is given by wt lt ð1 t nt Þ and
rental income is r t kt . As for the other notations used, wt is the
wage rate and r t is the rental rate on capital stock. The time
discount factor is denoted by b and the depreciation rate of
capital is d. Tr t denotes transfers from the government received
at period t and txt is the investment tax rate.
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(3)
where notations like uct , unt , F lt , F kt , etc. denote the first
derivative of the utility function and production function with
respect to different arguments like consumption, labor, and
capital. Eq. (1) is the resource constraint. Eq. (2) is the
production technology constraint. Eq. (3) denotes that in equilibrium the marginal rate of substitution between consumption
and leisure is equal to the after tax marginal return to labor.
Eq. (4) is the inter-temporal equation taking into account the
fact that in equilibrium, rental rate on capital is equal to the
marginal product of capital. The four equations outlined above
summarizes the equilibrium conditions of the economy every
yt ¼ ktu ðAt lt Þ1u
ŷt ¼
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when s 6¼ 1
when s ¼ 1
(5)
(6)
Note that on a balanced growth path, the variables ct , ktþ1 , yt ,
and gt grow at a rate ð1 þ gz Þ. Taking into account the
population growth rate h, assuming that s ¼ 1, and discounting
the model variables with respect to their long term trend
ð1 þ gz Þ, the fundamental equations of our model reduce to
u
1u
k̂t ðÂt lt Þ
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(7)
2
The other studies in literature that we are aware of that also uses the BCA
approach to study business cycle fluctuations are those of Chari et al. (2002a,
2006) who use the BCA approach to study the Great Depression in United
States, Canada and Germany, Kobayashi and Inaba (2006) who study the
Q4 Japanese lost decade, Wynne et al. (2006) who apply BCA to the Irish economy
and Kersting (2006) who studies the UK economy during the Thatcher regime.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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1a
a
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value of the labor wedge tn ¼ 0:54 and that of investment
wedge tx ¼ 0:48. Note that our results are quite similar to
Kobayashi and Inaba (2006) who uses the same technique and
get tn ¼ 0:59 and t x ¼ 0:33 as the mean value during the
period 1980–1984 (the differences are partly due to data
construction3).
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ĉt
ŷ
¼ ð1 uÞð1 tnt Þ t
(8)
1 lt
lt
b
ĉt
ŷtþ1
Et
þ ð1 dÞð1 þ t xtþ1 Þ ¼ ð1 þ txt Þ
u
ĉtþ1
ð1 þ gz Þ
k̂tþ1
(9)
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ĉt þ ð1 þ hÞ ð1 þ gz Þk̂tþ1 ð1 dÞk̂t þ ĝt ¼ ŷt
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where we denote a variable zt detrended by the long-term
growth rate of technological development ð1 þ gz Þt as ẑt , where
ẑt ¼ zt =ð1 þ gz Þt .
Given the wedges Ât , t nt , txt , and ĝt , and the predetermined
capital stock kðtÞ, Eqs. (7)–(10) solve for output, investment,
consumption and labor in terms of the wedges and capital
stock.
To solve the model we first need to estimate the parameters
fu; a; d; bg but the usual calibration technique is not very useful
here as we do not know the steady-state values for the wedges.
Therefore, we need to choose the parameters from literature.
We choose the capital share u ¼ 0:36; discount factor
b ¼ 0:972; depreciation rate d ¼ 0:089 and time allocation
parameter ð1 aÞ=a ¼ 1:13 (the parameters are from Prescott
and Hayashi, 2002). The time endowment is taken as 5000 h
annually. We further assume that long-term growth rate of the
per capita output is 2.15 percent, the average over the period
1960–2000, which is slightly higher than the long-term growth
rate of 2 percent in United States. This gives the value of
ð1 þ gz Þ which is 1.0215. The population growth rate h ¼ 0:01
which is the average growth rate of population in Japan during
the period 1960–2000. Given the parameter values, the steadystate values of the wedges can be estimated by solving the
steady-state equations such that the moments of the data during
the steady-state match the moments of the model. We assume
that Japanese economy was in a steady state during the period
1980–1984. The steady-state values of output, labor, government consumption and the capital output ratio are taken from
the Hayashi–Prescott data set as the average value of these
variables during the period 1980–1984. The steady-state
equations of the model are summarized by
2.2.1. Measuring the wedges
The accounting procedure has two parts: first we need to
estimate the wedges from the data and then we feed in the
wedges in our model to generate output, labor, consumption
and investment. This latter procedure is called decomposition.
Note that by construction of the BCA procedure, if we feed in
efficiency, labor, investment and government wedge in the
model all together, then we will get back the data.
The data we consider is the national income accounts of
Japan during 1980–2000 that gives us the time series of output,
consumption, government expenditure and investment. Since
ours is a closed economy model, we add net exports to
consumption following Chari et al. (2002a). The time series of
capital and labor is available from Hayashi and Prescott (2002).
Given the time series data and Eqs. (7) and (8), it is
straightforward to calculate efficiency and labor wedge. The
government wedge is taken directly from the data and is equal
to government consumption.
However, calculation of the investment wedge is more
difficult as it involves people’s expectations about the
persistence of the wedges. To this end, we need to define a
stochastic process underlying the wedges.
We
specify a vector
AR 1 process for wedges
st ¼ log At ; tnt; txt ; log gt :
stþ1 ¼ P0 þ Pst þ Qetþ1
(15)
442
443
444
We assume that the errors are i.i.d. over time and are
distributed normally with mean zero and covariance matrix V.
To ensure that V is positive semi-definite, we further estimate
0
the matrix Q where V ¼ QQ . Next we use the first-order
conditions of the model along with the four equations
underlying the vector autoregressive AR1 process to estimate
the parameters underlying the AR1 process for wedges. We use
the standard maximum likelihood procedure along with data on
output, consumption, investment, labor and government
expenditure to estimate the parameters underlying the
stochastic process.4
For solving the model we employ the technique of loglinearization around the steady state (King et al., 1988) and then
the method of undetermined coefficients.
Log-linearizing the equations around the steady state we get:
445
ỹt uk̃t ð1 uÞãt ð1 uÞl̃t ¼ 0
460
459
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
F
OO
376
PR
375
ED
374
CT
372
373
(10)
RR
E
371
u
1u
CO
370
ȳ ¼ k̄ ðĀl̄Þ
c̄
ȳ
1a
¼ ð1 uÞð1 tn Þ
a
1 l̄
l̄
ȳ
b
u þ ð1 dÞð1 þ tx Þ ¼ ð1 þ t x Þ
ð1 þ gz Þ
k̄
UN
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c̄ þ ð1 þ hÞ ð1 þ gz Þk̄ ð1 dÞk̄ þ ḡ ¼ ȳ
(11)
(12)
(13)
(14)
Thus the technique used here can be looked upon as a
‘‘dual’’ to the usual calibration technique used in real business
cycle models where we use the data to estimate the parameter
values. In BCA, wedges that are essentially distortions in the
market, do not have any numerical counterpart in the data. So,
we use the parameter values from literature and the data on
national income accounts and employment to derive the
steady-state value of the wedges. In our set-up, the steady-state
(16)
3
Kobayashi and Inaba (2006) add net exports to investment while we add it to
consumption. There are various ways of doing this adjustment in literature. For
example, Christiano and Davis (2006) add net exports to government consumption.
4
As in Chari et al. (2002a), we assume that the government consumption
wedge is not correlated with the other wedges.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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S. Chakraborty / Japan and the World Economy xxx (2008) xxx–xxx
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463
1
1
ỹt c̃t l̃t t̃nt ¼ 0
1 t̄ n
1 l̄
y
u Et ðỹtþ1 k̃tþ1 Þ þ ð1 dÞ ð1 þ t̄x ÞEt t̃xtþ1
k
1 þ gz
1 þ gz
1 þ gz
Et c̃tþ1 t̃xt þ
c̃t ¼ 0
b
b
b
465
464
cc̃t þ kð1 þ hÞ ð1 þ gz Þk̃tþ1 ð1 dÞkk̃t þ gg̃t yỹt ¼ 0
(19)
(17)
(18)
F
462
By construction of BCA procedure, if we feed in all the
wedges jointly in the model, then the model predictions exactly
match the data. We use this fact to calculate the realized value
of the wedges.
As mentioned earlier, we measure government wedge series,
gt directly from the data, where gt is government consumption.
To obtain the value of efficiency wedge, labor wedge and
investment wedge, we use data and the model’s decision rules
along with the parameters underlying the stochastic process for
the wedges.
Thus the realized value of the wedges can be estimated by
solving the equations:
t̃ nt ¼ tnt t̄ n ;
468
i.e. for the taxes, a variable with tilda denotes the deviation from
the steady state (not log deviation). This is consistent with other
BCA literature.
For the remaining macro variables and productivity variable,
a variable with tilda denotes the log- deviation of the variable
from its steady state.
Let us define the vector s̃t ¼ ãt ; t̃nt; t̃ xt ; g̃t . Given s̃t and k̃t ,
the log-linearization exercise yields the decision rules of the
form:
469
470
471
472
473
474
(22)
479
480
481
l̃t ¼ l̃t ðs̃t ; k̃t Þ
482
483
484
Writing the above equations in the matrix form we get:
2
3
ỹðtÞ
6 c̃ðtÞ 7
7
YðtÞ ¼ 6
4 l̃ðtÞ 5
x̃ðtÞ
494
495
496
497
498
499
500
501
502
RR
E
CO
UN
493
YðtÞ ¼ DXðtÞ þ jðtÞ
The matrix M summarizes the coefficients linking k̃ðt þ 1Þ
to XðtÞ and the matrix P from the AR(1) process underlying the
wedges, and the matrix D summarizes the coefficients linking
YðtÞ to XðtÞ. We use the Kalman filter to get to the likelihood
function to be maximized. The filter gives us one period ahead
predictions that are then compared with data. The difference
between the data and the predictions enter the likelihood
function. Once we have the parameters, we have the stochastic
process and we can use it to calculate the realized value of the
wedges. For further technical details, the interested reader
might refer to the Technical Appendix of Chari et al. (2002a).
513
518
x̃t ¼ x̃t ðs̃t ; k̃t Þ
Xðt þ 1Þ ¼ MXðtÞ þ Neðt þ 1Þ;
512
with k̃tþ1 ¼ ð1 dÞk̃t þ x̃t ðs̃t ; k̃t Þ and g̃t ¼ g̃dat
t where variables
with superscript dat are the observed data.
Once we have a numerical measure of the wedges, our next
step is to feed them into the model separately and in various
combinations to assess what fraction of fluctuations in output
that can be accounted for by various combinations of wedges,
thus letting us assess the importance of various wedges in
accounting for the lost decade. This exercise is referred to as
decomposition.
478
492
511
517
516
(21)
489
490
491
510
(26)
¼ l̃t ðs̃t ; k̃t Þ
c̃t ¼ c̃t ðs̃t ; k̃t Þ
Next we can write the equations in the state-space form as
509
515
477
488
508
(25)
(20)
485
486
487
506
507
x̃dat
t ¼ x̃t ðs̃t ; k̃t Þ
ỹt ¼ ỹt ðs̃t ; k̃t Þ
3
k̃ðtÞ
6 ãðtÞ 7
6
7
7
XðtÞ ¼ 6
6 t̃n ðtÞ 7
4 t̃x ðtÞ 5
g̃ðtÞ
505
514
476
2
504
(24)
dat
l̃t
(23)
503
ỹdat
t ¼ ỹt ðs̃t ; k̃t Þ
CT
475
t̃xt ¼ t xt t̄x
ED
467
466
OO
where
PR
6
460
459
519
520
521
522
523
524
525
526
2.2.2. Decomposition
Our accounting procedure decomposes movements in
variables from an initial date with an initial capital stock into
four components consisting of movements driven by each of the
four wedges away from their values at the initial date. We
construct these components as follows. Define the efficiency
component of the wedges by setting s̃1t ¼ fÃt ; t̃ n0; t̃x0 ; g̃0 g
where s̃1t is the vector of deviation of wedges in period t from
their steady-state values, where the efficiency wedge takes on
its period t value while the other wedges stay at their initial, i.e.
steady-state value. First we generate the capital stock series by
k̃tþ1 ¼ k̃tþ1 ðs̃1t ; k̃t Þ where k̃tþ1 ðs̃1t ; k̃t Þ is the estimated decision
rule of the capital stock next period.
Then, using the vector of wedges, s̃1t ¼ Ãt ; t̃ n0; t̃x0 ; g̃0 the
estimated capital stock series k̃tþ1 and the decision rules
estimated, we could get the movements in the decision variables
due to the efficiency component only. Thus, we can get:
527
ỹ1t ¼ ỹt ðs̃1t ; k̃t Þ
(27)
543
x̃1t ¼ x̃t ðs̃1t ; k̃t Þ
(28)
544
l̃1t ¼ l̃t ðs̃1t ; k̃t Þ
(29)
545
546
547
We can analogously get movements in decision variables
due to other components like labor or investment wedge,
also in different combinations of the wedges. For example,
we can define efficiency and labor component as s̃12t ¼
fÃt ; t̃ nt ; t̃x0 ; g̃0 g and proceed with decomposition where
efficiency and labor wedges take on their period t value,
respectively, while investment and government wedges remain
548
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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529
530
531
532
533
534
535
536
537
538
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540
541
542
549
550
551
552
553
554
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JAPWOR 667 1–17
S. Chakraborty / Japan and the World Economy xxx (2008) xxx–xxx
7
Table 1
Stylized facts of (a) the Japanese economy and (b) the US economy
Q5
Years
1970:1980
1980:1984
1985:1991
1991:2000
(a) Japanese economy
Growth rate of y (%)
c=y
x=y
g=y
9.38
0.57
0.29
0.13
3.24
0.54
0.33
0.19
1.8
0.54
0.31
0.14
4.25
0.54
0.31
0.13
0.79
0.54
0.28
0.15
(b) US economy
Growth rate of y (%)
c=y
x=y
g=y
2.77
0.65
0.17
0.18
2.24
0.68
0.18
0.15
2.28
0.68
0.18
0.15
1.79
0.69
0.19
0.14
2.33
0.69
0.22
0.12
OO
F
1960:1970
PR
Source: PWTs 6.2. Note: For the comparison of the US economy and the Japanese economy, we use the data from Penn World Tables 6.2 to give us consistent
estimates. In our data analysis for the rest of the paper, we make use of the more detailed Hayashi and Prescott (2002) dataset that is based on SNA 1968.
554
555
ỹ12t ¼ ỹt ðs̃12t ; k̃t Þ
(30)
556
x̃12t ¼ x̃t ðs̃12t ; k̃t Þ
(31)
l̃12t ¼ l̃t ðs̃12t ; k̃t Þ
(32)
558
557
559
560
For our results that we subsequently illustrate we perform
such decompositions for all possible combinations of wedges.
562
3. Results
563
3.1. Stylized facts of Japanese economy
CT
561
564
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
RR
E
567
CO
566
We begin with the stylized facts about the Japanese economy
and compare it with the US experience. We go back to the 1960s
to chart the progress of the two economies over the decades. The
data in Table 1(a) and (b) are from Penn World Tables 6.2. During
the 1960s, the average growth rate of per capita output y in Japan
was 9.38 percent as compared to 2.8 percent in the US. This is to
be expected given that this was the period of reconstruction
following the World War II. Japan enacted many pro-growth,
export-oriented policies that gave a boost to the economy.
During the 1970s the growth rate was 3.24 percent (2.24
percent in US). Given the oil-price shocks that hit the world in
early 1970s and then the second oil-price shock during the late
1970s, Japan still managed to maintain an impressive growth
rate. The economy gradually stabilized in early 1980s and the
growth rate in output was close to that of US (1.84 percent in
Japan as compared to 2.28 percent in US). Given this
observation it is probably reasonable to infer that Japan had
reached a balanced growth path during 1980–1984. On the
policy front, this period saw a host of liberalization policies
including reducing tariffs and allowing foreign direct investment which gave the economy another much needed boost.
The benefits of the liberalization policies were realized in
the mid-1980s. The average growth rate of output during 1985–
1991 was 4.3 percent as compared to 1.8 percent in US. This
period also saw a boom in asset and land prices that almost
doubled by 1989. However in 1988 there was a major change in
UN
565
labor market policies. The Labor Standards Law was revised
and workweek hours was reduced from 48 to 40. The
government also reduced the workdays from 6 to 5 along with
increases in vacation days. The financial institutions also saw
some major changes. The Basel Committee proposed and
enacted the Basel One accord in 1988 that increased the
regulatory capital requirements of banks worldwide. Most of
the Japanese banks came to follow the Basel requirements by
1992. Prescott and Hayashi (2002) also finds significant
declines in productivity during this period. Whether as a result
of these factors, or some other reasons, the Japanese economy
went into a recession. The average growth rate of output fell to
0.8 percent as compared to 2.33 percent in US.
We also look at the share of consumption ðc=yÞ, investment
ðx=yÞ and government consumption ðg=yÞ in output since 1960.
The share of consumption in Japan was 57 percent in the 1960s
and then stabilized to 54 percent as compared to 69 percent in
United States. The lower share of consumption in output meant
an increased share of investment. Investment to GDP ratio
maintained an average of 31 percent as compared to 18 percent
in United States. However during the 1990s, the share fell to 28
percent in Japan whereas the share of investment in output
increased to 22 percent in US. Government expenditure in
Japan, on the other hand, increased from 13 percent to 15
percent between the 1980s and the 1990s, while government
expenditure in US fell from 14 percent to 12 percent.
For our analysis we concentrate on the period 1980–2000
and for sake of brevity in our analysis we focus only on output
per capita. In our detailed analysis, that the interested reader
can obtain from us, we also measure our model’s performance
with respect to capital output ratio as an additional test that we
don’t present in this draft.5 In Fig. 1, we depict the detrended
output where the 1980 value is normalized to a 100. The
average growth rate of per capita GDP during late 1980s was
1.39 percent above trend but during the 1990s, it fell to 1
percent below trend level.
ED
at their initial date value. Now we can get:
5
For details on data construction, please refer to data appendix in
Appendix A.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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590
591
592
593
594
595
596
597
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599
600
601
602
603
604
605
606
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612
613
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615
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619
620
621
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623
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S. Chakraborty / Japan and the World Economy xxx (2008) xxx–xxx
Table 2a
Parameters of the vector autoregressive stochastic process for the wedges
on lagged states
3
0:019
0
7
ð0:09Þ
7
0:043
0 7
7
7
ð0:027Þ
7
0:836
0 7
7
7
ð0:158Þ
7
0
0:93 5
ð0:079Þ
F
Coefficient matrix of P
2
0:935
0:44
6 ð0:1Þ
ð0:29Þ
6
6 0:034
0:93
6
6 ð0:03Þ ð0:088Þ
6
6 0:068 0:049
6
6 ð0:14Þ ð0:31Þ
6
4 0
0
OO
Coefficient matrix Q ¼ VV 0
2
3
0:042
0
0
0
6 0:004 0:0082
0
0 7
6
7
4 0:031
0:002 0:033
0 5
0:00062 0:004 0:0136 0:006
Fig. 1. Per capita output. Note: This figure depicts the per capita output in Japan
during 1980–2000. The data is detrended by the long-term growth rate and the
value in 1980 is set equal to 100.
PR
Estimated using Japanese data during the period 1980–2000. Note: Matrix P
outlines the stochastic process underlying the wedges. The standard deviations
are given in the parentheses. V is the variance-covariance matrix that is positive
definite.
626
627
628
629
The objective of our decomposition exercise is to account for
these observed fluctuations in per capita output and to evaluate
the contribution of efficiency, labor and investment wedge in
Japanese economy.
3.2. Realized wedges
630
636
637
638
639
640
641
642
643
CT
635
RR
E
634
CO
632
633
In Section 2.2.1 we have described the method of measuring
the realized wedges using the decision rules of our model and
the data. Fig. 2 graphically depicts output and the realized
wedges. The parameters underlying the vector autoregressive
process of the wedges are outlined in Table 2a. In Table 2b, we
trace the changes in per capita output with respect to the long
term trend and the wedges over two subperiods. During the
1980s, output on average increases by 1.1 percent between
1980 and 1991 which is potentially aided by a 0.12 percent
increase in efficiency, a 2.65 percent drop in investment wedges
and an average increase in government consumption by 0.63
percent. The labor wedge, on the other hand, increases by 0.36
percent on average that dampens the output to some extent.
UN
631
However, during the 1990s, per capita output on average falls
by 0.62 percent. During this period, efficiency wedge falls by
0.76 percent, while labor wedge and investment wedge on
average increases by 0.49 percent and 1.25 percent, respectively. Thus, we suspect that during the 1990s, the fall in
efficiency as well as an increase in both labor and investment
wedge causes the depression in output. On the other hand,
government tried to stall the fall in output by increasing
government consumption by an average of 1.07 percent. The
economic intuition of our result is that according to our model,
at least on the face value, the wedges are equivalent to
productivity, labor income taxes and taxes on investment
expenditure. Hence increases in efficiency wedge and declines
in investment wedge in the 1980s are conducive to economic
growth and the fall in efficiency wedge and increases in
investment wedge in the 1990s are conducive to economic
depression. On the other hand, the fact that labor wedge has
registered a continuous increase since mid-1980s tells us that
had it been labor wedges alone, Japan would have experienced
an economic decline since the mid-1980s. We therefore
conclude that increasing labor wedges further added to the
economic decline of the 1990s but it is not important for the
boom during the 1980s. The government consumption wedge
ED
625
Fig. 2. Per capita output and wedges. Note: In this figure, we plot efficiency,
labor, investment and government consumption wedge calculated from our
stochastic model along with per capita output. The value for the wedges and
output in 1980 is set to 100 and we trace the fluctuations over time.
Table 2b
Evolution of per capita output and realized wedges over time (1980:2000)
Wedges
Output
Efficiency
Labor
Investment
Government consumption
Average percentage changes (in
value of wedges over time (in %))
1980:1991
1991:2000
1.1
0.12
0.36
2.65
0.63
0.62
0.76
0.49
1.28
1.07
The unconditional mean and standard deviation (in parentheses) of labor and
investment wedges, respectively, during 1980:2000: f0:562 ð0:02Þ and
0:428 ð0:04Þg. Note: The average percentage changes in per capita output
(with respect to the long term trend) and the wedges over the subperiods.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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644
645
646
647
648
649
650
651
652
653
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655
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658
659
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S. Chakraborty / Japan and the World Economy xxx (2008) xxx–xxx
9
Table 3
Properties of wedges
Wedges
Standard deviation (relative to output)
Cross correlation of wedge (with output at lag k)
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
0.84
0.93
0.93
0.58
0.95
0.91
0.93
0.83
0.84
0.93
0.64
0.95
PR
These summary statistics seems to suggest that if we want to
account both for the boom as well as the depression of the
Japanese economy since 1985, then we need to concentrate on
frictions that affect productivity or investment markets.
Introducing frictions as fluctuations in labor wedges will not
help account for the 1980s’ boom though it does amplify the
recession of the 1990s. How far is our analysis accurate? We
answer this next by feeding in the wedges one by one and in
various combinations in our model and reporting the results.
ED
674
0.9
0.91
0.99
0.48
3.2.1. Decomposition outcome
First, we feed the wedges one by one in our model and
compare the model outcomes with that of data. Fig. 3 depicts
the model outcome and data on output per capita. During the
period 1980–1991 output per capita increases by 12.2 percent
and falls by 6.6 percent by 2000 compared to the long term
trend of 2.15 percent. Feeding efficiency wedge alone in the
model, we find that the model generated output per capita
increases by 3.8 percent by 1991 and falls by 4.5 percent by
2000, and feeding investment wedge alone model output shows
an increase by 8.5 percent and fall by 1.2 percent. These results
suggest that efficiency and investment wedges jointly would
almost wholly account for fluctuations in output leaving a
limited role for labor wedges. In fact, if we feed labor wedge
alone in our model, we find that model generated output
declines by 0.3 percent between 1986 and 2000. These results
also suggest that investment wedges were particularly
important for generating the economic boom during the
1980s and efficiency wedges were in large part responsible for
the economic decline during the 1990s.6
Next, to provide further support to our conclusion that
efficiency and investment wedges jointly account for a large
portion of the economic fluctuations and labor wedge play a
CT
673
RR
E
672
CO
670
671
grows throughout the 1990s above trend. However even an
increase in government consumption was not enough to prevent
the downturn of the 1990s. The next question we tackle is
whether the correlations of output with wedges can shed further
light on this issue?
In Table 3(a) and (b) we depicts the correlations of output
with the wedges during the two sub-periods. Over both sub
periods, output is positively correlated with efficiency wedge
and negatively with investment wedge. As for labor wedge, we
find that the cross-correlation of output with labor wedge is
positive in the 1980s and negative in the 1990s. Looking at
efficiency and investment wedges, we find that efficiency
wedge was increasing during the 1980s whereas investment
wedge was decreasing. Both these developments are conducive
to economic growth and this is further supported by correlation
numbers. As for labor, labor wedge started to increase since the
late 1980s which we expect to dampen growth. So, it looks like
efficiency and investment wedges overwhelmed the impact of
labor wedges and resulted in an economic boom. In the 1990s,
efficiency wedge declined whereas investment and labor wedge
was increasing. Thus all three wedges are promising for
explaining the recession and the correlation numbers support
this conclusion.
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669
0.64
0.8
0.98
0.51
0.91
0.76
0.8
0.75
666
668
1
OO
(b) Summary statistics during the period 1991:2000
Efficiency
0.43
Labor
0.29
Investment
0.86
Government consumption
0.58
667
0
F
(a) Summary statistics during the period 1980:1991
Efficiency
0.2
Labor
0.21
Investment
1.04
Government consumption
0.19
1
Fig. 3. Per capita output: data and model predictions feeding a single wedge at a
time. Note: In this figure, we plot the per capita output generated by our
stochastic BCA model when we feed one wedge at a time and compare it with
the data.
6
To confirm our findings, we have also plotted the model generated capital
output ratio against data. The results are not presented here but interested
readers can get it from the author. We find that both efficiency and investment
wedges perform well in generating capital output ratios that closely matches
data. Efficiency wedge alone shows an increase in capital output ratio from 1.74
to 1.83 in 2000. Labor wedge alone predicts a capital output ratio of 1.85 by
2000 whereas investment wedge alone predicts a capital output ratio of 2 by
2000 as compared to 2.5 in the data.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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Japan? We look at the correlation of the labor wedge with
effective labor income tax rate and the correlation of the
investment wedge with the effective consumption tax rate that
is available for Japan. The tax data is collected from Mendoza
et al. (1994) updated on Mendoza’s website that provides us
with effective labor income taxes and consumption taxes (there
is no data on investment taxes so we use consumption taxes
instead as in a growth model tax on consumption is equivalent
to a tax on investment. The time period under consideration is
1980–1996 (the effective tax rate data is only available till
1996)). We plot the correlations overall and in two subperiods:
1980–1991 and 1991–1996. The results are presented in Table 4
and the graphical representation is in Fig. 5.
We find that during the entire period 1980–1996, the
correlation between labor income tax and labor wedge is 0.72.
During the first subperiod (1980–1991), the correlation is 0.75
and in the latter subperiod (1991–1996), the correlation is
0.59. Given these figures, we deduce that the increase in
effective labor income taxes during the 1980s was a significant
factor in increases in labor wedge. However, this changed in the
1990s. Labor wedge was still increasing though effective labor
income tax rate started to decline marginally. So, the labor
wedge during the 1990s reflect other frictions in the labor
market, possibly the frictions caused by Labor Standards Law,
that contributed to the recession in spite of the positive effect of
a declining labor income tax.
As for the investment wedge and the effective consumption
tax rate, the correlation during the period 1980–1996 stands at
0.05. During 1980–1991, the correlation is 0.047 and during
1991–1996, the correlation is 0.015. The results indicate that
though consumption taxes were increasing during the 1980s,
investment wedges were declining. This would reflect
relaxations in the investment market constraints and liberalization measures could have very well reduced the frictions and
significantly contributed to a decline in investment market
friction. In the 1990s, consumption tax rates continued to
increase and compounded the investment wedge. The correlation though positive is low, which indicates that other frictions
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minor role, we feed in efficiency and investment wedges jointly
in our model and hold labor and government wedge fixed at
their 1980 values. Considering both wedges jointly the model
can account for almost the entire fluctuations in output per
capita (Fig. 4).
Summarizing the results of our accounting procedure we
conclude that business cycle researchers interested in constructing detailed models of frictions to account for the
Japanese economy have to introduce the frictions in such a way
that they show up as efficiency and investment wedges in a
prototype growth model. More importantly, models of business
cycles in which frictions manifest themselves only as labor
wedges will not be very successful in accounting for the boom
during the 1980s and will only have a limited success in
accounting for the depression during the lost decade.
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Fig. 5. Wedges and effective tax rates (benchmark model). Note: In this figure,
we plot the wedges and compare the time trend of the wedges with the effective
tax rates from Mendoza et al. (1994). Note that the effective tax rate data in
Mendoza et al. (1994) has been updated only through 1996 so we truncate our
sample at 1996.
3.2.2. Wedges and effective tax rates
In the previous sections, we have calculated the realized
values of labor wedge and investment wedge. These wedges at
least on the face value look like labor income taxes and
investment taxes and represent the overall distortions in the
labor market and the investment market. One possible source of
the wedges are government policy changes that lead to changes
in effective tax rates. For example, if effective taxes vary a lot
over the business cycle, they would lead to distortions in the
first-order conditions and cause aggregate fluctuations. So a
natural extension of our study is to see to what extent are the
realized wedges in our model correlated to effective tax rates in
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Fig. 4. Per capita output: data and model predictions feeding efficiency and
investment wedges jointly in our model. Note: In this figure, we plot the per
capita output generated by our stochastic BCA model when we feed efficiency
and investment wedges jointly and compare it with the data.
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10
Table 4
Correlation of wedges and effective tax rates
Correlation (effective tax rate and wedges)
1980:1996
1980:1991
1991:1996
Labor
Investment
0.72
0.75
0.59
0.05
0.047
0.015
From Mendoza and Tesar (1994, updated).
Q6
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2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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also contributed to an increased investment wedge and
increases in financial frictions could have been an important
contributing factor.
4. Robustness of BCA procedure
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4.1. Sensitivity of BCA analysis to methodological and
modelling variations
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One concern regarding the use of BCA technique in
identifying distortion margins that are quantitatively important
in accounting for business cycle fluctuations is that the results
of the BCA analysis are very sensitive to small changes in
modelling environment. Christiano and Davis (2006) discuss
this point in detail. In their BCA analysis of the 1982 recession
in the US, Chari et al. (2006) argued that intertemporal wedges
manifest in investment wedges do not play any significant role
in accounting for 1982 recession but Christiano and Davis
(2006) shows that this result is overturned once they include
investment adjustment costs to an otherwise standard BCA
model. A more potentially damaging flaw emerged when
instead of modelling investment frictions as a tax on
investment, Christiano and Davis (2006) modeled investment
frictions as a tax on gross returns to capital. Once again,
investment wedges turned out to account for 26 percent of the
drop in output during the 1982 recession. The last point is also
confirmed by Kobayashi and Inaba (2006) who conduct a
similar analysis for the US great depression and arrive at a
conclusion opposite to that reached by Chari et al. (2002a).
We find the same problem in our analysis when we compare
our results with that of Kobayashi and Inaba (2006) regarding
application of BCA to account for Japanese economic boom
and depression of 1980–2000. While our analysis shows that
investment wedges are potentially important source of the
economic fluctuations, Kobayashi and Inaba (2006) stress that
investment wedges play an insignificant role. Rather, it is the
labor wedges which are important.
One common factor that emerges from comparing Chari
et al. (2006) result with Christiano and Davis (2006) and also
our result with Kobayashi and Inaba (2006) is that the role
played by investment wedges (or intertemporal wedges) is
particularly susceptible to modelling and methodological
differences in different studies. Underscoring the importance
of this point, Kobayashi and Inaba stress that’’ . . . the measured
values of the investment and capital wedges are too sensitive to
identifying assumptions: In both the original and new BCA
exercises, we assumed that the investment (or capital) wedge
remain constant from year T onward, where T is the last year of
the target period of the BCA exercise. This assumption may be
too restrictive and make the measurement of the intertemporal
Euler equations unreliable.’’ where the original BCA refers to
modelling intertemporal frictions as investment wedge and new
BCA refers to modelling them as tax on gross returns to capital.
Thus Kobayashi and Inaba alludes to possible sensitivity to
methodological differences while Christiano and Davis (2006)
stress that ‘‘ . . . BCA offers a menu of observationally
equivalent assessments about the importance of shocks to the
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intertemporal wedge. By focusing exclusively on the extreme
case of zero spillovers, CKM select the element in the menu
which minimizes the role of intertemporal shocks’’, thus
alluding to the importance of transmission channels.
In this section, we want to deconstruct our and Kobayashi
and Inaba’s methodology in an effort to identify the source of
differences between our results and the Kobayashi and Inaba
(2006) result.7
Note that our study and that of Kobayashi and Inaba (2006)
has three important methodological differences: (1) data
construction, (2) assumptions regarding the steady state in
calculating the time path of the wedges and (3) use of stochastic
version of the BCA model (this paper) as opposed to a
deterministic version (Kobayashi and Inaba (2006)). As far as
our assumptions regarding the parameters are concerned, they
are similar as we both use Hayashi and Prescott (2002) as our
source.
In terms of data construction, we use the Hayashi and
Prescott (2002) dataset that is based on the SNA 1968 while
Kobayashi and Inaba (2006) dataset is based on SNA 1993. One
other difference emerges when we try to reconcile the national
income accounts data of Japan with that of a closed economy.
We add net exports to consumption while Kobayashi and Inaba
(2006) adds net exports to investment that yields differences in
our and Kobayashi and Inaba’s measure of the time series of
capital stock (calculated by the perpetual inventory method).
In terms of steady-state calculations, we assume the steady
state to be 1980–1984 while for Kobayashi and Inaba (2006),
the steady state occurs in 2003. As we will show later on, this
difference turns out to be crucial and generates the difference in
investment wedges as measured in our model and as measured
in Kobayashi and Inaba (2006).
The final methodological difference is in terms of the model.
We use the traditional BCA approach where the model is
stochastic while Kobayashi and Inaba (2006) uses a deterministic setup where economic agents have perfect foresight.
For our deconstruction, we follow a step-by-step approach.
First, we formulate our model in a deterministic way and
compare the wedges in our model with that of Kobayashi and
Inaba (2006). One clarification is that the Kobayashi and Inaba
(2006) time series is from 1981–2006 while our time series is
1980–2000. Hence we plot the overlapping periods, concentrating on 1981–2000 for the wedge comparison.
From Eqs. (7) and (8), note that the calculation of efficiency
wedge At and the labor wedge t nt is straight-forward given the
data and stochastic vs. deterministic version would not affect
this calculation. However, we do use a deterministic model and
in Figs. 6 and 7 plot these wedges and compare them with the
counterparts in Kobayashi and Inaba (2006, referred to in the
diagram as K-I). For now, the steady-state values are calculated
as the mean of the variables during 1980–1984. Notice that
there are some differences in our measure of productivity and
that of Kobayashi and Inaba (2006). This stems from the
7
I would like to sincerely thank Dr. Kobayashi and Dr. Inaba for sharing their
data and their program with me for my deconstruction.
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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Fig. 6. Efficiency wedge comparison: our benchmark model with Kobayashi
and Inaba (2006). Note: In this figure, we plot the efficiency wedge in a
deterministic version of our model and compare it with the efficiency wedge in
Kobayashi and Inaba (2006).
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difference in our and Kobayashi and Inaba’s measure of capital
stock discussed above.8 However, the difference is more in
terms of magnitude of changes whereas the fluctuations are
perfectly lined up, with TFP growing above trend during the
1980s and below trend during the 1990s. This suggests that in
both our and Kobayashi and Inaba’s analysis, efficiency wedges
are a potentially important source of per capita output
fluctuations in Japan.
As for measurement of labor wedges, we first clarify a
difference in the definitions used. For our study, labor wedge is
simply the labor tax tnt : Kobayashi and Inaba (2006) define
labor wedge as ð1 tnt Þf1 where f is the scaling factor. For
ease of comparison, in Fig. 7, we use our definition and find that
our measure and that of Kobayashi and Inaba almost coincide
(no surprise as the time series of labor is almost identical and
the share of consumption in output does not vary much though
consumption series is not identical). For the labor wedge
measure, note the following important implications. Labor
wedge falls during 1981–1984 but then increases, beginning in
1984. Since according to our definition, an increase in labor
wedge is synonymous with a tax increase, the effect would be to
depress output. Hence while labor wedge increase can be
potentially important in explaining the depression of the 1990s,
it actually had a dampening effect on the Japanese economy
starting in the 1980s. This result is not unique to our study as
Kobayashi and Inaba (2006) point out ‘‘While our BCA results
imply that the labor wedge is a crucial factor that explains the
output declines during the 1990s, the labor wedge itself began
to deteriorate long before the recession started. Fig. 1 shows
that the labor wedge, calculated from (4), began to deteriorate
in 1984.9 There are two different interpretations for this. The
first is that the deterioration of the labor wedge represents a
structural change in the economy, which may be unrelated to
the recession in the 1990s: The labor wedge may represent a
declining trend in the Japanese economy, while a temporary
surge in productivity could have brought about a short period of
boom in the late 1980s. The other interpretation is that the
deterioration of the measured labor wedge in the 1980s is a
result of a measurement error: If there was a change in
production technology, in which the labor share (1 a)
decreases, the labor wedge declines, since we assumed a
constant a when we measured the labor wedge’’.10 In what they
call a ‘‘modified labor wedge’’, Kobayashi and Inaba (2006)
modify this traditional labor wedge by scaling it with changing
share of labor to get the labor wedge to deteriorate from 1991
instead of 1984 and thus improving the correlation with per
capita output. In our deterministic analysis, we hold the labor
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8
When we redo the analysis by adding net exports to investment as in
Kobayashi and Inaba (2006), this difference is no longer significant. However,
in this draft we follow Chari et al. (2002a) way of reconciling net exports by
adding them to consumption.
9
A clarification: the figure and equation number mentioned in this quote
from Kobayashi and Inaba (2006) refer to their paper.
10
See Kobayashi and Inaba (2006), p. 13: ‘‘When did labor wedge begin to
decline?’’
Fig. 7. Labor wedge comparison: our benchmark model with Kobayashi and
Inaba (2006). Note: In this figure, we plot the labor wedge in a deterministic
version of our model and compare it with the labor wedge in Kobayashi and
Inaba (2006); however, note that in Kobayashi and Inaba (2006), labor wedge is
defined as ð1 tnt Þf1 whereas our definition of labor wedge is t nt : hence we
modify Kobayashi–Inaba’s definition to ours and recalculate t nt from their labor
wedge values to make a consistent comparison with our realized labor wedge.
940
and capital share constant, use the traditional measure of labor
wedge and check how far we can go.
In the calculation of efficiency and labor wedge, we saw that
the steady-state assumptions did not play a major role and the
small differences where primarily due to data construction.
However, this is no longer true when we come to investment
wedges as depicted in Fig. 8. Before we explain the source of
the differences, note that the first-order intertemporal condition
is different depending on whether we assume a perfect foresight
or a stochastic model. In the former case, agent’s expectations
about the future play no role in determining the investment
wedge but in a stochastic version, expectations about the future
do play a role. The question is: is this difference enough to
overturn conclusions? We take up this question later on but for
now we continue with the deterministic version and calculate
investment wedges. In what we refer to as ‘‘benchmark 1’’, we
assume like Kobayashi and Inaba (2006) that the steady state is
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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implies that while a decline in investment taxes or wedges are
conducive to the economic boom of the 1980s, they cannot
explain the depression of the 1990s, that explains why
Kobayashi and Inaba (2006) finds investment wedges to play
an insignificant role.
One alternative way of using the shooting algorithm is to
begin with a steady-state value and then calculate the time
series of investment wedges for the target period. In this regard,
we follow the following rule: we set the wedges constant at their
steady-state value for the period before and including 1980 and
calculate the time path of investment wedges thereafter by
forward iteration.11
Note that under this assumption, the investment wedges or
txt decline in the 1980s but increase in the 1990s. Since the
investment wedges act like investment taxes, a deterioration of
the wedges are conducive to economic growth while an increase
spells economic recession. Thus depending on the steady-state
assumptions, the importance of investment wedges in
accounting for business cycle fluctuations in Japan vary greatly.
Our analysis, on its face value, looks promising to account
for the differences between ours and Kobayashi and Inaba
(2006) results. Will this be true when we apply it to our model to
calculate the model generated output and compare it to results
from Kobayashi and Inaba (2006)? Figs. 9 and 10 are designed
to answer this question.
In Fig. 9, we solve a deterministic model where the wedges
are calculated under the assumption that the steady state is 1980
and compare it with our stochastic version outcome. Note that
the results are similar. Efficiency and investment wedges almost
wholly account for per capita output in both the deterministic as
well as the stochastic version. So the assumption whether
economic agents have perfect foresight or not does not seem
very significant in altering the results of BCA analysis.12 Once
we find that under our assumption of 1980 being the steady
state, both stochastic and deterministic versions imply an
important role for investment wedge, a natural curiosity is:
would this result overturn if we use 2003 as our steady state?
Given that stochastic and deterministic versions don’t yield
very different outcomes, we concentrate on a deterministic
version to use the exact methodology (implying using
backward iteration technique to calculate investment wedge)
as in Kobayashi and Inaba (2006) and plot the results in Fig. 10.
Now, it is the efficiency and labor wedge that turns out to be
significant in accounting for the fluctuations in output. We also
plot the model outcome feeding in the labor and investment
wedge jointly and find that it does not do a good job in
accounting for the output fluctuations, particularly the booming
1980s, though the performance is much better for the 1990s
when investment and labor wedges jointly do result in a
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2003 while under ‘‘benchmark 2’’, we assume that 1980 is the
steady state as in our stochastic version discussed in previous
sections. Why does this matter? The deterministic version is
solved using a ‘‘shooting algorithm’’ that requires a strict
assumption regarding the period after or before the target period
as the case may be. In case of backward iteration, as used in
Kobayashi and Inaba (2006), the assumption is that investment
wedges revert to their steady-state values in 2003 and all other
wedges remain constant at their steady-state values thereafter
for t > 2003: When we follow the same technique, we find that
investment wedges almost continuously decline (or investment
taxes fall) during the 1980s and the 1990s, except for a brief
period during 1989–1991. For Kobayashi and Inaba (2006), the
trend is similar except that the upturn period is slightly longer,
(1987–1991) that we chalk to minor differences in data. This
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Fig. 8. Investment wedge comparison: our benchmark model with Kobayashi
and Inaba (2006). Note: In this figure, we plot the investment wedge t xt in a
deterministic version of our model and compare it with the investment wedge in
Kobayashi and Inaba (2006). Benchmark 1 plots the wedge in our model
assuming that 2003 is the steady state as in Kobayashi and Inaba (2006) and
benchmark 2 plots the investment wedge assuming 1980 is the steady state as in
our setup. There are also some differences in the shooting algorithm used in the
two benchmark cases that we discuss in detail in our paper.
11
Fig. 9. Per capita output: data and model predictions (stochastic vs. deterministic). Note: In this figure, we plot the per capita output as generated
by feeding in efficiency and investment wedges in our benchmark stochastic
model and compare to see if results change when we use the deterministic
version.
For interested readers, the details of the shooting algorithm are explained in
our technical appendix and also in Kobayashi and Inaba (2006), pp. 7–8.
12
This result is also borne out in a non-linearized parameterized expectations
algorithm approach used by Inaba (2007) that shows that their stochastic and
deterministic approach yield similar results, keeping all other factors the same
(including the fact that in their stochastic and deterministic version, they use
2003 as the steady state).
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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specifications
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deemed constant. Note that in literature, the standard practice
has been to designate the period just preceding the period of
analysis as the steady-state period. In this regard, Hayashi and
Prescott (2002) used the period 1984–1989 as their steady
state as their focus was on explaining the lost decade of 1990s,
while Kobayashi and Inaba (2006) use 2003 or the terminal
period as their steady state to facilitate use of backward
induction.
We choose the period 1980–1984. There are a couple of
reasons for our choice. On one hand, we want to not only
account for the depression of the 1990s, but also the boom of
the late 1980s. Secondly, as shown in the introduction, Japan
grew at almost 7 percent during the 1970s but after successfully
weathering the oil-price shocks, it had settled into a
comfortable aggregate growth rate of 3 percent or a per capita
growth rate of 2.15 percent. This continued till about 1984,
when there was a second growth spurt followed by the 1990s
depression. If we look at the relatively stable period of growth
in Japan’s history, 1980–1984 certainly qualifies as that period
when the growth rate was very similar to the magic number of 2
percent. Japan, after having played catch-up for two decades
was now stable. The question we are interested is: what caused
the subsequent fluctuations? To analyze this, we posit that
1980–1984 is our steady state and try to explain why Japan
moved away from this state. While reader’s might argue that
choice of steady state is vital methodologically and can alter the
predictions regarding the investment wedge, we believe there is
a need to choose the steady state based on historical
observations of growth patterns as we do here.
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Fig. 10. Per capita output: data and model predictions (Kobayashi–Inaba
approach). Note: In this figure, we plot the per capita output as generated
by feeding in jointly efficiency and labor wedges and labor and investment
wedges in a deterministic version of our model where the steady state is taken as
2003 as in Kobayashi and Inaba (2006).
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depression. In this regard, note that our result is quite similar to
Kobayashi and Inaba (2006). The small difference is due to the
fact that Kobayashi and Inaba (2006) in graph (3) combine
government wedge along with the others, while we just
concentrate on efficiency, labor and investment wedge. Note
that when we feed in labor along with investment wedge, the
downturn begins in 1984 as opposed to 1991 (similar to
Kobayashi and Inaba graph (3)) further confirming our
suspicion that labor wedge fails to explain the boom of the
1980s while being successful in accounting for the depression
of the 1990s. Kobayashi and Inaba (2006) tries to reconcile this
finding with the data by using the modified labor wedge by
adjusting for the labor share (Kobayashi and Inaba, 2006,
graphs (5) and (7)) such that labor wedge starts to deteriorate in
1991 as opposed to 1984 and thus get a better fit in accounting
for the boom of 1980s using the modified labor wedge.
Thus the results of this section tell us that the investment
wedge calculations are very sensitive to methodological
techniques and so we cannot conclude that investment wedges
are not significant and ignore them. Moreover, labor wedges,
though they definitely aid in explaining the downturn of the
1990s under any methodology, are not a good candidate to
explain the boom unless we decide to modify it. While we
concur with other studies that there are some difficulties in
applying the BCA methodology, we still believe that it is a good
tool that helps guide us to construct quantitatively promising
detailed models with primitive shocks to explain business cycle
fluctuations.
While the above section highlights the sensitivity of BCA
analysis, in particular, the investment wedges to small
variations in methodological techniques, in particular with
regards to the initial specification of the steady state, can we
make arguments in favor of choosing one period as our steady
state versus the other? Or is the choice arbitrary? In terms of
economic theory, a steady-state period is one in which all
variables except labor grow at a constant rate that is the rate of
long term technological progress of the economy and labor is
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The results from our previous section show the sensitivity of
BCA analysis to small changes in methodology. We are curious
as to how sensitive is our results to changes in parameterization? One problem with BCA analysis is we cannot apply the
usual calibration techniques, the reason being that while on the
face value, the wedges resemble taxes, they are much more than
just taxes. They incorporate all possible frictions that show up
as wedges and affect the economy, keeping it from achieving
the first best outcome. Now this in turn presents a problem as we
do not really have a numerical measure of the wedges in data, so
we cannot put in a steady-state value for the wedges and back
out the parameters.
What do we do? We follow all business cycle accounting
studies in this regard. We take the parameters from literature,
and using national income accounts data, we back out the
steady-state value of the wedges. Note that we are not saying
that wedges are zero in the steady state. Rather we are using a
‘‘dual’’ method and backing out the steady-state value of the
wedges. To give an idea of how we perform as opposed to
Kobayashi and Inaba (2006) who also follow the same
procedure, our steady-state value of the labor wedge t n ¼
0:54 and that of investment wedge tx ¼ 0:48: Note that our
results are quite similar to Kobayashi and Inaba (2006) who
uses the same technique and get t n ¼ 0:59 and tx ¼ 0:33 as the
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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Fig. 12. Wedges under alternative parameters and effective tax rates. Note: In
this figure, we plot the wedges under alternative parameter specifications as
noted in our robustness test. We also compare the time trend of the wedges with
the effective tax rates from Mendoza et al. (1994). Note that the effective tax
rate data in Mendoza et al. (1994) has been updated only through 1996 so we
truncate our sample at 1996.
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Fig. 11. Wedges under alternative parameters. Note: In this figure, we plot the
wedges under alternative parameter specifications as noted in our robustness
test. The time trend here can be compared with those in Fig. 2. The results turn
out to be pretty similar.
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mean value during the period 1980–1984 (the differences are
partly due to data construction).13
One possible alternative, as far as robustness analysis goes,
would be to assume that the steady-state value of the wedges are
the numerical value of the effective tax rates in this period, and
then use the national income account Figures for the period to
back out the parameters as we do in calibration. Next, we can
solve the model as we do in the paper and see how robust our
results are. To this end, we take the steady-state value of the
labor wedge tn ¼ 0:28 and that of investment wedge tx ¼ 0:05
from Mendoza et al. (1994). These are the average effective tax
rates on labor and consumption for the period 1980–1984.
Using the national income account Figures and the steady-state
equations, where the share of consumption in output is 58
percent, the share of government expenditure in output is 15
percent, leisure is 0.33 and the capital to output ratio is 1.86, we
calculate d ¼ 0:113; u ¼ 0:3; b ¼ 0:98 and time variation
parameter ð1 a=aÞ ¼ 1:52:
Next, we calculate the time variation in our wedges using
these new set of parameters and our stochastic specification. We
plot the results in Fig. 11 and compare them with the wedges in
our benchmark model in Fig. 2. Not surprisingly, as our
alternative set of parameters calibrated by the usual techniques
are not too different from the parameters of the benchmark
model and as we use the similar stochastic modelling, the time
trends are quite similar. Consequently, we conclude that our
results are robust to alternative parameterization.
In Fig. 12, we plot the wedges in our model under the
alternative specifications and compare them with the time trend
of effective tax rates from Mendoza et al. (1994). As expected,
the results of our benchmark analysis does not change. While
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the labor tax rate fluctuations correlate with labor wedges quite
well (in the 1980s, though the correlation turns negative in the
1990s), the same is not true for investment wedges and taxes
(measured as effective tax on consumption expenditure). While
investment wedges tend to fall in the 1980s and rise in the
1990s, the consumption taxes remain pretty stable as we found
in our correlation analysis in Section 3.2.2.
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Kobayashi and Inaba (2006) add net exports to investment while we add it to
consumption. There are various ways of doing this adjustment in literature. For
example, Christiano and Davis (2006) add net exports to government consumption.
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5. Conclusion
The Japanese economic experience during the period 1980–
2000 constitutes an important business cycle episode of the
20th century. In this paper we quantitatively account for
business cycle fluctuations using the technique of business
cycle accounting. We model the Japanese economy as a
neoclassical growth model and allow four wedges: efficiency
wedge that show up as fluctuations in productivity, labor wedge
that distorts the first-order condition equating marginal rate of
substitution between consumption and leisure to real wage rate,
investment wedge that distorts the intertemporal substitution
condition and government expenditure wedge that shows up in
the resource constraint. Further we assume that the wedges
follow a vector autoregressive process of order one.
We use national income accounts data from Japan (from
Hayashi and Prescott, 2002 dataset) and the parameters from
literature to calculate the realized value of the wedges.
Comparing the measured wedges with effective tax rates, we
find that labor income taxes were strongly correlated to labor
wedge in the 1980s but the correlation is negative in the 1990s
which indicates factors other than taxes led to increased labor
market frictions. As for investment wedges and effective tax on
consumption, the correlation is negative in the 1980s which
implies that even though taxes were increasing, other factors
like liberalization, helped relax the investment market
constraints. In the 1990s, consumption taxes continued to
increase thus aggravating the investment frictions.
Feeding in the wedges one by one and in various
combinations in our model, we find that efficiency and
Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001
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Q7 Uncited
references
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Bayoumi (1999), Ricardo et al. (2004), Carlstrom and Fuerst
(1997), Chakraborty et al. (2007), Chari et al. (2002b), Cole and
Ohanian (1999), Fukao and Ug (2004), Hoshi and Kashyap
(2004), Ireland (2004), Jermann (1998), Kiyotaki and Moore
(1997), Kobayashi and Inaba (2005), Kydland et al. (in press),
Kydland and Prescott (1982), Peek and Rosengren (1997),
Prescott (1999), Sakuragawa (2002) and Sakuragawa and
Hosono (2004).
Acknowledgements
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I would like to thank V.V. Chari for his help and advice. My
thanks to Ellen R. McGrattan and Michele Boldrin for their
suggestions. I greatly benefited from the comments made by the
editor and the anonymous referee along with the suggestions
given by David Weinstein, Takatoshi Ito, R. Anton Braun,
Fumio Hayashi and members of the Japan Economic Seminar
group. Data help from Keiichiro Kobayashi and Masaru Inaba
is gratefully acknowledged. All remaining errors are mine.
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Appendix A. Data appendix
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References
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national growth rates. We also look at the share of private
consumption, investment and government consumption to
output.
For our analysis, we take the data from Hayashi and
Prescott’s (2002) dataset used for ‘‘The 1990s in Japan: A
Lost Decade’’. To convert the open economy to a closed
economy, we add net exports to private consumption. We
also remove net indirect business taxes from private
consumption and private investment to get the GDP at
factor prices. The aggregate investment is taken as the sum of
private and public investment.
The parameters of the model are taken from Hayashi and
Prescott (2002). Using depreciation rate 8.9 percent and the
steady-state capital to output ratio of 1.74 during 1980, and
given the measure of investment, we calculate the capital
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The labor force in Japan is taken as population aged 20–69.
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at: http://www.bsos.umd.edu/econ/mendoza/pdfs/newtaxdata.pdf.
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investment wedges significantly account for fluctuations in
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that the economy boomed in the 1980s despite the worsening
frictions in labor market. During the 1990s worsening labor
wedges added to the recession.
Our robustness tests add to the recent business cycle
accounting literature by showing that the measurement of the
wedges, particularly the investment wedge, is quite sensitive to
minor methodological differences which points to one potential
flaw in the BCA methodology that has been pointed out by
earlier studies. Nevertheless, we believe in the usefulness of
using the BCA results in aiding the construction of detailed
models with primitive frictions that we know, a priori, will be
successful in quantitatively accounting for economic fluctuations.
For the Japanese case, given our results we conclude that to
construct a detailed dynamic general equilibrium model that
will be successful in accounting for the economic fluctuations,
we need to insert frictions in such a way that in the model it
shows up as fluctuations in TFP and investment wedges. It will
be interesting to see if future applications of these models
confirm this belief.
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1. In Table 1(a) and (b) we compare the variables between
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components are obtained by extrapolating the 1996 values in
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Please cite this article in press as: Chakraborty, S., The boom and the bust of the Japanese economy: A quantitative look at the period 1980–
2000, Japan World Economy (2008), doi:10.1016/j.japwor.2008.01.001