1

Valley of Institutional Change:
Japanese Political Economy 1990-2005
Sota Kato1

Abstract
Why does an extensive reform of a national political-economic system invite temporal, andoften substantial, loss of economic output? This paper extends the previous research oftransition economies on this topic and tackles the question from two new aspects. First, itutilizes recent developments in theories of institutions and institutional change. Second, it
analyzes the Japanese political economy from 1990 to 2005 and examines how and to what
extent institutional changes affected economic performance. After the collapse of the bubble
economy in the early 1990s, Japan implemented series of neoliberal reforms aimed atabandoning its once renowned “Japan model.”This paper deduces a theoretical model to show that an extensive system change, including achange from an economically inefficient system to a more efficient one, invites a temporal lossof institutional complementarities among composing institutions during the change. Such a lossof institutional complementarities results in “valley of institutional change,” an output fall
during transition.

1

Professor, International University of Japan. Senior Fellow, Tokyo Foundation and Canon
Global Institute. skato@yj9.so-net.ne.jp

2

1. Introduction
Why does an extensive reform of a national political-economic system invite temporal, andoften substantial, loss of economic output? This question was initially raised in relation totransition between capitalism and socialism. Przeworski (1985) earlier pointed out that, even if
assuming that socialism is superior to capitalism, “valley of transition,” a temporal drop of
workers’ welfare during transition from capitalism to socialism exists. The topic became ahighly debated issue when the opposite transition, from communism to capitalism, occurred
after the fall of the Berlin wall.
The question is especially puzzling because, when ex-communist states initiated theirtransition in the early 1990s, the consensus among scholars and reformers was that the capitalist
system was superior to the communist system in producing better economic outcomes.
Reformers then thus rushed to implant Western capitalism systems to post-communist states but
the results were mostly devastating (Roland 2000). So why would a transition toward an
economically more efficient system invite an economic downturn? Prezeworski provided one
possible answer and transitional economists proposed several others.
This paper extends the previous research on transition economies and tackles the question
from two new aspects. First, it utilizes recent developments in theories of institution andinstitutional change. Second, it analyzes the Japanese political economy from 1990 to 2005 andexamines how and to what extent institutional changes affected economic performance. Afterthe collapse of the bubble economy in the early 1990s, Japan implemented neoliberal reform
plans by executing extensive institutional changes aimed at abandoning its once renowned“Japan model” and to be more like the US (Nakatani 1996, 2008; Ohmori 2007; Lechevalier2012).

3

The paper starts from reviewing past related research. It then shows what this research can
add to. In the theory section, I deduce a theoretical model to show that an extensive systemchange, including a change from an economically inefficient system to a more efficient one,
invites an output fall. During an extensive institutional change of a national political-economic
system, due to different speed of change among different types of institutions, a temporal loss ofinstitutional complementarities among composing institutions inevitably occurs. Such loss ofinstitutional complementarities invites, I argue, a temporal and inevitable output fall. I call the
output loss during extensive institutional change “valley of institutional change,” expressing thebroader range of applicability compared to Prezeworski’s “valley of transition.”
Hypotheses derived from the theoretical model are tested against panel data for Japaneseindustries (n=65) from 1990 to 2005. The dependent variable of the empirical testing model isthe output of each industry. The testing variables are indicators that represent the embeddednessof each industry to political-economic institutions that constituted the “Japan model.”Finally,in the concluding remark, I briefly summarize results of theoretical and empirical analyses andtheir implications for policy making.

2. Past Research

2.1 Research on output fall during transition
Przeworski argued that, contrary to Marx’s assertion, workers’ material interests incapitalist societies do not automatically lead to transition toward socialism even if socialism wassuperior to capitalism. The main reason he raised was the existence of “valley of transition,” atemporal drop of workers’ welfare during transition from capitalism to socialism. Such “valley”
exists because, faced with imminent nationalization, capitalists would disinvest and might even4seek for using armed forces to prevent the transition.
Applicability of Przeworski’s argument, however, is limited to transition from capitalism tosocialism. His insightful argument thus did not garner enough attention when the transition tothe opposite direction, from communism to capitalism, occurred in the 1990s.
In the 1990s, Western economists led “big bang” structural reforms, aiming to implant
capitalist systems into post-communist states. The results were, however, devastating (Roland
2000). Observing the debacle, transitional economists started to investigate why transition
toward supposedly more efficient system incurs serious output loss.
Many of transitional economists based their explanations on standard economic theory.
Calvo and Coricelli (1992), for instance, presented the credit crunch hypothesis. Whenstabilization policy was implemented in Poland in 1990, they claimed, high real interest rate
along with announcement of hard budget constraint substantially reduced firms’ demand for
credit and thus led to output fall. Roland and Verdier (1999) raised the search friction hypothesis.
Relation-specific investments only take place when long-term partner exists (Williamson 1996).
After liberalization, firms had to take time to search a new partner who was trustable to create
long-term relationship. They did not invest until they find such a partner, leading to output fall
during the search period.
Transitional economists’ arguments are theoretically rigid and many of them demonstrated
empirical validness as well. Nonetheless, the range of application of transitional economists’claims is, in contrast to Przeworski’s argument, mostly limited to transition from communism tocapitalism. Moreover, most of them focus only on economic aspect of transition and ignore
political economic and structural aspects.

2.2 Varieties of capitalism (VOC) perspective

5

After the collapse of communism in 1989, social scientists’ who were interested incomparing capitalism and socialism/communism turned their interest toward comparing
different types of capitalism.2 Hall and Soskice (2001) presented “varieties of capitalism (VOC)”
perspective which has recently become one of the most referred research in the field ofcomparative political economy.
VOC classifies two types of capitalism by how firms coordinate with other actors.
Capitalist economies in which firms mostly rely on market mode of coordination are classified
as Liberalized Market Economies (LMEs) whereas those in which firms rely on strategic modeof coordination are classified as Coordinated Market Economies (CMEs). Typical examples ofthe former, in VOC’s view, are the US and UK, and the examples of the latter are Germany,
France and Japan.
Patterns of coordination, whether strategic or market driven, depends on institutionalsetting of each economy. VOC emphasizes the concept of institutional complementarities,3
which means the presence of one set of institutions raises the return of the other.
Complementarities among institutions across different spheres of political economy enhance
aggregate economic performance. Such complementarities, VOC argues, realize distinct
patterns of combinations of institutions that result in “varieties” of political economic system4.
While VOC perspective has become an intellectual base for analyses of post cold war
comparative political economy, it has gathered various criticisms as well. One of the major
2

Several political scientists had proceeded in this topic. Johnson (1982), for example,
conceptualized “developmental state model” and pointed out that the developmental state employs
economic strategies quite distinct from Western economies. Main-stream economists, on the other
hand, were reluctant to admit significant differences among capitalist states and their economic
strategies until recently. Aoki (1990; 2001) was one of the few exceptions and explained rationale ofJapanese and East Asian corporate system by using game theoretic framework.
3
Definition of institutional complementarities for this paper will be addressed in the later section.
4
VOC advocates emphasize existence of complementary relation between institutions governing
labor relations and corporate governance (Hall and Soskice 2001: Hall and Gingerich 2009). They
empirically and theoretically showed that the pattern of institutional setting of labor relations
correlates with the pattern of institutional setting of corporate governance.

6

criticisms of VOC is that it over-emphasizes stableness and path-dependence of economies andfails to explain dynamic elements of economic change (Hancke et al. 2007; Streeck and Thelen2005). In fact, since the 1980s, neoliberal movement spread across the world and some of the
CMEs including Japan implemented neoliberal reform plans to change themselves to LMEs.
VOC perspective is incompetent in explaining such a phenomenon and thus needs toincorporate dynamic aspects.

2.3. Japan`s “lost decade” debate
The prolonged economic stagnation in Japan in the 1990s, often labeled Japan’s “Lost
Decade,” provoked intensified debates among social scientists worldwide (e.g., Harada 1998;
Ono 1998; Posen 1998; Krugman 1999; Yoshikawa 1999; Iwata 2001; Kato & Kobayashi 2001;
Ogawa 2009). Their approaches can roughly be divided into two patterns, namely, the cyclical(demand-side) approach and the structural (supply-side) approach. The former regards the
economic stagnation as a purely macroeconomic phenomenon worsened by policy failures. The
latter views that the “Japan model” had become outdated by the 1990 and blames key
components of the Japan model such as the main bank system, keiretsu, cross-shareholding andactive bureaucracy for blocking economic recovery.
Several studies, including my earlier work with Keiichiro Kobayashi (2001), theoreticallyand empirically showed that structure mattered for the prolonged stagnation5. Careful observersof Japanese political economy, on the other hand, mostly agree that Japan underwent extensive
institutional change after 1990s (Pempel 2000; Aoki 2001; Hoshi & Kashyap 2001; Toya
2003; Vogel 2006; Jackson & Miyajima 2007; Kato 2009)6. Using the notions of VOC, Japan5

We do not deny cyclical argument. We think that both cyclical and structural factors had
significant effects on the prolonged economic downturn (Kobayashi & Kato 2001; for a similar view,see Yoshikawa 1999).
6
There are the opposite views as well. Lincoln (2001), for instance, insists that Japan could not7attempted to transform her political economic system from CMEs to LMEs (Kato 2011).
Then why did not Japanese economy recover more promptly? If the political economic
structure, notably the “Japan model,” was one of the major causes of the Japanese economic
stagnation in the 1990s, and if the Japanese government implemented drastic structural reforms
in the 1990s, why was Japan unable to recover from the stagnation more smoothly? These are
the questions this paper attempts to solve by extending past research of transitional economies
and comparative institutional analysis including VOC perspective.

2.4. Aims of this paper
Why does an extensive reform of a national political-economic system invite temporal, andoften substantial, loss of economic output? Prezeworski and transitional economists provided
possible answers for transitions between capitalism and socialism/communism. Social
scientists` interest after the cold war era has moved from comparing “capitalism vs. socialism”
to “capitalism vs. capitalism” (Aoki & Kato 2007). Few researchers, however, have analyzed
possible output fall during transitions between different types of capitalism.
VOC perspective, on the other hand, successfully showed why divergent patterns ofcapitalism coexist but it lacks dynamic aspects. Why do not CMEs make transition to LMEs orvice versa? What happens if such transition occurs, as was the case of Japan? What are thecosts of transition?
This paper aims to explain why extensive institutional change, including change from one
type of capitalism to another, invites temporal output fall by analyzing Japanese experience in
1990-2005. During the era, Japan implemented neoliberal reforms which provide social
scientists a favorable large-N sample to examine the effects of extensive institutional change.
promptly recover from the recession because structural reforms were too little, too slow. Katz (1998,2010) takes a similar, although more cautious view.

8

I first construct a theoretical model and show that even transition toward more efficientinstitutional setting invites temporal output loss under a certain condition. In the next section,
hypotheses derived from the theoretical model are tested against data gathered from Japanese
economy during 1990-2005. I use panel data, consisting of 65 industries in the time span of
1990-2005.

3. Model and Hypothesis

3.1 Assumptions
Two key assumptions for deducing a theoretical model are as follows.

Assumption 1: Institutional complementarities exist.
Assumption 2: The time necessary for institutions to change varies from one institution toanother.

As for Assumption 1, Hall and Gingerich (2009) define institutional complementarities as,“One set of institutions is said to be complementary to another when its presence raises thereturns available from the other.” This paper will follow their definition. 7 The idea of
institutional complementarities has been widely accepted by economists (e.g., Aoki 2001: Hoffand Stiglitz 2001; Teranishi 2003; Roland 2004), political scientists (Hall & Soskice 2001) andsociologists (Kenworthy 2006).
Assumption 2 is probably less well acknowledged but no less important. Williamson(1996) distinguished four levels of institutions by how quickly they change. Roland (2004)

7

As for a formal definition of institutional complementarities, see Kato (2011).

9classified institutions into “slow-moving” and “fast-moving” institutions. Typical examples ofthe former, according to Roland, are informal institutions, such as beliefs and values; politicalinstitutions are examples of the latter. Kato (2012) empirically showed “government centeredinstitution,” institution such as legal system that government deliberately establishes, changes
faster than “private centered institution” such as business customs.

3.2. Model
If Assumptions 1 and 2 hold, will a loss of institutional complementarities among
institutions during system change results in output fall? To examine this question, in thefollowing, I deduce a theoretical model from the two assumptions.
Let assume that the performance of system
institutions
and let

and

.

Let

,

and
.

is dependent on the effectiveness of two

denote the effectiveness of institutions

andSuppose that the initial system S0 was at the point of. An extensive system reforms were undertaken to change toward asuperior system S1 where. The performance of

can be shown in the following

simple model;

The second term shows strong complementarities between
deviate from complementary relations, the performance of system
substantially. Let assume thatandchange as following

and

. If

, which isanddecreases

10

where

denote the speed of institutional change ofandIn this model, one can reasonably assume that the performance of, respectively.
ffects industry’s

productivity. This situation can be expressed as follows. There is a representative firm whose
production technology is:

where

,

respectively.

,

denote output, productivity, labor input, and capital input,

I assume

consists of institutional factorHenceis expressed as

where

and

and non-institutional factor

are parameters.

To assess how loss of institutional complementarities affects economic output, I will run asimple simulation by inserting actual values for each parameter. For the sake of simplicity, let、、

Also let、、In this case, supposethat extensive system reforms were undertaken to change from T0 where S0 = R0 = 0 toward asuperior system T1 where S1 = R1 = 1. In this case, since Rt changes 10 times faster than St, Rt
reaches Rt = 1when t = 1/10, and St reaches St =1 when t = 1. The representative firm’s output

11

duringwill be as follows:

For the sake of simplicity, letThus when. Then, when,

, the performance of system Tt temporarily drops under the initialsystem T0 even though the system change was heading to a more superior system T1. I call this“valley of institutional change.”8 One can substitute communism to T0 and capitalism to T1 toearn insights. Figure 1 and Figure 2 illustrate changes of Rt and St and how they relate to Yt.
One can confirm from these figures and equations that even when the system changes from aninferior one to a superior one, under certain condition, there can be inevitable output fall, what I
call “valley of institutional change,” during the transition.

Figure 1.
Figure 2.

Since Przeworski’s “valley of transition” was only applicable to transition from capitalism tosocialism, I used the term “valley of institutional change” to express that the range ofapplication is much wider for this notion.
8

12

Figure 1: Simulated Change of St and Rt

St,Rt

Rt

St

t

1

1/10
Figure 2: Simulated Change of Output (Yt)

Yt

Valley of Institutional Change

1/10

1

t

13

3.3. Illustrative Example
Figure 3 shows an illustrative example.

Suppose that in Country U, Institution IU andInstitution iU are complementary. Also suppose that in Country J, Institution IJ and Institution iJ
are complementary. The combination of IU and iU generates H (high) economic performance inCountry U, the combination of IJ and iJ generates M (mediocre) economic performance inCountry J, and the combination of institutions without complementarities ({IU, iJ},{IJ, iU})
generates L (low) economic performance.

Figure 3.
Policymakers and the public in Country J, after seeing that the economic performance ofCountry U outperforms their country’s performance, may seek a systemic transformation to theinstitutions of Country U. As for the transformation strategy, they would probably want totransform Institution I and Institution i simultaneously because if Institution I and Institution iare transformed separately, they would have a low-performance combination of {IU, iJ} that
results in performance lower than the current performance of Country J. Thus, policymakers ofCountry J would want to jump to the system of Country U through the Big Bang approach,changing institutions I and i simultaneously.
Suppose, however, that the speed of transformation for Institution I is faster than the speedof transformation i. In that event, even when policymakers intend to make a linear jump fromthe system of Country J to the system of Country U, the actual transformation path would curveas shown in Figure 3, and temporarily go through the domains of IU and iＪ, resulting in the
decline of the economic performance of Country J. This is the intuitive description of the“valley of institutional change” I formally showed earlier.

14

Figure 3: Institutional Transition (Curve)
IU

IJ

iU

H

L

iJ

L

M

Hypothetical big-bang
approach path

Actual path when the speed of
institions' transition differs

15

3.4. Hypothesis
The general hypothesis of the main point of this paper can be derived from thetheoretical model as follows:

Hypothesis 1: An extensive system change, under which industries operates, inevitably
invites temporary output fall of industries because, due to difference of speed ofchange among different types of institutions, institutional complementarities will
be lost/loosened temporally during the change.

4. Empirical Test

4.1 Unit of Analysis
The primary unit of analysis of this paper is industry. Business economists have madestrong arguments that even in the US, where the market is well-developed, industry does matter
significantly (e.g., Porter & McGahan 1997)9. There is further rationale for choosing industry as
the primary unit of analysis for this research when considering the history and structure of theJapanese political economy. By inheriting the legacy of the wartime economy, industry played aspecial role in the post-WWII Japanese political economy. Teranishi (2003) pointed out that inJapan, industry worked as a political platform for interest coordination—a role played by social
classes in Western countries. Others similarly indicated that the Japanese political economy wasvertically partitioned by industry, and so it functioned as a basic unit of political economic
coordination. Whereas Sato and Matsuzaki (1986) and Aoki (1995/2000) called the Japanese

9

Porter (1980) points out features of industries that define competitive strucuture of each industry
and firms within the industry.

16

version of the iron triangle “shikirareta tagenshugi” (bureauplurarism), Murakami (1994)
named it “compartmentalized competition.” The Liberal Democratic Party (LDP), the party thatheld onto power for most of the post WWII era, and each ministry’s departments were divided
vertically by industrial sectors. As a consequence, political economic institutions varied acrossindustries resulting in the “dual economy” (Katz 1998), high variance of profitability acrossJapanese industries.

4.2 Dataset
I compiled a political economic dataset covering 65 industries during 1990–2005. 10
Economic data was mainly gathered from the Japan Industry Productivity Database 2006 (JIP
2006)11. Corporate financial data were gathered from the NEEDS Financial QUEST databaseand converted to industries using Mitsubishi Research Institute (MRI) categorization. (For the
methods of conversion, see Appendix A and B.) Political data was gathered from governmental
sources and converted to JIP categorization through MRI to the JIP conversion matrix (see also
Appendix B). To make different types of institutional variables comparable, I normalized eachvariable before using it in statistical analyses.

4.3. Coordination Indices
The “Japanmodel”

wascharacterized

by intimate, long-term, andinformalgovernment-industry and finance-industry relationships (Aoki 1995/2000; Hoshi & Kashyap
2001; Teranishi 2003). Both relationships were mutually complementary (Aoki 2001; Teranishi
10

JIP 2006 consisted of 108 industries. By eliminating variables that are irrelevant for testing
Hypotheses and by eliminating variables with substantial missing values, the number of industries
shrunk to XX industries.
11
The JIP database was compiled in a collaborative effort between the Research Institute of
Economy, Trade and Industry (RIETI), a subsidiary institute of Ministry of Economy, Trade, andIndustry (METI), and Hitotsubashi University. The JIP database and its detailed description areavailable from the RIETI website (http://www.rieti.go.jp/jp/database/JIP2006/index.html).

17

2003; Kato 2011). The main testing variables for industry-level analysis of this paper are thus
those that represent institutions for government-industry coordination (Git) and finance-industry
coordination (Fit).12 I constructed indices that represent each of them for empirical testing. The
summary of each index is shown in Table 1.

Table 1.

12

VOC advocates often indicate institutions that coordinate corporate governance and labor
relations as mutually complementary institutions (e.g., Hall & Gingerich 2009). In the case of Japan,
however, as Pempel and Tsunekawa (1979) pointed out, labor was mostly absent from political
economic coordination. Instead, political economic coordination was led by economic ministries, the
LDP, and trade associations, all of them being included in analyses of this paper.

18

Table 1. Coordination Indices
Index

Variables
Keiretsu ratio ( the ratio of firms affiliated in major keiretsu)
Private debt to equity ratio
Cross shareholding ratio ( the ratio of mutually held shares by two
firms)
Non-capital market finance ratio
Number of ex-bureaucrats ("amakudari" bureaucrats)
Amount of political donation
Budget size of trade association
Ratio of firms under government regulation

19

As for the government-industry coordination index Git for industry i in year t, four
variables that serve as proxies for the degree of government-industry coordination are combined.
The higher the value of each variable, the higher the level of government-industry coordination.
Since each variable are normalized, I simply took an average value of four variables for Git.
Variable ExB is the number of retired bureaucrats taking executive positions in eachindustry. Such a custom is called “amakudari” and it has long been a symbol of collaborative
government-firm relations 13 . As Ramseyer and Rosenbluth (1994) correctly pointed out,
retaining executive positions in private firms after retirement (i.e., amakudari) had been a toppriority of Japanese bureaucrats, thus making this variable a strong proxy of closeness ofgovernment-industry relationships. As trade associations functioned as a point of contact
between industry and the bureaucracy in the Japan model (Yonekura 1993; Teranishi 2003;
Sasada 2011), the budget size of trade associations (TAb) should represent the collaborative
government-industry relations as well. Since regulation is a primary political tool for thegovernment to affect industrial behavior, the number of firms under control of governmental
regulations (Reg) should also represent strength of government-industry coordination. Amountof political donation (Pdn) is another obvious proxy.
Index Fit includes four variables that are proxies for intimate, long-term, and informal
finance-industry relationship. Based on long-term relationship, aggressive borrowing frombanks was thought to be a feature of the “Japan Model” (Hoshi & Kashyap 2001; Ikeo 2006:
Ogawa 2009). I thus included private debt to equity ratio (Pde). Similarly, under the “Japan
Model,” capital market was highly regulated by Ministry of Finance and did not develop(Ogawa 2009). Firms relied on non-capital market finance (Ncm), mostly bank loans.
Cross-shareholding among firms (Csh ) was another feature of the “Japan model.” It functioned

13“Amakudari” means fall from heaven in Japanese.

20

to lessen pressures from equity market and strengthen relational banking.
Figure 4 shows how Git and Fit changed during 1990-200514. Both lessened their values
substantially during 1990-2005 meaning that the “Japan Model” was considerably dismantled.
Between the two, Git changed faster when extensive institutional change started in the early
1990s. Fit eventually took over Git when Git completed institutional change. This is in line withmy earlier claim that “government centered institution” changes faster than “private centeredinstitution” (Kato 2012).
One can intuitively grasp from the Figure 4 that the gap of speed of institutional change
between Git and Fit loosened institutional complementarities during 1990-2005. In the next
section, I will assess how such loosening of institutional complementarities during the period
affected economic outputs of industries.

Figure 4.

14

Git and Fit that appear in Figure 4 are aggregated across industries.

21

-.3

-.2

-.1

0

.1

.2

Figure 4: Transition of Coordination Indices

1990

1995

2000
year
G

F

2005

22

4.4. Regression Analysis
The equation I estimated to test the hypothesis 1 is as follows. Since all of the model anddata I used passed the Hausman test, I used random effects model for estimation.

The reason why I subtracted 1990 data of most variables is as follows. Year 1990 isgenerally regarded as the last year of Japan’s bubble economy (Noguchi 2008). Till then, Japan
outpaced virtually all other OECD countries in growth rate for several decades. Japan’s
extensive institutional reform thus started after 1990, absorbing dissatisfactions of Japanese whothen were not used to economic downturn. Since the major aim of this paper is to assess how
extensive institutional change affect economic output, by subtracting 1990 data, I examined how
institutional change since pre-neoliberal reform era affected economic output15.
Yit is nominal added value for industry i in year t andis an error term. Capital input(Kit) and labor input (Lit) are included as control variables. Both are expected to be positively
correlated to the output. I also added ratio of domestic sales (DSit) to control for each industry’s
strength of link to the Japanese market. Stronger link to the Japanese market is expected to work
negatively on output. Another control variable is ratio of large enterprises (LEit). The higher
ratio of large enterprises is expected to positively affect the output.
The testing variable is. This testing variable IC

I also did estimates without subtracting 1990 data. The results, however, were very
similar to those presented in Table 2.
15

23

it shows that how institutional complementarities between finance-government relationship (Fit)and government-industry relationship (Git) had been weakened since 1990. If the hypothesis
holds, ICit should increase as an extensive institutional change proceeds and ICit should
negatively affects output (i.e.,

1< 0).

Table 2 shows the result of the estimations. The parameter estimates are mostly stable
across all the models. In all the models, the coefficient of the testing variable ICit is substantiallyand significantly negative. That is, loss of institutional complementarities between Git and Fit
leads to substantial and significant output loss. Signs of coefficients of other variables that tookstatistically significant values were as expected.

Table 2.

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Table 2: Loss of Institutional Complementarities and Output Fall

VARIABLES
Loss of Institutional complementarities (ICit)
Labor input
Capital and service input

(1)
RE

(2)
RE

(3)
RE

(4)
RE

-0.0826***(0.0127)
0.0378***(0.00372)
0.00861**(0.00351)-0.0826***(0.0128)0.0377***(0.00376)
0.00872**(0.00359)-0.00137-0.0839***(0.0135)
0.0381***(0.00388)
0.00814**(0.00377)-0.0841***(0.0135)
0.0379***(0.00391)
0.00846**(0.00384)-0.00529

Ratio of Large enterprise(0.0104)(0.0115)-0.00211(0.00603)
0.0398**(0.0171)0.0403**(0.0158)

0.0400**(0.0160)-0.00197(0.00601)
0.0404**(0.0170)

Observations

973

973

913

913

Number of industry

65
0.206

65
0.206

61
0.204

61
0.205

Ratio of domestic sales
Constant

2

R

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

26

For the purpose of simulation, I used equation (4) of Table 2. For variables other than thetesting variable, I inserted mean value of eachvariable and calculated predicted values of
3 depicts

which I denote.16 Figure

aggregated across industries. As is clear from the Figure 3, loss ofinstitutional complementarities during extensive institutional change invites serious output fall
which I call “valley of institutional change.”
Figure 5.

16More formerly,

27

-.005
-.01
-.015
-.02

predicted value

0

.005

Figure 5: Predicted Output Fall caused by Loss of Institutional Complementarities
during Extensive Institutional Change (Simulation)

1990

1995

2000
year

2005

28

5. Conclusion
Why does an extensive reform of a national political-economic system invite temporal, andoften substantial, loss of economic output? Earlier, Prezeworski (1985) pointed out existence of“valley of transition” during transition from capitalism to socialism. Transitional economists
explained the output fall during transition to the opposite direction, from communism tocapitalism. Their range of application was, however, limited to transitions between capitalism andsocialism/communism.
By utilizing recent developments in theories of institution and institutional change, thispaper theoretically and empirically showed that extensive institutional change, including changes
between different types of capitalism, invite inevitable output loss. I call such output loss during
the change “valley of institutional change.” It has broader range of application than Prezeworski’s“valley of transition.”“Valley of institutional change” has strong implication for the VOC perspective. VOC has
been criticized for being excessively static. Why not different patterns of capitalisms change to
one way or another? Indeed, the Japanese case since the 1990s, which this paper empiricallyexamined, was an attempt to change Japan from CMEs to LMEs. The existence of “valley of
institutional change,” however, can partially explain why different types of capitalism remain
divergent and do not converge. In the other words, the dynamic analysis of this paper ironically
legitimizes, to a certain extent, the static nature of VOC.
In Japan’s “lost decade” debate, supply-siders often criticized Japanese policymakers for
acting “too little, too late.” However, results of this paper’s analyses suggest that, what was
crucial for Japanese policymakers was not just to accelerate pace of change but to maintain evenpace of change among different spheres of political economy.
Analyses of this paper pose tough challenge for leaders of capitalist states. Existence of“valley of institutional change” makes firms and voters reluctant to accept major changes.
Leaders determined to persuade firms and voters to endure a severe output fall in the short run
can only initiate and sustain extensive change.

29

Appendix A■JIP-MRI-DBJ table

JIP Number

JIP Industry Name

1

Rice,wheat production

2

Miscellaneous crop farming

3

4

DBJ Number－

DBJ (Industry Group)－

MRI Number--

MRI Industry Name
--

－

－

--

--

1

Meat & daily products

--

--

－

－

--

--

－

－

--

--

Livestock and sericulture farming

Agricultural services

5

Forestry

6

Fisheries
59

Fishery

7

Mining60

Mining8

Livestock products

1

Meat & daily products

9

Seafood products

59

Fishery

-**
19
20
**
40
42
54
--

2

Grain Milling & Feeds

37

Grain mill products

10

Flour and grain mill products
4
5
6

Seasoning
Confectionery & Bakery
Other foods

2
3

Grain Milling & Feeds
Breweries

**
38
39
41
43
54
-36

Manufacture of sugar
Cooking oil
Confectionery
Miscellaneous foods and related products
Fat-Breweries

6

Other foods

--

--

8
9

Spinning
Other textiles

**
44
45
46
48

Cotton and stable fiber
Wolo and spinning
Linen, ramie and jute spinning
Other textile products

11

Miscellaneous foods and related products

12

Prepared animal foods and organic fertilizers

13

Beverages

14

Tobacco15

Textile products

16

Lumber and wood products

17

Furniture and fixtures

18
Pulp, paper, and coated and glazed paper
19
Paper products
20 Printing, plate making for printing and bookbinding
21
Leather,leather products and fur
22

Rubber products

23

Chemical fertilizers

24

Basic inorganic chemicals

25

Basic organic chemicals--

Industry Number

47
48
49
47
48
49
47
48
49
51
52
53
47
48
49
50
48
54
55
48
56
57
1

Industry
Farming, Forestry, Fisheries
Agriculture
Various Agriculture
Farming, Forestry, Fisheries
Agriculture
Various Agriculture
Farming, Forestry, Fisheries
Agriculture
Various agriculture
Sericulture
Livestock
Various livestock industries
Farming, Forestry, Fisheries
Agriculture
Various agriculture
FarmlandAgriculture
Forestry
LumberAgriculture
Fisheries and marine products
Various fisheries
Mining

37
41

Foods
Other supplementary foods

37
41
37
38
39
37
39
41
42
43

Foods
Other supplementary foods
Foods
Rice
Other staple diet
Foods
Other staple diet
Other supplementary foods
Seasoning
Confectionery

37
37
44
45
46
37
46
31
32
33
34

Foods
Foods
Beverage
Alcohol
Tea・Tabacco
Foods
Tea・Tobacco
TextjlesFilature
Spinning
Sewing

16
17
55
16
17
18
35
35
36

General equipment
Furniture, Wood products
Lumber
General equipment
Furniture, Wood products
Commodity
Paper, Pulp
Paper, Pulp
Printing

19
24

General chemicals
Rubber

19
26
19
20
19
21
22

General chemicals
Chemical fertilizer
General chemicals
Inorganic chemicals
General chemicals
Organic chemicals
Organic synthetic chemistry

Coal mining
Miscellaneous mining
Dairy products
Processed meat
Fat
--

58

Misc.Manufacturing

49

Lumber and wood products

58

Misc.Manufacturing

10

Paper&pulp

11
－
20
21

Printing－
Tires
Other Rubber Products

49
50
50
51
62
**
60
61

Lumber and wood products
Pulp and paper
Pulp and paper
Printing
Leather products
Tires and tube
Miscellaneous rubber products

12

Chemicals-Major

58

Other chemical industries

14
13

Inorganic chemicals
Organic chemicals

52
**
53
57

Inorganic chemical industries
Organic chemical industries
Petrochemical

30

12
26

Organic chemicals

27

Chemical fibers

28

Miscellaneous chemical products

29
30
31

Pharmaceutical products
Petroleum products
Coal products

12

Chemicals-Major
Chemicals-Major

15
16
12
17
19
23
24

Oil, Fats & Cosmetics
PaintsChemicals-Major
Pharmaceuticals
Petroleum
Sheet glass
Other Glass & Glassware

22
25
26

**
53
58
**
47
58
56

Organic chemical industries
Other chemical industries
Chemical fibers
Other chemical industries
Paints and inks

55
59
-63

Pharmaceutical products
Oil refinement-GlassCement64

CementCeramic Wares
Other Stone & Clay Pds.

65
**
66
67
68
**
69
70
70
71
**
72
73
1
1
**
3
7
**
3
7
**
4
6
7
**
5
8

Pottery

Office and household equipment
Communication and household electric appliances

Industrial Electric Eq.
Household Electric Appl.

8

Communication and household electric appliances

Electronic data 48
processing machines, digital and analog computer equipment and accessories
44
Computers & Electric Eq.
46
Communication Equipment

88Communication and household electric appliances
Communication and household electric appliances

**
8
17

Communication and household electric appliances
Miscellaneous precision machinery

32

Glass and its products

33

Cement and its products

34

Pottery

35

Miscellaneous ceramic, stone and clay products

27
28
29
37
Miscellaneous iron and steel
30
38
Smelting and refining of non-ferrous metals
31
32
39
Non-ferrous metal products
33
34
40Fabricated constructional and architectural metal products 35
41
Miscellaneous fabricated metal products
35
36
42
General industry machinery
39

Iron & Steel-Major
Ordinary SteelSpecial SteelOther Steel Products
Nonderrous Metal Refining
NonFerrous Metal Rolling
Wire & Cables
Die Castings
Fabricated Metal Products
Fabricated Metal Products
Machine Tools
Chemical Plants & Tanks

37
38

Agricultural Machinery
Construction Machinery

41
42
43
47
40

Other Machinery
Bearings
Other Machinery Parts
Electric Measuring Instr.
Office Machines

36

Pig iron and crude steel

43

Special industry machinery

44

Miscellaneous machinery

45

Office and service industry machines

46

Heavy construction equipment

47

Household electric appliances

49

45
49

Fireproof products
Carbon and black lead products
Miscellaneous stone and clay products
Steel industries with blast furnace
Miscellaneous iron and steel
Miscellaneous iron and steel
Primary refining of non-ferrous metals
Rolling and drawing
Wires and cables
Metal products
Metal products
Machine tools
Industry electric machinery
Machine tools
Industry electric machinery
Miscellaneous industry machinery
Miscellaneous machinery parts
Industry electric machinery

Communication equipment
45

50Electronic equipment and electric measuring instruments

Industrial Electric Eq.

19
21
22
19

General chemicals
Organic chemicals
Organic synthetic chemistry
General chemicals

19
23

General chemicals
Oil, paints

25
5
5
27
28
27
29
27

Pharmaceuticals
Power, FuelPower, Fuel
Ceramics
Glass, Fireproof products
Ceramics
Stone, Cement
Ceramics

27
30

Ceramics
Asbestus

6
8
7
8
9
9

Ordinary steel
Iron and steel products
Special steel, cast iron
Iron and steel products
Metal
Metal

9
9
10
11
12
10
11
12
10
12
14

Metal
Metal
General machinery
Tools, parts
Industry machinery
General machinery
Tools, parts
Industry machinery
General machinery
Industry machinery
Electronic equipment

10
14

General machinery
Electronic equipment

10
14
10
14
10
14
75
76
77
10
14
15

General machinery
Electronic equipment
General machinery
Electronic equipment
General machinery
Electronic equipment
Communications
Telegraph, telephone
Radio wave
General machinery
Electronic equipment
Precision machinery

31

44
51

Semiconductor devices and integrated circuits

52

Electronic parts

53

Miscellaneous electrical machinery equipment

54

Motor vehicles

55

Motor vehicle parts and accessories

56

Other transportation equipment

57

Precision machinery & equipment

58

Plasctic Products

50

Other Electric Equipment

**
8
17
**
8
17
9

51

Automobiles

10Motor vehicles

52
53
54

Auto Parts & Accessories
Shipbuilding-Major
Other Transportation Eq.

Motor vehicle body and parts

55
56

Optical Instruments
Other Precision Instr.

11
**
12
13
14
**
15
16
17

Watches
CamerasMiscellaneous precision instruments

57
58

Plastic Products
Misc.Manufacturing

18

Miscellaneous manufacturing industries

61
63
62

General Contractors-Major
Dredging
General Contractors

21

Construction

21

Construction

64
79Special Trade Contractors
Electricity Supply

32

Electricity

80
－
－
－
65
66
67
68
69
70
－
－

Gas Supply－－－
Wholesale Trade-Major
Other Wholesale Trade
Department Stores
Chaine Stores
Restaurant Operators
Other Retail Trade
－
－

33
---22

Gas
---Wholesale

**
23
24

Department stores
Other retails

71

Real Estate

--25
--

--Real estate
--

72
73

Railroads
Trucking

26

Railway tracks

78
74

Other Transportation
Shipping

27
28Road transportation
Water transportation

77

Harbor Transportation

48

59

Miscellaneous manufacturing industries

60

Construction

61

Civil engineering

62

Electricity

63
64
65
66

Gas, heat supply
Waterworks
Water supply for industrial use
Waste disposal

67

Wholesale

68

Retail

69
70
71
72

FinanceInsurance
Real estate
Housing73

Railway

74

75

Computers & Electric Eq.
Electronic Eq. & Comp.

Communication and household electric appliances
Miscellaneous precision machinery
Communication and household electric appliances
Miscellaneous precision instruments
Miscellaneous precision instruments

Shipbuilding and repair
Railroad cars
Other transportation equipment

Road transportation

Water transportation

10
14
15
10
14
15
10
14
10
13
10
11
10
13

General machinery
Electronic equipment
Precision machinery
General machinery
Electronic equipment
Precision machinery
General machinery
Electronic equipment
General machineryTransportation equipment
General machinery
Tools, parts
General machineryTransportation equipment

1015

General machinery
Precision machinery

10
16
19
27
31
62
63
58
59
60
61
2
4
3

General machinery
General tools
General chemicals
Ceramics
TextjlesConstruction
Construction equipment・Building materials
Construction
Civil engineering
National land developpment
Pland construction
Electricity
Nuclear power
Gas

65
64
66
67
68
66
67
69
70
71
66
67
72
73

Real estate
Housing
Transportations・Traffic
Transportations・Warehousing
Railroads
Transportations・Traffic
Transportations・Warehousing
Automobiles
Cars
Freight tranport
Transportations・Traffic
Transportations・Warehousing
Shipping
Vassels

32

75
76

Air transportation

77

Other transportation and packing

7678
Telegraph and telephone
81
79
Mail
－
80
Education (private and non-profit)
87
81
Research (private)－
82
Medical (private)－
83
Hygiene (private and non-profit)－
84
Other public services－
85
Advertising－86
Rental of office equipment and goods－
87
Automobile maintenance services－
88
Other services for businesses－
89
Entertainment
86
90
Broadcast
83
91 Information services and internet-based services 82
92
NewspapersH－
Video picture,
93 sound information, character information production and 84distribution
94
Eating and drinking places－95

Accommodation

96
97

Laundry, beauty and bath services
Other services for individuals

85
87
87

Airlines

29

Air transportation

Warehousing
Communication－
Other Services
－
－
－
－
－
－
－
－
Amusument Services
BroadcastComputer Services－
Other Media－

30-----------35
31
-----

Warehousing-----------Movie and entertainment
Broadcast-----HotelsOther services
Other services

34---

Accomodation and sightseeing
---

66
67
74
66
67

Transportations・Traffic
Transportations・Warehousing
Airlines
Transportations・Traffic
Transportations・Warehousing

78
75
75

Sightseeing
Communications
Communications

78
79

Sightseeing
Hotels33

Appendix B: Data Source and Conversion Rules

1. JIP 2006
In grouping companies to industry, “JIP 2006” was used. The Japan Industrial Productivity
Database 2009 (JIP 2006) is compiled in a collaborative effort between RIETI and Hitotsubashi
University's G-COE Hi-Stat Program. The JIP 2006 database contains annual data on 108
sectors covering the entire Japanese economy from 1970-2006 that can be used for total factor
productivity (TFP) analysis.

2. Conversion from MRI to JIP2006
In case JIP2006 rule was not available, we followed the categorization of “The Analysis ofBusiness Management” released by Mitsubishi Research Institute (MRI) to sort each company
to industry. This handbook is a survey reference which collects and examines the listed leading
companies on each industry. The industries are grouped into 74. In order to unify all theindustrial categorization with that of JIP 2006, in this book, the classification by MRI wasconverted to that of JIP 2006 by using the following formula:

To find out the values of each item of a company by industry based on JIP, we used thevalues on a basis of industry appearing in the MRI reference as follows:（JIPi：the value of i industry based on JIP classification. MRIik：the value of ik
industry based on MRI classification. nik: the number of companies of ik industry.
k={1, 2,…,K}. ∀k, MRIik ∈ JIPi）

34

3. NEEDS Financial QUESTIn collecting the financial data of the companies, we used NEEDS Financial QUEST.
NEEDS Financial QUEST is a database including Corporate Financial and Stock Market andMacroeconomic data. We made use of these data to present Loan (Loan from bank), MBloan(Loan from main bank), Govloan (Loan from governmental institutions).

4.

The number of industries (sample size)
The number of industries we took as sample was 65. We did not use industries such as

government-linked industries and financial industries that were not theoretically adequate totake into account. In addition, industries with substantial missing data were removed.

35

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