ISSN 1880-6988

THE JOURNAL OF ECONOMETRIC STUDY
OF NORTHEAST ASIA (JESNA)

Vol. 9 No. 2 August 2014

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido andNiwa Estimates with Ofcial Ones
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soshichi Kinoshita
Prolonged Deation and Structural Change in the Labor Market in Japan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sei Kuribayashi

C

o

e
t

Game Theoretical Simulations of the Exchange Rate Policy Using a Global
Macroeconometric Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taiyo Ozaki

n

n

t

s

Paying a Tribute to the Memory of Prof. Lawrence R. Klein (Nobel Prize Laureate
Economist); Honorary Editor-in-Chief of JESNA: Prof. Klein and North-East Asia
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuntaro Shishido

The Macroeconomic Effectiveness of Resilience Investment in the Context ofEarthquake Risk
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takeru Mori
Yusuke Kanda
A Survey of the Arguments Concerning China’s GDP Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Li

International Centre for the Study of East Asian Development (ICSEAD)

THE JOURNAL OF ECONOMETRIC STUDY OF NORTHEAST ASIA
(JESNA)EDITIONAL COMITTEE
Editors (Board Members)
Takahiro Akita
Soshichi Kinoshita
Reiitsu Kojima
Masaaki Kuboniwa
Shuntaro Shishido*
Tatsuo Hatta(International University of Japan)(Nagoya University)(Daito Bunka University)(Hitotsubashi University)(University of Tsukuba and International Universityof Japan)(International Centre for the Study of East AsianDevelopment)* Chairman of the Board

Co-editors
Kiyoshi Fujikawa
Osamu Nakamura
Sang-woo Nam
Haruki Niwa
Steven Rosefield
Hiroshi Sakamoto
Shinichiro Tabata
Yasushi Toda
Qiyan Wang

(Nagoya University)(International University of Japan)(Asia Development Bank)(Osaka Gakuin University)(University of North Carolina)(International Centre for the Study of East AsianDevelopment)(Hokkaido University)(University of Florida)(Renmin University of China)

THE JOURNAL OF ECONOMETRIC STUDY OF NORTHEAST ASIA is interested
primarily in publishing articles on econometric analysis of the Northeast Asian region(s),or related regions, including various types of modeling, econometric studies, and thedevelopment of a body of data, such as for national accounts, input-output tables, flow-of–funds accounts, and various indices of prices and quantities, etc.
The journal is published in English in principle twice a year.

The International Centre for the Study of East Asian Development, Kitakyushu (ICSEAD)
Address: 11-4 Otemachi, Kokurakita, Kitakyushu, 803-0814, JapanTel:+81-93-583-6202
Fax:+81-93-583-6576
E-mail: jesna@icsead.or.jp

The Journal of Econometric Study of Northeast Asia
Vol. 9 No. 2

August 2014

ContentsPaying a Tribute to the Memory of Prof. Lawrence R. Klein(Nobel Prize Laureate Economist); Honorary Editor-in-Chief of JESNA:
Prof. Klein and North-East Asia............................................................................................ Shuntaro Shishido

Page

1

Game Theoretical Simulations of the Exchange Rate Policy
Using a Global Macroeconometric Model............................................................................................ Taiyo Ozaki

3

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido andNiwa Estimates with Ofcial Ones............................................................................................ Soshichi Kinoshita

23

Prolonged Deation and Structural Change in the Labor Market in Japan............................................................................................ Sei Kuribayashi

33

The Macroeconomic Effectiveness of Resilience Investment in theContext of Earthquake Risk............................................................................................ Takeru Mori
Yusuke Kanda
A Survey of the Arguments Concerning China’s GDP Statistics............................................................................................ Jie Li

41

59

The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 2014

1

Paying a Tribute to the Memory of Prof. Lawrence R. Klein(Nobel Prize Laureate Economist);
Honorary Editor-in-Chief of JESNA:
Prof. Klein and North-East Asia
Editor-in-Chief, Shuntaro Shishido

We have been notied that Prof. Lawrence Klein, Emeritus Professor of the Universityof Pennsylvania, who served as an honorary Editor-in-chief for this journal, died at the ageof 93 on October 20, 2013. It was a great loss worldwide for intellectuals, particularly foreconomists, and people in economic and political elds. Prof. Klein was not only a promoter,but also a founder and creator of the econometric model in economics.
It was his great achievement that enlightened people all over the world that economic
measures are not simply the work of politicians, but succeed only when a model of
econometrics is applied as a scientic instrument.
Due to efforts made by the Professor and his followers, we learned that for people tobe free from poverty, and for the purpose of logical allocation as well as development of
national resources, the model of econometrics is an indispensable instrument.
In the autumn of 1999, Prof. Klein was invited to the North-East Asian International
Conference in Niigata sponsored by ERINA (Economic Research Institute for Northeast
Asia). In the conference he emphasized how important it was for regional peace andeconomic development to develop the economy of North-East Asia. To realize this heinsisted on the importance of carrying out international studies, leaving a deep impression
on people.
This journal, JESNA, was rst published to commemorate the Professor’s visit andaccepting a position as its honorary committee chairman. Since then he had strongly
supported JESNA’s publishing activities.
In retrospect, the life of Prof. Klein made outstanding contributions in the elds ofdeveloping and extending econometric models, particularly in terms of policy application,in the elds of academic services and practical cooperation. From short term economic up
to medium and long-term analysis, as well as economic forecasting and simulation analysisof specially designated areas, and furthermore, as the focus for models of multi-national
world and policy analysis, the Professor devoted the latter half of his life to international
cooperation with the United Nations.
On the other hand while Prof. Klein had been particularly interested in the Japanese
economy, he developed, in the 1960s, together with Prof. Shinichi Ichimura’s group at
Osaka University, a quarterly macro-economic model of Japan for the rst and the largest
scale.
In addition, he also developed several macro-models of Japanese economy including
demographic growth since the Meiji period, and macro-metric models of various types ofJapanese economy.
In his proposal for Japan’s macro-economic policies of the Japan-US relationshipearly in the 1970s, he proposed a policy program of “connective locomotives by the three

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The Journal of Econometric Study of Northeast Asia

countries of Japan, US and Germany”, and in the latter half of the 1980s, he offered many
valuable proposals to Japan’s policymaking authorities, such as participating in a joint
proposal for 430 trillion yen public investment in 10 years, out of his concern that Japanmight have to plunge into deation after the collapse of the bubble economy, and also
recently setting up a growth goal for Japan to escape from deationary trend, a proposal for
4% growth of real GDP.
At the same time, while arguments over the American economy were important, in aninterpretation of global ination after the worldwide oil shock in the 1970s, monetarists
from anti-Keynesian groups considered the importance of the money supply, and challenged
his interpretation. Prof. Klein and his followers fought back against them by applying thetheory of import cost-push ination, thereby prevailing over their opponents. Also, despite
criticism of the inationary tendency of macro-models, the Professor and his followers
emphasized the neutrality of the macro-econometric-model, in dealing with countercyclical polity and his counter argument also prevailed.
On the other hand, regarding the over-emphasis of demand side in the macro-models,the mainstream of macro-metric model economists with the Professor as its nucleus refuted
all sorts of criticism, and even introduced the role and operation of IT based on production
function, leading to endogenizing of the oating exchange rate which resulted in the selfdestruction of monetarist opponents. It was Prof. Klein’s real value that he always came up
with counter-arguments as well as constructive proposals through both theories andempirical evidences.
Research groups related to JESNA, following Prof. Klein’s philosophy that soundeconomic development is achievable only through the mutual understanding and cooperationof North-East Asian countries, hereby undertake to make further efforts, and we all
sincerely pray for the soul of the departed Prof. Klein.

The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 2014

3

Game Theoretical Simulations of the Exchange Rate Policy
Using a Global Macroeconometric Model
Taiyo Ozaki*

Abstract
We examined the impacts of the exchange rate policy on GDP under the gametheoretical framework which implies a single-player case, a cooperative case and a
Nash case. Against the commonly accepted story that the coordination can always yieldgains, we found that the optimal exchange rate policy differs by game type and thecharacteristics of the economy, especially how the country depends on the trade.
The magnitude of the adjustment of the exchange rate is in the following order;[Nash case > cooperative case > single-player case].
Although the Nash case may require the largest adjustment costs in many
economies, it does not necessarily mean that the contribution to the world economy of
the Nash game should be less than that of the cooperative adjustment because it boosts
the US economy most, which is linked to EU growth and has the dominant part of theworld economy.
The effects boosting the exports through the depreciation of the currency varyfrom nations and types of the game. Chinese economy has the largest effects if she could
act as a single-player. A competitive exchange rate adjustment, the Nash game, will
benet the US economy most, whereas Japan prefers the modest cooperative game.

K EYWORDS : new Keynesian model, global macroeconometric model, game theoreticalsimulation, exchange rate policy
JEL classication: E47, E62

1. Introduction
In September 2008, the Lehman crisis occurred, followed by a steep recession lastingfor several years. During such periods, if major countries try to reset their exchange rate
policies to compensate for the declining economy, what type of global adjustment of theexchange rate is desirable? We examined the characteristics of the exchange rate schemes
through a game theoretical framework using the actual global macroeconometric model.
Using the optimal control techniques, we tested the impacts under such game types as
the competitive Nash and the cooperative and single player cases. The model used herein isa small new Keynesian global macroeconometric model, which involves 4 countries andone aggregated area, namely, China, Japan, Korea, the US, and the Euro zone, and includes
almost 240 equations with forward expectation partly and bilateral trade models.
Dynamic optimization technics and game theoretical simulations, especially the case
* Professor, Kyoto Gakuen University. E-mail: ozaki@kyotogakuen.ac.jp
The author would like to express his gratitude to professors Soshichi Kinoshita, Mitsuo Yamada and anonymousreferees for their helpful comments and contributions to the nal version.
The research is supported by a Grant-in Aid for Scientic Research (#23530297) from the Japan Society for the
Promotion of Science.

4

The Journal of Econometric Study of Northeast Asiaof dynamic iterative/sequential games are well matched technically in calculating DP
problem and evaluating loss functions or pay-off matrices to reach the equilibrium
conditions.
Preceding researches which deal with the empirical analysis in this area are rather few
and can be found, for example, in Kawai (1993), Frey (2006), Wu (2004), McNeil andAsilis (2002) and Henry (1998), etc..
Kawai (1993) argued that international coordination of regime choice might benecessary to ensure optimal outcomes of the world, using the small two-country model.
Frey (2006) analysed the conditions under which coordination/non-coordination is
attractive to a country using an experimental model. He insists that results depend on therelative sizes of the country and the large country always has a preference for policy
coordination with the small country. Wu (2004) tried to nd the optimal strategy among
two different authorities in Singapore that is, National Wage Council and the Monetary
Authority. His result is almost same as the previous ones that the Nash-rule equilibrium is
unstable and the non-Nash rule becomes more meaningful. These results are all comingfrom static experimental models and assert the coordination may benet the welfare,however, as we show later, the coordination does not always yield the optimal solution inreality.
Meanwhile, an unique analysis that makes use of a linear quadratic control method isthe case of McNeils and Asilis (2002), in which they treat with the macroeconomic policy
games between France, Germany, Italy and Spain. In this paper they argue that the noncooperative use of broad money targets brings signicant welfare gains comparing to thecooperative exchange rate targets. Our analysis has a similar technical framework to thisapproach, however, of course, aims and the econometric model used herein are quite
different.

2. Methodology of the optimal control and the game
2.1 Loss function
When policy-makers attempt to achieve certain targets, such as GDP growth, ination
rate, and unemployment rate, how should policy instruments be managed? There may be
several sets of objectives and instruments. We dene the basic control problems byminimising the following loss function:(1)
where y is a vector of objective variables and y* is a vector of strategic targets, which is asubset of endogenous variables. w is a weight matrix, and I is the set of instrument/control
variables as a subset of exogenous variables x . The optimal control solution can be obtainedGame Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model5

iteratively as a function of y , I=(y,x;θ).
Several cases may exist, the most simple of which is the case with one target and one
corresponding instrument/control. This situation is a “single-player game”. However, inmany cases, there may be more than one policy-maker trying to pursue each goal. If theobjectives are combined into one target through negotiation and coordination, the optimal
policy for each player will be assigned to maximise/minimize the joint loss function. Thiscase is called a “cooperative game”. We should note that a solution in this case does notalways converge uniquely because the rank condition is not satised in general. If an agent
tries to forestall the plans of his or her opponents, the agent should alter the moves repeatedly
when the condition is changed under uncertainty. This case can be called a “Nash game”.
Later, we offer actual applications of the single-player, cooperative, and Nash games
regarding the exchange rate policy regime.
In reality, the loss function could be rewritten to involve a dumping or penalising
factor to suppress the time-inconsistent policy uctuations.

(2)
where, It is a vector of instruments.
2.2 Flow of the game
Flow of the game is quite same for all types of games except the denition of the loss
function and a set of instruments. Each player begins with the initial settings and solves the
DP problem simultaneously to evaluate the loss function at the rst step, and carries
iteratively alternation until converging.
Figure 1 Flow of the Game6

The Journal of Econometric Study of Northeast Asia

2.3 Calculation method
A general form of the nonlinear model is as follows,(3)
and we obtain the solution using iteratively numerical method that is PLQ \LWVL

\LWV 

To solve this, we used Newton’s Method which uses 1st derivatives of the model, in which
we consider the Taylor expansion around the point y = y(4)
Using the Jacobian matrix following(5)
until converging to the certain ε,
We calculate the s-th solution \LWV V  … 6 iteratively(6)
If the model contains forward looking variables, the formulation above may change.
The expectation at time t + k is
(7)
(8)

(

If we assume (  WW NN   , then we obtain a simple ‘model consistent’ estimates of future
values \W N  \WHH N _W  .

\W N  \W N _W 

We reset the nonlinear model with 1 period lead time to be,(9)
The expectation is replaced to the actual value in reality, and can be rewritten as follows,(10)Game Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

7

The difference equation system can be interpreted as the simultaneous equation systemhaving n × T stacked variables.

(11)

For solving the stacked system with 1-period lag and lead for example, we expand theJacobian matrix to have block tri-diagonal elements
(12)

(13)

The Newton method can be also applied for the solution of the model.
To calculate these system we set the terminal conditions as \L 7 M  \L 7 which areoutside the solution period. Though the terminal conditions are somehow arbitrary, solutions
reect the structures of the equilibrium which the error correction term involves.
The technical assistance can be obtained from Salemi (2006), and Fair (2003). The
software (a model solver) used here is “Winsolve 4.0”, developed by Pierse (2007) at Surrey
University, which may be the only software that can handle the optimal control problem
with multi-targets and multi-instruments, in which the large nonlinear econometric modelwith forward looking variables is available.

3. Small global model
3.1 Integration of short-term demand shocks and long-term equilibrium
Recent macro models have similar features that integrate short-time Keynesian
dynamics with long-term neo-classical supply-side equilibrium, such as the OECD’s new
global model (Kaline Herve, Nigel Pain et.al., 2011) or that used by Henry and Pagan(2004).
The presence of nominal rigidities in wage and price settings serves to slow the processof adjusting to external events such that the output is largely demand-driven in the shortterm but supply-driven in the long term.

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3.2 Flexible bilateral trade model
We adopted a bilateral trade model that includes not only the income/price effects
between two countries but also the indirect substitution or complementary effects of a third
country. To carry out this end in the plain forms, we estimated the “share in nominal
dollars” VLM  7LM  0 M of the trading partners derived from the translog cost function insteadof adopting a conventional trade model, in which the total cost is, in this case, the nominal
total imports.
3.3 Econometric features
An important characteristic of this model is the use of an ECM (error correction
model) formulation that complies with both long-term equilibrium and short-term shocks,
with the error correction term denoting the long-term equilibrium relation between key
variables that represents the co-integration process, and other explanatory variables
represent a variety of shocks.
Although a forward-looking formulation is not necessarily required, the model usuallycontains the model-consistent expectations Eyt+j.
3.4 Key equations
We propose a New Keynesian macroeconometric model composed of the following
key behavioural equations.
(14)
(15)

(16)

(17)

(18)
(19)
(20)

(21)

Game Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

9

where C is real consumption, I is real xed investment, G is real government
expenditures, X is real exports, M is real imports, y is real GDP, y* is potential GDP, wf isnancial wealth, πis ination rate, r is long-term interest rate, L is number of employees,
w is wage rate, K is capital stock, p is implicit GDP deator, Pc is CPI, PIF is xed investment
deator or PPI, PX is export price (exogenous), and PM is import price.
In reality, the model has almost 240 equations, including 5 countries/areas: China
Japan, Korea, the US and the EU. Its sample period is from 1990Q1 to 2010Q4. For thisstudy, the exchange rate is treated as exogenous and the key instrument. Details aredescribed in Ozaki (2013).
3.5 Interdependence between countries
We show the main characteristics of the model to make clear how the interdependence
between countries operates. First, taking the diffusion of the economic impacts to the othercountries through the overall scal multiplier, average of own multipliers of the US, Japanand China range from 1.25 to 1.35 and the US has signicant effects on Japan and China,
which have multipliers of 0.12 and 0.61 respectively. However, interdependent relations arequite asymmetric. The trends in the Chinese economy have potentially a great inuence on
world markets, but at this moment, the effects on the GDP of developed countries are rather
small and limited. The Korean economy is integrated greatly with China through the supply
chain of intermediate goods and has become to complement the Chinese economy.
Interdependence may vary due to the estimated parameters of the structural equations.
The following tables show the partial elasticity of the relative price combinations and theelasticity of GDP regarding imports of China, Japan and the US. The trade function includes
both effects of bi-lateral and third-country relations, for example, a rise in the export price ofJapan will cause the slowdown of imports of China from Japan followed by various changes
in third-country relations such as increase of imports from Korea and EU, and so on.
So Table 2 also presents the complement and/or substitute relation between countries.
A rise in export prices of China, for example, results in a reduction in China’s exports,
which will simultaneously cause a reduction in Korea’s exports. Therefore, China andKorea can be said to “complements” each other, on the other hand, a decline in US exports
will lead to an increase in the exports of all other nations/area, which indicates that thesecountries are “substitutes”. Documents are partly quoted from Ozaki (2013).

Table 1 International Diffusion of Fiscal Shock
US

Japan

EU

China

Korea

US expansion

1.28

0.12

0.01

0.61

0.31

Japan

0.01

1.25

0.00

0.26

0.10

EU

0.01

0.06

1.59

0.19

0.13

China

0.01

0.02

0.00

1.35

0.18

Korea

0.00

0.01

0.00

0.03

1.46

(*) effects on other countries by 1% expansion of GDPV at the 1st year average(%)

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The Journal of Econometric Study of Northeast Asia

Table 2 Partial Elasticity of the Trade Share of Relative Prices and GDP(Imports of China)
1% rise in export price of ...

JP→CN

US→CN

KR→CN

EU→CN

Yen (JP)

-0.05

+0.02

+0.14

+0.25

Dollar (US)

+0.18

-0.15

+0.41

+0.31

Won (KR)

-0.01

-0.14

-0.13

-0.59

Euro (EU)

+0.01

+0.09

-0.08

-0.07

Elasticity of GDP of China

0.39

0.38

0.82

0.08

1% rise in export price of ...

CN→JP

US→JP

KR→JP

EU→JP

RMB (CN)

-0.31

+0.33

-1.23

+0.17

Dollar (US)

+1.71

-0.81

+0.13

+0.28

Won (KR)

-1.34

+1.07

-0.21

+0.04

Euro (EU)

+0.29

+0.57

+0.25

-0.02

Elasticity of GDP of Japan

1.77

0.15

0.40

0.05

1% rise in export price of ...

CN→US

JP→US

KR→US

EU→US

RMB (CN)

-0.20

+0.18

-0.24

-0.89

Yen (JP)

+0.78

-0.65

+0.78

+0.12

Won (KR)

+1.13

+0.42

-0.34

-0.22

Euro (EU)

-0.25

+0.16

+0.06

-0.04

Elasticity of GDP of USA

0.28

1.56

1.47

0.10

(Imports of Japan)(Imports of the US)

4. Game theoretical simulation analyses
4.1 Setting up the gameWe assume that China, Japan, and the US (3 players) aimed to increase the real GDP
by 1% during the periods from 2008Q3 to 2009Q3 solely by depreciating the currency
value of the yuan, yen, and dollar, respectively. Of course, many stimulus packages wereactually imposed during that time in each country, but we used a highly simplied policy
measure for the purposes of this study to make clear the effects of the exchange rate policy
independently. GDP growth itself may not be the actual policy target for a developed
country in reality, ination target or reduction of the current account decit can be deemed
more relevant, whereas, the Lehman shock moved governments, especially the governmentof China to aim at maintaining the high growth rate to compensate the steep drop at thattime. This is why we adopt the growth rate as a policy goal. Besides this, one can point out
that setting up the exchange rate as a policy instrument seems too much simple andGame Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

11

Figure 2 Changes in Real GDP (%)

unrealistic to analyse an actual monetary policy, though, every country clings to induce
implicitly the exchange rate to be depreciated to the certain level through various channels.
Therefore, we regarded the exchange rate as the common and lucid policy instrument foreach country. It is expected even a simple scheme and the experimental simulation canbring some economic characteristics of the nation in the light.
We dynamically simulated the model with forward-looking variables from 2007Q1 to
2010Q4, including the above periods. Using the optimal control techniques, the optimal
regulator problem, we calculated the optimal path of the exchange rates of each player.
The games examined here are as follows,a) A single-player game: each government makes a move as an individual/independent
player. The other players are the mere followers to a specic policy leader, who may not
make some counter actions, and can hardly achieve the goals or sometimes get worse. This
does not assure a stable equilibrium, but only a temporal and partial solution.
b) A cooperative game: 3 players have a single aggregated target to achieve theircommon objectives as,(22)
j=CN,JP,US
Here, weights are arbitrary and affect greatly the efciency of calculation. If wesucceed in reaching the goal (L=0), every players have the same gain, that is, 1% growthpath. This means the cooperative equilibrium.
c) A Nash game: 3 players make each move iteratively without negotiation under the
given conditions set and forced by their adversaries. At the rst stage, each player can ’treach the goal (Lj≠0) and alters its policy each other, and repeat until converging, Lj =0 for
all j is attained. This is a sequential Nash equilibrium.
One thing we should note is a topic concerning the exchange rate of the US, US dollar
is of course the numeraire and is usually not treated the same as other currencies. However,
we treat this as one of exogenous variables in model specication as if it is a real variable(that is US_RXD as well as other currencies), which has the value of 1.0. For this reason,
US export price, for example, is dually dened as US_PX for the domestic use and US_PX$,12

The Journal of Econometric Study of Northeast Asia

(US_PX$=US_PX/US_RXD(=1)) for the trade models. Every country model including
the US model contains the same structures. In the control problem, it can be simply regarded
as one of controllable instrument, which has the value different from 1.0 and is consistent
with the optimized target.
Figure 3 China’s Optimal Exchange Rate (Yuan/$)Figure 4 Japan’s Optimal Exchange Rate (Yen/$)Figure 5 US Optimal Exchange Rate ($)(*) The base exchange rateof the US is set to 1.0.

Game Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

13

4.2 Optimal exchange rate
Obviously, the depreciation of each currency will drive the economy mainly by
expanding exports. The width of adjustment of the exchange rate is clearly narrow if
independent moves can be performed. In contrast, the Nash case indicates that it needs
excess action to pursue the target; consequently, it has an overshooting loss. Comparing the
Nash game with the cooperative game, the latter will be moderate for all players.
The Nash case shows that the adjustment after the period 2009Q4 takes longer and has
the tendency to become depreciative.
4.3 How does the single game affect the other countries’ performance?
No single authority can change its position on the exchange rate without being
perceived by the other authorities in reality, especially for the major countries studied
herein. However, it is important to know the partial effects.
When China makes a move independently, of course, China reaches the goal of 1%
growth, and Korea’s GDP growth will rise as almost higher as China, reecting the
Figure 6 GDP Impact of China’s Single Move

Figure 7 GDP Impact by Japan’s Single Move

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The Journal of Econometric Study of Northeast Asia

complementary integration of the manufacturing industry of Korea with China. In contrast,
the US economy will suffer by 0.1%, and the impact remains within relatively a low level,on the other hand, it helps Japan’s GDP slightly in the long run.
Japan’s move will cause the decline of all other economies, whereas a US move
reduces the GDP of export-oriented countries, such as China, Japan, and Korea, whileraising the EU economy coincidentally with the US economy.
Generally the magnitude of the impact is highest in the case of the US and what wecall “beggar-my-neighbour” effects can be observed widely except the case of China.
4.4 How does the cooperative/Nash game affect the other counties?
Comparing the cooperative game to the Nash game, the Nash game produces thegreater impacts on the tertiary countries, such as the EU. This difference denotes thepossibility that international coordination does not always yield gains. This result is quite
different from the conventional theory quoted previously, in which coordination is highly
benecial. Although the Nash solution may require excessive adjustment costs, the gains
also become greater for the world economy.
Figure 8 GDP Impact by the US’ Single Move

Figure 9 Impact on the GDP of the EU and Korea (Cooperative/Nash Case)Game Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

15

4.5 How does the game affect the demand structures?
First, we show briey the demand structures of China, Japan and the US at the year
2005, which denote percentages of the main element of the nominal GDP. As is well known,
Chinese economy has the peculiar features that it greatly depends on the external economy
and the investment. As a result, even today, attention of stabilizing the currency yuan is ofprimary importance for China.
Depending on the domestic demand characteristics and trading patterns of eachcountry, the import/export structures are affected quite differently. For example, Korea
strongly depends on the Chinese economy and keeps in step with China. The same thing istrue for EU, which owes much to the US economy, and China will adjust its trade structure
against the depreciation of other currencies to maintain 1% growth by reducing income
leakage to foreign countries. China maintains its GDP by reducing imports rather thanincreasing exports. Even if the depreciation of the yuan could be approved, it does not help
Figure 10 Demand Structures％( )(*) % of each major element of nominal GDP at 2005

Figure 11 China’s Demand Structure by Game Type(*) The contribution of each demand category in %

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Figure 12 Japan’s Demand Structure by Game Type

Figure 13 US Demand Structure by Game Typeto expand Chinese exports.
The depreciation of all major currencies will promote the exports of Japan; however,the cooperative game has effects on the exports that are twice as high as those for the Nashgame. The moderate coordination solutions seem to apply to Japan. Regarding the US
economy, the competitive Nash solution can boost exports, which offers the mostadvantageous results for the US economy.
As we have shown above, the most suitable exchange rate policy for a specic country
is not generally determined in advance. The single-player game is appropriate for China,
the cooperative game for Japan, and the Nash game for the US.
Regarding domestic demands, both of consumption and investments can be damaged
most in the case of Nash due to the rise in long term interest rates.

6. Concluding remarks
We examined the impacts of the exchange rate policy on GDP under the gametheoretical framework. We assumed that China, Japan, and the US had changed theirGame Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

17

exchange rate policies to compensate the sharp decline from 2008Q3 to 2009Q3 using
three games: the single-player, cooperative, and Nash games.
The optimal exchange rate policy differs by game type and the characteristics of theeconomy, especially how the country depends on trade. The width of the currency movement
is greatest for the case of the Nash game to achieve the same targets, whereas the singleplayer game is the lowest case. Although the Nash case may require the largest adjustment
costs in many economies, it does not necessarily mean that the contribution to the world
economy of the Nash game should be less than that of the cooperative adjustment because
it boosts the US economy most, which is linked to EU growth.
China is one of the most export-oriented countries and may prefer the sole depreciationof the yuan without being notied. Regardless of the game type, the depreciation of othermajor currencies will be negative for the Chinese economy. A competitive exchange rateadjustment, the Nash game, will benet the US economy most, whereas Japan prefers themodest cooperative game.
It is quite interesting that these tendencies seem to reect each nation’s mentality.
References
Fair, Ray C. (2003), “Optimal Control and Stochastic Simulation of Large Nonlinear Models with
Rational Expectations,” Computational Economics, 21, pp. 245-56.
Frey, Rainer (2006), “Coordinated or Uncoordinated Monetary Policy under a Regime of Flexible
Exchange Rates? – The Power in Decision Making,” Journal of Economic Integration 21(2), pp.
211-233.
Henry, Brian (1998), “The gains from international policy coordination,” Economic Outlook, 22, pp. 4-9.
Henry, S. G. B. and A. R. Pagan (2004), “The Econometrics of the New Keynesian Policy Model:
Introduction,” Oxford Bulletin of Economics and Statistics, Supplement 2004, 66, pp. 581-607.
Herve, Karine, Pain Nigel et.al. (2011), “The OECD’s New Global Model,” Economic Modelling 28, pp.
589-601.
Kawai, Masahiro (1993), “Optimal and Sustainable Exchange Rate Regimes,” IMF Staff Papers Vol. 40,
No. 2.
McNeils, Paul D and Carlos M. Asilis (2002), “Macroeconomic policy games and asset-price volatility inthe EMS: a linear quadratic control analysis of France, Germany, Italy and Spain,” Economic
Modelling 19, pp. 1-24.
Ozaki, Taiyo (2013), “A New Keynesian Approach to a Small Global Macroeconometric Model,” Studies
in Applied Economics, Vol. 7, pp. 23-59.
Pierse, Richard (2007), WinSolve version 4 tutorials, www.econ.surrey.ac.uk/winsolve/
Salemi, Michael K (2006), “Econometric Policy Evaluation and Inverse Control,” Journal of Money,
Credit, and Banking, 38, pp. 1737-1764.
Wu, Ying (2004), “Singapore’s Collective Bargaining in a Game of Wage and Exchange Rate Policies,”
Open Economics Review 15, pp. 273-289.

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Appendices
Appendix A
Diagram of the global macroeconometric model. (Quoted from Ozaki, 2013)

Game Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

19

Appendix B
List of Equations with respect to Japan Model. (Quoted from Ozaki, 2013)
Equations in the other country model are quite similar to the following specications.
(GDP)
JP_GDP = JP_C + JP_IF + JP_GC + JP_X JP_M
JP_GDPV = JP_CV + JP_IFV + JP_GCV + JP_XV JP_MV + JP_GDPVZ
JP_CV = JP_C * JP_PC / 100
JP_IFV = JP_IF * JP_PIF / 100
JP_GI = JP_GIV / JP_PIF * 100
JP_GC = JP_GCV / JP_PC * 100(Consumption)
DLOG(JP_C) = 0.553 0.163*(LOG(JP_C( 1)) (3.85 + 0.74*LOG(JP_PEDY( 1))+0.26*LOG(JP_PENW( 1) /JP_PC( 1)))) + 0.0405*DLOG(JP_PEDY) + 0.0745*DLOG(JP_PEDY( 1))+ 0.192*DLOG(JP_PENW/JP_PC) 0.679*DLOG(JP_PC/JP_PC( 1)) 0.0019*DLOG(JP_RSH)
JP_PEDY = JP_PEDYV / JP_PC * 100
JP_PEDYV = JP_PEWFP + JP_PENIR + JP_GCGPE + JP_PEOCR + JP_PEOY JP_TY JP_TSS
JP_PENW = 1915.4 + 1.47*(JP_PEDYV JP_CV) + JP_PENW( 1)JP_PEWFP = JP_ER * (JP_ET JP_ES) / 1000
JP_PENIR = 2565.9 + 0.547*(0.5*(((1 + JP_RLG/100)^0.25) 1) + 0.5*(((1 + JP_RSH/100)^0.25)1))*JP_PENW( 1)
LOG(JP_PEOY) = 41.9 + 1.95*LOG(JP_ER*JP_ES) + 1.64*LOG(JP_GDPV)(Investment)
DLOG(JP_IPNR) = 0.0876 0.0246*(LOG(JP_IPNR( 1)) LOG(JP_GDP( 1)) + 0.05*LOG(JP_RLG))+ 0.0986*DLOG(JP_IPNR( 1)) + 0.723*DLOG(JP_GDP) + 0.120*DLOG(JP_COGTP/JP_PIF)
1.86*DLOG(JP_PIF/JP_PIF( 1)) 0.01*LOG(JP_RLG/JP_PIF)
JP_IF = JP_IPNR + JP_GI + JP_IPRD + JP_IFZ
JP_COGTP = JP_GDPV JP_PEWFP JP_TX JP_TP JP_TC JP_PEOCR JP_PEOY JP_COZ
JP_K = (1.0 JP_DELTA) * JP_K( 1) + JP_IPNR(Export and Import)
JP_M$V = 12805.0 + 1.04*(WT_CNJP + WT_KRJP + WT_USJP + WT_EUJP + WT_RWJP)
JP_MV = 0.001*JP_M$V*JP_RXD
JP_M = 100*JP_MV/JP_PM
JP_X$V = 1053.5 + 1.01*(WT_JPCN + WT_JPKR + WT_JPUS + WT_JPEU + WT_JPRW)
JP_XV = 0.001*JP_X$V*JP_RXD
JP_X = 100*JP_XV/JP_PX(Trade Share)
LOG(WT_CNJP/JP_M$V) = 31.69 0.308*LOG((CN_PX/CN_RXD)/(JP_PM/JP_RXD))+ 1.71*LOG((US_PX/US_RXD)/(JP_PM/JP_RXD)) 1.34*LOG((KR_PX/KR_RXD)/(JP_PM/
JP_RXD))
0.284*LOG((EU_PX/EU_RXD)/(JP_PM/JP_RXD)) + 1.774*LOG(JP_GDP)
LOG(WT_EUJP/JP_M$V) = 2.873 + 0.696*LOG(WT_EUJP( 1)/JP_M$V( 1))+ 0.166*LOG((CN_PX/CN_RXD)/(JP_PM/JP_RXD)) + 0.281*LOG((US_PX/US_RXD)/(JP_PM/
JP_RXD))+ 0.0383*LOG((KR_PX/KR_RXD)/(JP_PM/JP_RXD))
0.0175*LOG((EU_PX/EU_RXD)/(JP_PM/JP_RXD))+ 0.0474*LOG(JP_GDP)
LOG(WT_KRJP/JP_M$V) = 7.019 1.238*LOG((CN_PX/CN_RXD)/(JP_PM/JP_RXD))+ 0.134*LOG((US_PX/US_RXD)/(JP_PM/JP_RXD)) 0.206*LOG((KR_PX/KR_RXD)/(JP_PM/JP_RXD))

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+ 0.246*LOG((EU_PX/EU_RXD)/(JP_PM/JP_RXD)) + 0.399*LOG(JP_GDP)
LOG(WT_USJP/JP_M$V) = 0.976 + 0.337*LOG((CN_PX/CN_RXD)/(JP_PM/JP_RXD))
0.813*LOG((US_PX/US_RXD)/(JP_PM/JP_RXD)) + 1.066*LOG((KR_PX/KR_RXD)/(JP_PM/JP_
RXD))+ 0.572*LOG((EU_PX/EU_RXD)/(JP_PM/JP_RXD)) + 0.146*LOG(JP_GDP)(Price)JP_PM$ = 60.0 + 0.412*((WT_CNJP + WT_KRJP + WT_USJP + WT_EUJP + WT_RWJP)/(WT_CNJP/(CN_PX$*1.11)+ WT_KRJP/KR_PX$ + WT_USJP/(US_PX*1.10) + WT_EUJP/EU_PX$ + WT_RWJP/(US_PMFU*86/54)))+ [AR(1) = 0.89]
JP_PM = 1.0*JP_PM$*JP_RXD/100
JP_PX$ = 1.0*100*JP_PX/JP_RXD
DLOG(JP_PIF) = 0.00117 0.00236*(LOG(JP_PIF( 1)) 0.1*LOG(JP_ER( 1)) 0.9*LOG(JP_PM( 1)))
+ 0.218*DLOG(JP_PIF( 1)) + 0.575*DLOG(JP_PIF( 2)) + 0.0613*DLOG(JP_ER) + 0.0764*DLOG(JP_PM)+ 0.0616*DLOG((1 + JP_TXRA))DLOG(JP_PC) = 0.1117 0.0662*(LOG(JP_PC( 1)) 0.7*LOG(JP_ER( 1)) 0.3*LOG(JP_PIF( 1)))
+ 0.123*DLOG(JP_PC( 1)) + 0.169*DLOG(JP_PIF) + 0.00746*DLOG(JP_PM) + 0.0898*DLOG(JP_ER)+ 0.650*DLOG((1 + JP_TXRA))
JP_PGDP = 100 * JP_GDPV / JP_GDP(Wage)DLOG(JP_ER) = 0.306 0.0439*(LOG(JP_ER( 1)) + LOG(JP_YHAT( 1)/JP_GDP( 1))) + 0.331*DLOG(JP_ER( 4))
0.145*DLOG(JP_YHAT/JP_GDP)(Potential Output)
LOG(JP_YHAT) = 9.769 + 0.125*LOG(JP_K) + 0.0378*LOG(JP_ET) + [AR(1) = 0.983](Labor)DLOG(JP_ET) = 0.106 0.0121*(LOG(JP_ET( 1)) 0.22*LOG(JP_GDP( 1)) + 0.15*LOG(JP_ER( 1)/
JP_PGDP( 1)))
+ 0.464*DLOG(JP_ET( 1)) + 0.217*DLOG(JP_GDPV) 0.0568*DLOG(JP_ER/JP_PGDP)
JP_U = 1.00*JP_LS 1.00*JP_ET
JP_UP = 1.637 + 1.00*JP_U/JP_LS*100 0.00000186*JP_UP( 1)(Interest Rate)
JP_RLG = 2.881 + 0.840*JP_RLG( 1) + 0.1307*(0.5*JP_RSH + 0.5*US_RSH) + 2.839*(JP_GDPV/
JP_GDPV( 1))+ 0.0073*(JP_GGDBT/JP_GDPV)(Government)
JP_TY = JP_TYR * (JP_PEWFP + JP_PENIR + JP_GCGPE + JP_PEOCR + JP_PEOY)
JP_TX = (JP_TXR + JP_TXR B) * JP_CV + JP_TXFU
JP_TP = JP_TPR * JP_PEWFP
JP_TSS = JP_TSSR * JP_PEWFP
LOG(JP_TC) = 5.39 + 0.377*LOG(JP_TCRR*JP_COGTP)
LOG(JP_GCGPE) = 8.314 + 0.541*LOG(JP_PC/JP_PC( 4)) + 0.133*LOG(JP_U) + 0.00683*TREND
JP_GREV = JP_TY + JP_TX + JP_TP + JP_TC + JP_GDIR + JP_TSS + JP_GREVZ
JP_GEXP = JP_GCV + JP_GIV + JP_GCGPE + JP_GDIP + JP_GEXPZ
JP_GB = JP_GREV JP_GEXP
JP_GGDBT = JP_GGDBT( 1) JP_GB + JP_DBTPOLGame Theoretical Simulations of the Exchange Rate Policy Using a Global Macroeconometric Model

21

Appendix C
List of Variables
Main data source is Oxford Economics Global Data Bank, and the variable list below is also quoted
from Oxford Economics with respect to the Japan model, however, the variable names are the same as inall the other countries’ models, for example, JP_C, CN_C, ....Abbreviation codes for the country are US,
JP (Japan), EU, CN (China), KR (South Korea), WT (trade between i and j)

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The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 201423

On Japan’s Growth Potential and the GDP Gap: A Comparisonof Shishido and Niwa Estimates with Ofcial Ones
Soshichi Kinoshita*

Abstract
The potential output or the GDP Gap of the Japanese economy has become an
important indicator for judging the short-run and long run trend of the depressed
Japanese economy since the late 1990s. The Cabinet Ofce, Government of Japan(CAO) and the Bank of Japan (BOJ) have ofcially released their estimates at thequarterly base. Dissenting from these ofcial estimates, Shishido and Niwa estimated it
independently, and, proposed the alternative macroeconomic policies to escape fromthe depressed state of the Japanese economy. This paper examines, focusing on Shishido
and Niwa estimates the sources of their differences with the ofcial ones from threepoints of view; factor shares, TFP growth rate and potential factor inputs. It is shownthat the differences are mostly dissolved by applying the common assumption on thesethree factors in the calculation.

K EYWORDS : Japan’s GDP Gap; Potential Output; Aggregate Production Function; LaborShare
JEL Classications: E1

1. Introduction
Since the bubble burst in the early 1990s, the Japanese economy has been in the
prolonged period of poor performance in which real GDP growth has averaged 1 percent
per year over the decades. Many studies have been done on the causes behind the growth
slowdown theoretically and empirically; whether it was caused by either demand side
factors or supply side ones.
Kazuo Sato (2001) reviewed critically the estimates of Japan’s GDP potential and thederived GDP gap, focusing on the Shishido and Niwa estimates in relation to ofcial onesby BOJ and CAO. He pointed out the inappropriateness of private gross and net capitalstock estimates used in their studies, and the difference in the assumption on technicalchange was the main factor to cause the difference in potential GDP and GDP gap estimates.
He concluded that both Shishido and Niwa overestimated the GDP gap, while ofcial
estimates were underestimated.1
This paper re-examines the GDP gap estimates by Shishido and Niwa based on the updated
data covering the 2000s, and show the sources of their overestimation against the ofcial ones.
* Professor emeritus, Nagoya University. E-mail: kinosita@sugiyama-u.ac.jp
The author would like to thank professors Shuntaro Shishido and Mitsuo Yamada for their helpful comments andsuggestions.
1
Sato (2001). He reviewed two groups of sharply contrasting estimates of Japan’s GDP gap, one of which was
CAO and BOJ, named optimists, the other was Shishido and Niwa, called pessimists.

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2. Measurement of the GDP Gap
The GDP gap is an indicator to measure the demand-supply imbalance of macro
economy, dened as
GDP gap= (actual GDP/potential GDP 1)×100 (1)where potential GDP is the GDP that can be produced by an economy if all its resources of
production are utilized at the average level of the past usage. When actual GDP is equal topotential GDP, the GDP gap is zero, otherwise becomes positive or negative.
The crucial point in the measurement is how to calculate the potential output. The
most popular method used in the studies is based on production function approach,especially the usage of Cobb=Douglas production function in the following,Yt = AtKtaLtb(2)Here the determinants of real GDP(Y) are capital input (K), labor input (L) and totalfactor productivity (A). Two elasticity parameters, a and b determine the contributions of K
and L to output. There are two ways to specify the two parameters, the rst is to estimate
directly the Cobb=Douglas function with the Ordinary Least Squares method, where At is
assumed to grow at an exponential rate (ect). The other is to derive the parameters from thefactor shares of K and L by assuming the equilibrium conditions in the factor market withconstant return to scale. In both cases, potential GDP is calculated by inserting the potential
levels of K, L and the past trend of TFP into the production function.
The procedure adopted by CAO to derive potential GDP is summarized in thefollowing.
Potential inputs of capital and labor are dened as follows,
Potential input of capita:
xed capital stock×potential rate of capital utilization (3)Potential input of labor:
population aged 15 or over×potential rate of labor force participation× (1 rate of structural unemployment)×potential hours of work (4)The potential GDP estimates are heavily depending on the assumed levels of potential
inputs of K, L and the treatment of technical progress.
Here, potential levels of labor force participation rate and hours of works are computed
as the trend components obtained by using Hodrick=Prescott lter to the observed variables.
As for the potential rate of capital utilization, the actual operation rates of manufacturing
and non-manufacturing industries are adjusted to exclude the cyclical factors by making
regression of these variables with TANKAN survey data of BOJ.2 The rate of structural
unemployment is estimated using UV analysis to differentiate structural unemployment
from cyclical unemployment based on the relationship between the unemployment rate andthe vacancy rates.3,4
2

TANKAN survey is an abbreviation of the Short-term Economic Survey of Enterprises in Japan.
As for the concrete method to allocate the reta of unemployment into cyclical unemployment and structuralfrictional one, see the appendix note in the Annual Report o Economy and Public Finance 2006 and Useful Labor
Statistics 2012.
4
BOJ re-examined the estimation method of GDP gap at the end of 2005 and since 2006 almost same procedure
as CAO has been taken to calculate the gap.
3

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido and Niwa Estimates with Ofcial Ones25

3. Case of Shishido
Shishido measured the potential GDP by estimating an aggregate production functionof Cobb=Douglas type with the time series data from 1973 to 2004 and concluded that theJapan’s GDP gap at 2007 was much larger than the ofcial estimates. He proposed
furthermore that to ll the large gap, aggressive demand management policies of Keynesiantype was urgently needed.5
He estimated the following aggregate production function for the period from 1973 to2004,
Ln(GDP)=3.8693+0.26439*ln(KPG(3))+0.81743*ln(L*LHRTL) 0.15392*ln(URATE)(7.5844)(7.9002)(7.817)+0.1255ln (KGGSNA/KGGSNA (1)) +0.01846*TREND (1.3115)(14.2054)
DW=1.8477
AdjR2 =0.9988(5)

where KPG=gross xed business capital stock, L= number of workers, LHRTL=hours
of works, URATE=rate of unemployment, KGGSNA=public xed capital stock,
TREND=time trend. Numerical values shown below the estimated parameters are t-values.
In this equation, the values of the elasticity of production with respect to capital andlabor are 0.264 and 0.817 respectively, and the sum of them is 1.08 showing the existence ofa weak scale economy. The rate of unemployment might have been included as a proxy of
the rate of capital utilization. The growth rate of TFP, as measured by time trend, is 1.8%
at the annual average rate over the whole sample period.
To apply the CAO procedure -explained above to Shishido’s estimates of the potential
output, we re-estimated his production function with the data from 1981 to 2004.
The result is as follows,
Ln(GDP)=4.1915+0.2283*ln(KGP(1))+1.1459*ln(L*LHRTL)0.1054*ln(URATE)(7.5290)(9.8115)=6.8877)+0.01532*TREND
(7.2383)(6)
AdjR2 =0.9983

DW=2.0292

To measure the potential output with the production function, Shishido assumed full
utilization rate of capital (100 %) and the potential capital stock is set equal to actual capitalstock. As for the potential labor input, total hours of works in 1980 (170 hours per month)
are equated with the potential hours of work, and structural unemployment rate is set to theaverage rate of unemployment, 2.1% (observed in the latter half of 1970s), and potentiallabor participation rate is equated with the actual rate. The potential factor inputs thus
determined and the estimated TFP of 1.5% are combined to calculate the potential output,
where factors of production are fully employed.
5
Shishido (2004, 2010). Both Shishido (2004) and Shishido (2010) estimated the same type of Cobb=Douglas
production function for the periods, 1957-1998 and 1973-2004 respectively.

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It is shown in Figure 1 that the GDP gap calculated from the Shishido’s potential
output has become quite large since the end of bubble economy around 1992, and increased
from one digit to two digits level, 20% in 2007.6 This gap was larger than the ofcial ones
because it does not take into account of both the statutory reduction in working hours from1988, and the upward trend of structural unemployment observed since the latter half of1990s.
However if we apply the CAO assumptions on potential inputs and TFP growth to
Shishido’s model, an alternate GDP gap (case2) is obtained as shown in Figure 2, wheretime series properties are very similar to that of CAO.7
Figure 2 shows that GDP gaps were positive in the bubble period due to the excess
demand over potential supply, and almost zero from 2006-2007. It turned into negative
after the Lehman shock in 2008. However the gap is around 8%, much smaller than his
original estimates of 20%. The GDP gaps uctuate within one digit order over the whole
period after 1980.
Figure 1 GDP Gap: Shishido’s Estimates (case 1)(%)

Figure 2 GDP Gap: Shishido’s Estimates (case 2)(%)

6
Shishido showed the GDP gap estimates only for the extrapolation period (2007-2020), not for interpolation
period. But the widening tendency of the gap is maintained.
7
Shishido (2004) showed the GDP gap estimates only for the extrapolation period (2007-2020), not forinterpolation period. But the widening tendency of the gap is maintained.

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido and Niwa Estimates with Ofcial Ones27

4. The Case of Niwa
Niwa also measured the GDP gaps for almost the same period as Shishido did. His
results diverged farther from the ofcial estimates as compared to Shishido. 8
His GDP gap depends on a Cobb=Douglas production function with constant returnto scale and endogenous technical progress. Dening M =total factor input, K=capital input,
L=labor input and A=total factor productivity index (TFP), his production function is
written as,
GDP = At × Mt(7)M = Kta × Ltb(8)where M is a geometric mean of K and L weighted by elasticity parameters, a and b. He took
the case of constant return to scale, a+b=1.
As for the productivity factor, he didn’t use TFP estimates derived from Solow
residuals, but assumed that TFP growth, g(A) is proportional to the GDP growth, g(GDP)as follows,d=g(A)/g(GDP)(9)where d is a proportional factor given exogenously. As g(GDP)is the sum of g(A) andg(M), this implies that g(A) is determined proportionally by g(M). As a result, potential
GDP and GDP gap had tendency to be calculated larger when larger value is set for theparameter d, accordingly higher technical progress.
Labor share of National Income is set equal to output elasticity of labor, b, using theequilibrium condition of factor market. He examined two cases based on the System of
National Account 1985 as shown in Table 1.
The potential input of labor is derived from actual labor force, which includes the
structural unemployment (set equal to actual unemployment) and hour of works xed at thelevel of 1970. On the other hand, potential capital input is given by the private gross xed
capital with full utilization (100%) of capital. As for the technical progress, the case of
1/3.5 is taken here among the four alternative cases.
Table 1 Labor Share: Cases of Niwa’s Estimate9
case

Denition of labor share

Labor share

Capital share

Original weight

Employee’s income/GDP

0.544

0.456

Adjusted weight

Adjusted employees income/GDP

0.58

0.42

Note: adjusted employees income =compensation of employees and 0.549* operating surplus of unincorporated
enterprises. 0.549 is the labor share of private incorporated enterprises for 1985. See the Appendix on thedetails of alternative denition of labor share.

8

Niwa (2000, 2006). Niwa (2000) covered the period 1970-1997 and Niwa (2006) extended to samples from 1970
to 2004.
9
As regards the denition of the factor shares in the SNA, alternative formulae are proposed and utilized in the
study. See the details in Appendix.

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Figure 3 GDP Gap: Niwa’s Estimate (case 1, original case)(%)

Figure 4 Niwa’s Estimate (case2, capital share is set at 0.33)(%)

Figure 3 shows the calculated GDP gaps based on Niwa’s assumption of potential
factor inputs and technical progress. GDP gap here have been widened to about 15% since
the latter half of 1970s, and peaked near 45% in 2002. The GDP gaps in the early 2000s
were ten times larger than those of CAO.
The large Niwa’s GDP gaps is comparing to those of CAO might be highly dependent
on the his assumptions of(1) Labor share and technical progress(2) Potential inputs of capital and labor.
To examine the inuence of different assumptions on capital share, CAO’s share, 0.33,
was used in place of the original share. As depicted in Figure 4, the decreasing values ofcapital share from 0.456 to 0.33 resulted in the reduction of potential GDP, and revised GDP
gap reaches by 20% in 2000.
Further, Figure 5 shows the GDP gap obtained when the potential inputs of capital andlabor were adjusted to the CAO procedure. It is concluded that the adjusted Niwa’s estimates(case 3) are comparable to the ofcial ones, and cyclical movements since the 1970s arewithin the one digit level.1010Kamata and Masuda (2000) surveyed the effects of the measurement errors in factor shares and other key
statistics on the GDP gap estimates based on an aggregate production function. The capital stock data used by
Niwa was found to be higher by 0.2% at the average annual growth rate than the updated CAO estimate. In thecomparison, Niwa’s capital stock data was replaced by the updated one.

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido and Niwa Estimates with Ofcial Ones

29

Figure 5 Niwa’s Estimates (based on the CAO procedure except for g(A)(%)

5. Comparisons of Alternative Estimates of the GDP Gap
CAO has released the GDP gap estimates for business indicator in the Indexes ofBusiness Conditions monthly and Annual Report on the Japanese Economy and PublicFinance.
Also BOJ has been associated with the analytical issues related to potential output and GDP
gap, and utilizes her own GDP gap estimates for judging business and price trend.
In calculating the potential output for GDP gap, CAO and BOJ use almost the sameprocedure explained above except for private xed capital stock data. CAO uses the “Gross
Capital Stock of Private Enterprises” data compiled by the Economic and Social ResearchInstitute, while BOJ changed stock data to the net xed capital stock of JIP (Japan Industry
Productivity) data base at the Research Institute of Economy Trade & Industry. The reason
pointed by BOJ is the overestimates of Gross Capital Stock data.
Two estimates of GDP gap can be compared in Figure 6, where BOJ series are smaller
than CAO ones, with wider difference in the period of bubble economy from 1988 to 1992.
Shishido’s GDP gap (case 2), re-calculated by the author using the CAO procedure, iscompared with CAO in Figure 7, where cyclical patterns are quite similar between the two
estimates, but Shishido’s gap is larger by 1~2% after the bubble burst. This may be due to
Figure 6 GDP Gap Estimates: CAO vs. BOJ

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Figure 7 A Comparison of GDP Gap: Shishido vs. CAO

Figure 8 A Comparison of GDP Gap: Niwa vs. CAO

the difference in the estimated production elasticity and TFP growth rate.11
Niwa’s estimate revised by the author using the CAO procedure is shown in Figure 8.
It is seen from here that until 1991 the two series followed the almost same pattern, andsince then had a tendency to diverge from each other. The main reason lies in the treatmentof technical progress. He assumed a proportional factor between economic growth andtechnical progress. In this paper, we examined only the case that d is set at 1/3.5. The
difference of his estimates becomes larger than CAO if d is set larger than 1/3.5.

6. Concluding remarks
This paper revisited the GDP gap issues raised by Shishido and Niwa and examinedthe differences between their estimates and ofcial ones by referring to the relevant
production parameters including technical progress and denition of the potential factor
inputs. The observed differences in the gaps can be almost accounted for by the differencesin the assumptions for estimates when recalculated by the author using the common
procedure of potential GDP adopted by CAO.
11

CAO estimates the elasticity of production from factor share, and TFP growth rate from Solow’s residuals.

On Japan’s Growth Potential and the GDP Gap: A Comparison of Shishido and Niwa Estimates with Ofcial Ones

31

Reference
BLS (2007), “Technical Information about the BLS Multifactor Productivity Measure”, US Bureau ofLabor Statistics, September.
BOJ (2006), “The New Estimates of Output Gap and Potential Growth Rate”, Bank of Japan Review,
May.
Hayashi, Fumio and Edward Prescott (2002), “The 1990s in Japan: A lost Decade”, Review of Economic
Dynamics, vol. 5 Issue 1. pp. 206-235.
Niwa, Haruki (2000), “The recent deationary gap in Japan: a quantitative measurement”, Journal of
Asian Economics, vol. 11 Issue 2, pp. 245-258.
Niwa, Haruki (2003), “Deationary Gap in Japan, 1970-2000: A Quantitative Measurement”, Journal of
Economic Policy Studies, vol. 1 Issue 1, pp. 1-33.
OECD (2001), Measuring Productivity: Measurement of Aggregate and Industry level Productivity
Growth, OECD Manual.
Sato, Kazuo (2001), “Japan’s GDP estimates: a critical review”, Journal of Asian Economics, vol.12 Issue
1, pp. 21-36.
Shishido, Shuntaro (2004), “Japan’s economic growth and policy-making”, in Erik Dietzenbacher andMichael L. Lahr eds. Wassily Leontief and Input-Output Economics.
Shishido, Shuntaro, Akira Kawakami and Kiyoshi Tamashiro (2010), “Policy Alternative for Japan
toward 2020”, in Shinichi Ichimua and Lawrence R. Klein eds. Macroeconometric Model of Japan,
World Scientic.
Appendix: Alternative denition of labor share in the National AccountIn the UN System of National Account, GDP at the income side is dened as follows,
GDP=compensation of employees + operating surplus and mixed income+ taxes on productions
and imports subsidies + consumption of xed capital
The labor share is typically dened as a ratio of compensation of employees to an output measuresuch as GDP, and calculated as the following formula,
LS= compensation of employees/GDP
In order to measure the true contribution of labor to output, some adjustments are required both to thenumerator and denominator. First, in the numerator, a large part of the operating surplus of unincorporated
enterprises (mostly self-employed) should be added as the labor income. Second, tax on production andimports and imputed service of owner-occupied dwellings need to be excluded from the denominator.
As for the estimation of labor income of unincorporated enterprises, two methods are available: one
is to assume that the average compensation per hour of self-employed person equals that of a wage earner(case a), and the other is to apply the labor share of corporates to that of operating surplus of unincorporated
enterprises (case b).
The following results are obtained for the alternative labor share in Table 1.

Appendix Table 1 A comparison of labor share: 1980- 2009
Unadjusted compensation of
employees/GDP
Adjusted compensation of
employees(case a)/GDP
Adjusted compensation of
employees(case b)/Y

Labor share average

Standard deviation

Capital share average

0.53040.00140.4696

0.65110.0392

0.34890.67340.01650.3266

Note: Y=GDP – (tax on production and imports + imputed service of owner-occupied dwellings).

The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 201433

Prolonged Deation and Structural Change in the Labor
Market in Japan
Sei Kuribayashi*

Abstract
Japan’s economy has fallen into deation since the middle of the 1990s. It is oneof main objectives of so-called Abenomics to overcome this prolonged deation in two
years or so. We divide the entire period from 1981 to 2012 into three basic periods: theperiods 81-91, 92-97, and 98-12. We estimated general price equations, and macroeconomic relations such as Phillips curve and Okun’s law in the labor market, andtested structural change between these periods. We found structural change of theseequations between the periods 81-91 and 92-12. Prolonged deation exerted a great
inuence on the labor market in Japan. Some experimental calculations with ourestimated equations tell us that cumulative 10% growth rate will be required to overcomepresent prolonged deation, although the price target of 2% will not be reached.

K EYWORDS : Prolonged Deation, Structural Change in the Labor Market, CPI Equation,
Phillips Curve, Okun’s Law, BOJ’s 2% Ination Targeting.
JEL classications: E24, E31

1. Introduction
Recently, the question of how to overcome prolonged deation has become a major
issue in Japan. So-called “Abenomics” has attracted attention not only domestically but
also internationally. Therefore, we have to know what economic factors decisively inuence
general prices in a quantitative manner. It is theoretically and widely debated whether
monetary factors such as money supply, or real ones such as output gap, mainly matter ingeneral price determination.
The purpose of this paper is to estimate general price equations and macro-economic
relationships in the labor market in Japan, and quantitatively assess economic growth andother factors required to overcome Japan’s prolonged deation.
The organization of this paper is as follows: in section 2, after briey describing the
movements of general prices and related indicators, we estimate general price equations
and test structural change between the period prior to the burst of the bubble and the postbubble era; in section 3, we estimate macro-economic relationships in the labor market andalso test structural change; lastly, we examine economic growth and other main factors
required to overcome the prolonged deation based on the estimated equations in the nal
section.

* Research Institute for Advancement of Living Standards. E-mail: fzt02645@nifty.ne.jp

34

The Journal of Econometric Study of Northeast Asia

2. Price Equations
In the 1970s, two oil shocks brought about high ination in Japan as well as othermajor countries in the world. Japan’s economy somehow managed to weather the second oilshock and avoided double-digit ination, unlike in the rst one. After the two oil shocks,
Japan’s ination in terms of consumer prices decelerated from 7.8% in 1980 to near zero
percent in the middle of the 1990s, except in peak years in the bubble era.
Deation is internationally dened as an economic situation in which general prices
decline for more than or equal to two years consecutively. In general, we consider the
consumer price index (CPI), private consumption deator or GDP deator (PGDP) as arepresentative indicator of general prices. In Japan, the CPI (all items) and the private
consumption deator started to fall consecutively year by year around 1998, and the PGDP
has declined since around 1995. That is to say, Japan’s economy has been in deation for
more than fteen years since the late 1990s.
Figure 1 illustrates annual rates of increase in the CPI, the PGDP, the compensation of
employees per capita (wage, hereafter), and the GDP gap. We divide the entire period from1981 to 2012 into three basic periods in this paper: “middle-growth” from 1981 to 1991 (8191), “bubble-aftermath” from 1992 to 1997 (92-97) and “prolonged-deation” from 1998 to2012 (98-12). As seen in Figure 1, the rates of increase in the CPI have been below zero
since 1999 and those in the PGDP since 1995. Since we consider GDP deator is the mostappropriate economic indicator for general prices, it is reasonable to assume that Japan’s
economy fell into deation in the middle of the 1990s. In particular Japan’s deation has
worsened since 1998 as wages started to decrease consecutively.
Table 1 shows the average annual rate of increase in price-related indicators for eachperiod. In the 81-91 period, Japan’s economy grew at the average rate of 4.6%. Importprices declined at the average rate of 6.8%, which reected a 6.7% annual drop of crude oil
price and 6.3% annual appreciation of the yen. Wages increased at the rate of 3.5%, which
was slightly higher than the rate of increase in productivity (3.4%). The GDP gap, which ismeasured by the ratio of the difference between potential and actual GDP to potential GDP,uctuated cyclically at the level of 1.9%. The Bank of Japan (BOJ) reduced the ofcial
discount rate consecutively from 1981 to 1988, and accommodated the money supply
Figure 1 Prices, Wage, and GDP gap

Prolonged Deation and Structural Change in the Labor Market in Japan

Table 1 Average Annual Rate of Increase in Sub-periods
Import price Exchange
Average
Wage rate Productivity
Index
rate
crude price35(Percent)GDP
deatorSub-period

CPI

CGPI

81-91

1.77

0.79

6.84

6.29

6.65

3.49

3.37

1.35

92-97

0.74

1.13

1.44

2.78

0.52

0.87

0.97

0.10

98-12

0.23

0.14

2.43

2.65

14.72

1.09

1.01

1.27

0.13

0.08

2.25

1.35

12.63

0.94

1.02

1.13

Reference
95-12

Sub-period

Real growth Nominal
rate
growth rate

81-91

4.63

92-97
98-12

M2+CD

Unemployment rate,
Monetary
Total
Employees average, (minimum andbase
employment
maximum)

6.05

9.07

7.78

1.22

1.98

2.49

(2.1, 2.8)

1.34

1.45

2.25

4.65

0.75

0.53

2.29

4.91

0.37

1.13

2.93

(2.2, 3.4)

0.22

0.25

4.63

(4.1, 5.4)

0.79

0.35

2.48

5.77

0.18

0.27

4.41

(2.9, 5.4)

Reference
95-12

Note: 1. CPI denotes consumer price index, CGPI denotes corporate goods price index and M2+CD denotes
M2+certicated deposits.
2. Productivity is dened as the ratio between real GDP and total employment.

required for the average growth of 4.6%. As a result, the CPI increased on average at 1.8%.
But Japan’s economy was in so-called disination. That is to say, the rate of increase in the
CPI reduced year by year from 4.8% in 1981 to 0% in 1987, although the CPI increased at
the rate of around 3% at the peak of the bubble era.
In the period 92-97, Japan’s economy stagnated with an average growth of 1.3%, and
the GDP gap became very high in the rst three years, although rather high growth in 1996
and 1997 reduced it to a level a little bit higher than the average in the previous period.
Import prices declined at the average rate of 1.4% and wages increased at the average rateof 0.9%. The CPI increased at the average rate of 0.7%, which would have been lower
unless the consumption tax rate had been raised by two percentage points in April 1997.
The BOJ eased its monetary policy after the burst of the bubble, and lowered the ofcial
discount rate consecutively from 6.0% in 1991 to 0.5% in 1997. The money supply increased
at the average rate of 2.2%. Japan’s economy fell into deation in 1995 in terms of the GDP
deator.
In the 98-12 period, Japan’s economy slipped into a deationary spiral, and the GDP
gap widened with an upward trend as can be seen in Figure 1. The average growth-rate inthis period was further reduced to 0.8%, although Japan’s economy grew at the rate of about
2% over the ve years from 2003 to 2007. As a result, the CPI declined at the average rateof 0.2%, and the PGDP at the average rate of 1.3%, despite the fact that import prices
increased at 2.4%, and the BOJ relaxed restrictions by adopting a zero interest, quantitative
monetary policy. In particular, the PGDP has decreased every year since 1995.1
1Actually the PGDP increased at 0.6% only in 1997. But if the consumption tax rate had not been raised, it would
not have risen.

36

The Journal of Econometric Study of Northeast Asia

Taking into account the above-mentioned trends in price-related economic indicators,
we estimated price equations by the ordinary method of least squares, and the estimatedresults are shown in Table 2. We regressed both the CPI and the PGDP with respect to the
GDP gap, the unit labor cost, the money supply, and the exchange rate over the periods 8112 and 92-12. We also tried to include import prices in the explanatory variables, but wecould not obtain the expected positive coefcients, and these are not shown in Table 2.
For the entire sample period, we obtained expected signs for coefcients on all
Table 2 Price Equations
Constant GDP gap

Unit laborcost

0.79 0.1236

0.4397

Money
supplyExchangerate

Sampleperiod

R2

s

d

0.74

0.74

0.92 1981-2012

0.50

0.62

F=4.15
1.58 1992-2012 P=0.0158

0.75

0.72

1.55 1981-2012

0.50

0.62

F=2.73
1.63 1992-2012 P=0.0527

0.78

0.67

1.63 1981-2012

0.48

0.64

F=2.491.62 1992-2012 P=0.0699

0.78

0.68

1.63 1981-2012

0.65

0.87

1.54 1981-2012

0.20

0.81

F=5.191.31 1992-2002 P=0.0061

0.71

0.80

1.75 1981-2012

0.19

0.82

F=2.151.27 1992-2012 P=0.1061

0.70

0.81

1.5 1981-2012

0.25

0.78

F=2.961.29 1992-2012 P=0.0406

0.72

0.79

1.68 1981-2012

CPI
EQ1

F
1.88

EQ2

1.25

5.18

1.57 0.3064

0.2907

4.22

3.97

3.03

2.04

0.41450.42890.0443

4.55

5.28

5.31

0.94

1.85 0.2864
4.01
EQ3

0.2754 0.1564

3.60

2.84

1.03 0.3552

0.40110.0075

4.84

5.27

2.36

0.92 0.2852

0.29620.0051

1.66
0.61
EQ4

Chow test

3.09

1.04 0.3562
1.62

1.03

3.00

0.44

0.4011 0.0019

4.49

5.17

1.54 0.4410

0.41670.04

0.0076
2.09

PGDP
EQ1

5.13

5.73

4.29

0.14 0.1909

0.2561

0.78
EQ2

0.07

0.06

0.50 0.3133

0.40070.1321

3.60

4.48

2.52

0.46 0.1685

0.23890.1749
0.88

1.00

EQ3

EQ4

0.75

1.61

1.87

0.10

0.31740.37660.0091

0.14

3.58

4.10

2.36

2.63

0.09990.27960.0220

1.42

0.88

2.30

0.30

0.26700.3786

0.09540.0060

1.55

0.41

2.93

4.25

1.66

1.44

Note: 1. All variables are measured in percent.
2. R 2 is coefcient of determination adjusted for degrees of freedom, s is standard deviation and d is DurbinWatson ratio.
3. Figures under coefcients are t-values.

Prolonged Deation and Structural Change in the Labor Market in Japan

37

explanatory variables except money supply in equation 4 for CPI. Also, most coefcientsare statistically signicant at the 5% level. The money supply is not signicant in both the
CPI and PGDP equations. This is because of various roundabout transmission routes ofmonetary policy. As shown in Table 2, we also estimated all equations except equation 4over the period 92-12 which includes both 92-97 and 98-12, and conducted a Chow
breakpoint test between the periods 81-91 and 92-12. We found evidence of structuralchange at the 5% signicance level for equation 1 of CPI and nearly so for equation 2 of
CPI, as F-statistics and P-values in the last column of Table 2 indicate. As far as PGDP isconcerned, structural change is noticed in equations 1 and 3. We could not obtain statistically
better estimation results for the latter period than for the entire period. All in all, the GDP
gap and the unit labor cost inuenced the CPI most signicantly in Japan. The exchange
rate has a statistically signicant although not so inuential impact on the CPI, but we could
not nd empirically that the money supply was a signicant factor in general pricedetermination. This would be because the BOJ’s monetary policy did not control money
supply directly, and it was endogenously determined in the past.

3. Okun’s Law
We estimated Okun’s law which shows a macro-economic relationship between
unemployment and output. According to previous studies in the literature of Japan’s
economy, there exists a stable relationship between the rst differences of unemployment
rate(Δut =ut-ut-1) and growth-rate(gt): Δut =a + bgt. Based on this, we estimated Okun’s
equations over various periods and tested structural change using the Chow test, andF-statistics ant P-values are shown in the last column of Table 3. The Chow test gives
evidence of the structural changes of Okun’s law between the periods 81-91 and 92-12, andalso between the periods 81-97 and 98-12.
The second to last column shows values of growth-rate required for maintaining
unemployment at the previous year level for each sample period. Take equation 5 forexample. 1.4% growth is needed to keep unemployment at the previous year level for theperiod 98-12. Naturally, a comparison between equations 4 and 5 implies that the lower the
average unemployment for a sample period, the higher the growth-rate required to keep it.
One of the distinctive features in the Japanese labor market is a steady increase inirregular workers such as part-timers and dispatched temporary workers since the middleof the 1980s due to deregulation in the labor market. The ratio of irregular employees tototal employees rose year by year consecutively, from 15.3% in 1984 to 35.2% in 2012.
When we included the irregular worker ratio in the explanatory variables in the Okun
equations, therefore, we obtained equations 8 and 9 which tted the observation data better
than the other equations. The estimated coefcients of the growth-rate are almost the samein comparison with equations 3 and 9. That is to say, the inclusion of irregular workers in
the equations does not change the impact of the growth-rate on unemployment. On the one
hand, an increase in the irregular worker ratio reduces the growth-rate required to keep
unemployment unchanged.

38

The Journal of Econometric Study of Northeast Asia

Table 3 Okun’s Law
Δu
EQ1

ratio ofConstant Growthirregular(a)rate (b)workers0.2847 0.0885
4.39

EQ2
EQ3
EQ4

4.55

R2

s

d

0.83780.43

0.27

0.94 1981-2012

3.2

0.39

0.18

2.04 1981-1991

4.6

0.57

0.27

0.85 1992-2012

1.9

0.44

0.17

1.64 1981-1997

4.5

0.64

0.29

1.04 1998-2012

1.4

0.52

0.17

1.43 1992-1999

3.3

0.60

0.28

1.09 2000-2012

1.1

0.71

0.20

1.26 1984-2012

0.68

0.23

1.28 1992-2012

2.66

2.72

0.26630.14350.8524

4.00

4.81

3.03

0.3458 0.0775
4.17

3.65

EQ5

0.21410.15510.8072

2.65

4.74

2.68

EQ6

0.4173 0.1273

EQ7
EQ8
EQ9

2.91

0.1550 0.1408
1.74

a/(-b)

2.98

0.5068 0.1099

6.28

SampleperiodDUM

0.73954.10

1.0755 0.1294

2.46
0.02930.6256

5.71

7.85

4.40

2.96

0.99220.14310.0261

0.6620

3.57

5.55

2.67

2.61

Chow test｝｝｝

F=4.39P=0.0041
F=6.80P=0.0041
F=2.96

P=0.0807

Note: 1. Growth-rate and ratio of irregular workers are measured in percent.
2. R2 is coefcient of determination adjusted for degrees of freedom, s is standard deviation and d is DurbinWatson ratio.
3. DUM is a dummmy variable for data irregularity, taking value 1 for 2011 and 0 otherwise.
4. Figures under coefcients are t-values.

4. Phillips Curve
The scatter in the rate of increase in wages and unemployment over the period 81-12
is shown graphically in Figure 2. We can observe a gradual downward sloping relationshipbetween two variables for the period 81-91. On the other hand, we can see an almost
horizontally stretching relationship around the 0.9% rate of increase in wages for the period
Figure 2 Unemployment and Changes in Wage

Prolonged Deation and Structural Change in the Labor Market in Japan

39

Table 4 Phillips Curve
Wage Constant Unemployment
(b)
rate
(a)
EQ1
EQ2

1.72812.3693

9.88

8.62

1.89

12.2822 3.4430

EQ5

4.03

3.8045 0.9300

0.7602 1.8974

3.24

3.46

3.1409 0.3864

0.73841.03
EQ6

2.8993

4.54

3.21

Sampleperiod

s

d

0.74

1.19

0.93 1981-2012

4.2

0.51

0.84

1.49 1981-1991

3.6

0.70

0.68

1.64 1992-2012

3.8

0.81

1.02

0.89 1981-2012

0.79

0.55

1.95 1981-1991

0.68

0.70

1.73 1992-2012

1.76

0.36

3.57

3.2414 0.8414

0.09262.9797

0.30

3.79

2.72

R2

a/( b)

3.39

2.9610 0.7768
4.13

EQ4

DUM

7.2827

4.89
EQ3

CPI

3.03

Chow test｝

F=24.38
P=0.0000｝

F=14.03
P=0.0000

Note: 1. CPI denotes the rate of increase in CPI. u and CPI are measured in percent.
2. R2 is coefcient of determination adjusted for degrees of freedom, s is standard deviation and d is DurbinWatson ratio.
3. DUM is a Rehman shock dummmy variable, taking value 1 for 2009 and 0 otherwise.
4. Figures under coefcients are t-values.

92-97, and a slightly declining relationship for the period 98-12. We estimated both ordinary
and augmented Phillips curves over the entire and two sub-periods based on the above
mentioned observation. The estimated Phillips equations are listed in Table 4. We also
tested structural change using the Chow test, which is presented in its last column. BothPhillips curves changed structurally between the periods 81-91 and 92-12. But we could not
get satisfactory estimates of coefcients for the augmented Phillips curves for the period
92-12. The second to last column for equations 1 to 3 shows the values of unemployment at
which wages do not change on average over the period. Take equation 3 for example. If theunemployment is 3.8%, wages will not change. If the unemployment comes down to 3.0%,
wages will increase at the rate of about 0.6%.

5. Concluding Remarks
Taking into account the fact that the CPI in Japan has never increased at the rate ofmore than 1.7% since 1992 and decreased in most years, it seems to be an enormous
challenge to achieve the price stability target of 2% over the next two years or so. Let us try
to make two rough experimental calculations with our estimated equations. Since structuralchange is detected for macro-economic relationships in the labor market, the estimatedregressions over the period 92-12 are basically used in the following calculation.
Case 1: We assume values for wages, the unit labor cost and the GDP gap to be 3.0%,
0.6% and 0.6% respectively, referring to actual economic situations around the middle ofthe 1980s. Equation 1 of CPI for the period 92-12 gives us a value of 1.6% for the rate ofincrease in the CPI. In order for wages to increase at the assumed rate of 3.0%, unemployment
has to be 2.5% based on equation 1 of the Phillips curve2 . We have to reduce unemployment
2
Since the actual rates of increase in wages is less than 1.3% in the period 92-12, we use equation 1 of the Phillips
curve.

40

The Journal of Econometric Study of Northeast Asia

by 1.8 percentage points from 4.3% in 2012 to attain the unemployment of 2.5%. According
to equation 3 of Okun’s law, a growth of 14.4% will be required cumulatively. That means
we need 2% growth for about seven years, or 3% growth for about ve years. Consequently,
this economic growth situation will bring down the GDP gap to less than 1%.
Case 2: Actually, real GDP grew at the rate of 9.5% over the ve years from 2003 to
2007. We therefore assume Japan’s economy will expand at the rate of 10% cumulatively
from 2013 on; that is to say, 2% annual growth for ve years, or 2.5% for four years, and
the GDP gap is assumed to be reduced by 3.8 percentage points from 5.9% in 2012 to 2.1%,
just like over the period 03-07. Based on equation 3 of Okun’s law, unemployment will be
reduced by 1.2 percentage points from 4.3% in 2012 to 3.1%. Substituting 3.1% for theunemployment of equation 1 of the Phillips curve, we get 1.9% for the rate of increase inwages, which is assumed to lead to the 0.5% increase in unit labor cost. As a result, we
obtain a value of 1% for the rate of increase in the CPI.
The above two cases tell us that Japan’s economy require a steady long-term economic
growth of more than 2% to overcome the deep-rooted prolonged deation, although theprice target of 2% will not be reached.
The BOJ adopted a 2% ination targeting monetary policy in January 2013, in April
2013 introduced “Quantitative and Qualitative Monetary Policy Easing”, and changed themain operating target for monetary market operation from an overnight call rate to amonetary base in order to achieve the ination target of 2%. In particular, the BOJ’s newmonetary policy aims to work on people’s expectations. The effects of Abenomics including
the drastic change in the BOJ’s monetary policy to overcome deation remain to be seen.
References
Bosworth, Derek, Peter Dawkins and Thorsten Stromback, (1996), The Economics of the Labour Market,
Longman.
Cabinet ofce, Government of Japan, (each year), Annual Report on the Japanese Economy and Public
Finance., [in Japanese].
Kuribayashi, Sei, (2013), “Japanese Labor Market under Prolonged Deation”, The Journal of Economics,(Chuo University), Vol. 53 No. 5·6, pp. 79-118, [in Japanese].
Kuribayashi, Sei, (2010), “Macro Economic Policy over Prolonged Deation in Japan”, Iijima, Ookuni,
etal, Institutional Reform and Economic Policy, (Chuo University), pp. 3-44, [in Japanese].
Kuribayashi, Sei, (2000), “Unemployment and Employment in Japan”, The Journal of Economics, (Chuo
University), Vol. 40 No. 5·6, pp. 325-362, [in Japanese].
Ministry of Health, Labour and Welfare, (each year), White Paper on the Labour Economy, [in Japanese].

The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 201441

The Macroeconomic Effectiveness of Resilience Investment in
the Context of Earthquake Risk
Takeru Mori*, Yusuke Kanda**

Abstract
Many people fear that Japan will continue to experience massive earthquakes, andmany believe that the Japanese economy is experiencing deation. Public investment isimportant for both reducing and preventing damage from natural disasters. Some view
such investment as a countermeasure that will not only increase the resilience withwhich Japan deals with disasters but also facilitates the nation’s recovery from deation.
Others have voiced concern that public investment will detract from the economic
efciency of Japan. Taking into account the probability of earthquake occurrence andextent of damage, this study analyzed the macroeconomic effects of public investment
and examined whether public investment could increase the resiliency and economic
efciency of Japan.

K EYWORDS : P ublic investment, Great earthquake, Economic efciency, Resilience
JEL Classication: H20, H61, H68

1. Introduction
Japan faces the constant risk of major earthquakes, and predictions of these events,
such as the Capital earthquake and Nankai Trough earthquake, have been made. The
Central Disaster Prevention Council estimated as many as 320,000 fatalities from the
Nankai Trough earthquake and as many as 11,000 deaths from the Capital earthquake(Japanese Cabinet Ofce, 2009). Another model predicted much more damage from the
Capital earthquake, estimating the number of deaths at 150,000 (Nankai Trough Earthquake
Study Measures Working Group, 2012). In a 1988 report, the Central Disaster Prevention
Council estimated the probability of the occurrence of a 7-magnitude great earthquake
within the next 30 years at 70% for a Capital earthquake and at 88% for a Tokai earthquake(Central Disaster Management Council Japan, 1988). Given the increased probability of amajor earthquake, public investment in the country’s infrastructure (e.g., maintenance ofthe transportation system and reinforcement of the seismic design of buildings) has recently
emerged as a very important factor in disaster prevention and mitigation. Thus, a system fordeveloping resilience to minimize the damage from a natural disaster through accurate risk
prediction and the construction of public works is needed. Issues related to economic
growth can also be considered within this framework.
“Resilience” is an important concept when considering the future economic growth ofJapan. Indeed, without a system to enable resilient responses to local and global risk factors,* Graduate School of Engineering, Kyoto University, Japan. E-mail: mori@trans.kuciv.kyoto-u.ac.jp** Graduate School of Engineering, Kyoto University, Japan. E-mail: kanda@trans.kuciv.kyoto-u.ac.jp

42

The Journal of Econometric Study of Northeast Asia

neither governments nor private companies can grow in the context of the current highly
competitive environment. Resilience has emerged repeatedly as an important topic related
to the domestic and global economy.
However, others believe that the notion of resilience is antithetical to economic
rationality and effectiveness. According to this line of thinking, if limited funding is
allocated to the development of resilience in the context of the risk of earthquakes, theresulting uncertainty would decrease the competitiveness of nations and businesses. In
reality, risk is present in all economic situations. Therefore it is possible to manage risk, andrisk is necessary for economic growth.
Although some investment is needed to develop resilience, overinvestment leads to adecrease in competitiveness. Therefore, the level of investment should correspond to therisk of disaster. Great risk will require heavy investment, whereas less serious risk might
permit less investment. Owing to Japan’s high risk for a major earthquake, public investment
is necessary to maintain resilience in the face of this kind of future disaster. Public
investment in resilience may be regarded as “resilience investment.”
In this study, we investigated the appropriate level of resilience investment in thecontext of economic growth targets and the occurrence probability of damage from a major
earthquake. Additionally, we developed models based on the macro-economic simulation
model used for analysis by the Japanese government. Finally, we examined the level ofresilience that would reduce the risk to Japan’s macroeconomy and ascertained the level ofresilience appropriate for the sustainable development of the Japanese economy.

2. Methodology
2.1 Scenario Analysis
To demonstrate the macroeconomic effect of public investment, we employed scenario
analysis to compare the occurrence with the absence of an earthquake. This analysis also
considered macroeconomic simulation models in which an investment in resilience was or
was not made to measure resilience effects.
We estimated the potential effect of two disasters, a Capital earthquake and a Nankai
Trough earthquake. As shown in Table 1, the damage from each disaster was measured interms of a variety of categories and classied under two divisions. “Direct damage” wasmeasured in terms of numbers of dead and injured, disruption of the infrastructure(including homes, roads, railways, the water supply, and sewage systems), and damage toprivate facilities. “Indirect damage” included such factors as decreases in exports and rapid
increases in imports due to the demand for materials and alternative energy. Some of thepredicted values of damage presented in Table 1 were based on the extant literature (marked
with asterisks), whereas other values were estimated in this study. Estimation of indirect
damage requires an appropriate period of observation following a disaster; this period
consists of the time until the affected area returns to a normal level of economic activity.
“Duration of damage” was also considered indirect damage and is presented in Table 2.
The projected magnitude of the major earthquake in these scenarios is presented as“an average value obtained from the trend of past earthquakes,” and the probability ofThe Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

43

Table 1 Estimated Damage from Major Earthquakes&DSLWDO1DQNDL7URXJK*UHDW (DVW -DSDQ(DUWKTXDNH(DUWKTXDNH(DUWKTXDNH

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8WLOL]DWLRQUDWH GHFOLQH * As estimates have not been published, estimates in this study were based on previous studies (see Expert
Committee for Capital Earthquake, 2005; Nankai Trough Earthquake Study Measures Working Group, 2012,2013; Kansai Institute for Social and Economic Research, 2011; Foundation for Real Estate Information Network
for East Japan, 2012; Japanese Cabinet Ofce 2005, 2012, and 2013; The Bank of Tokyo-Mitsubishi UFJ, 2011;
Japanese Ministry of Internal Affairs and Communications, Japanese Ministry of Land, Infrastructure andTransport, 2010; Mizuho Research Institute, 2011).
** We assumed that a nuclear power plant would not be located in areas affected by the Great East Japan
Earthquake and hypothesized that alternative energy demands would not occur.
*** Based on data from the Japanese government, private sector capital investment was taken to be proportional
to the rate of operation (Japanese Cabinet Ofce, 2011).

occurrence is also described as “about 88% within 30 years” (Japanese Cabinet Ofce,2012). Therefore, although the actual size of an earthquake cannot be determined, ourestimates are probably highly accurate. Our simulations valued damages based primarily
on mean values from Table 1, and we used the probability distribution of occurrence tomeasure stochastic changes in earthquake size. Because the scenarios must also consider
maximal damages, the use of only mean values is not sufcient (i.e., reports estimate
150,000 deaths in the event of a Capital earthquake; mean values lead to estimates of
110,000 deaths). Additionally, the initial occurrence of the two earthquakes (Capital
earthquake and Nankai Trough earthquake) were set to vary according to a probability
density function (see Figure 1) obtained from published occurrence probability gures. The
probability gures were derived from a Poisson process (The Headquarters for Earthquake
Research Promotion, 2010). We identied two scenarios: one based on “A Resilience planfor Japan,” published by the Japanese government, and the other on a government plan to

44

The Journal of Econometric Study of Northeast Asia

invest 200 billion yen over 10 years for a resilience system. We examined the following two
scenarios: (1) an investment of 20 trillion yen per year over a 10-year period and (2) an
investment of half that amount.
In this study, we assumed that damages, both direct and indirect, would be reduced byresilience investment. Improvements in the earthquake-proof infrastructure (e.g., roads,
railways, and buildings) could decrease loss of life, disruptions to the trafc network, andTable 2 Duration of Indirect Damage,QGH['XUDWLRQ%DVLV([SRUWVRI\HDUVā'DWDRQWKHSHULRGRIUHFRYHU\IURPWKHVKRFNVEHIRUHEXVLQHVVDFWLYLWLHVJRRGVDQGVHUYLFHVUHWXUQHGWREDVHOLQHVKRZDGHFUHDVHLQFRUSRUDWHSURILWVRIDERXWGXULQJWKH\HDUVDIWHUDVXSSO\FKDLQGLVUXSWLRQFRPSDUHGZLWKWKH
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EHHQLQFUHDVLQJVLQFH1RYHPEHUDQGLWZLOOUHDFKDOHYHOVLPLODU
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XWLOL]DWLRQLVH[SHFWHGWRFRQWLQXHIRU\HDUV****: As estimates have not been published, our estimates were based on previous studies (see Hendricks andSinghal, 2005; Japanese Cabinet Ofce, 2009; Japanese Ministry of Health, Labour and Welfare, 2012).

Figure 1 Probability Density Function for the Initial Occurrence of an Earthquake
Capital Earthquake

Nankai Trough Earthquake

The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

Figure 2 Overview of the Macroeconometric Simulation Model

Table 3 Assumed Damages from Great Earthquakes after Resilience Investment,QGH[

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45

46

The Journal of Econometric Study of Northeast Asia

losses of social capital, housing, and the private sector from res and tsunamis. If direct
damage can be minimized, it may also be possible to decrease indirect damage. We
referenced some predicted values, such as anticipated deaths from a Nankai Troughearthquake under a resilience system, and estimated other values, including direct andindirect damages, by anticipating the conditions that would obtain under a resilienceoriented system. Additionally, we created a hypothetical scenario involving a nuclear power
plant that was severely damaged by a Great East earthquake but that operated continually(Table 3).
2.2 Modeling
We simulated a macroeconomic model to measure resilience and effectiveness under
the aforementioned assumptions. The macroeconomic model used in this study was basedon a model adopted, and later revised, by the Japanese government (Hino et al., 2012) tocompute the effects of improved infrastructure on the nation’s macroeconomy. The model
demonstrates that domestic demand is expanded by resilience investment and thataccessibility is increased by the development of transportation networks. As a consequence,
private consumption is improved and GDP ultimately increases. Conversely, it isdemonstrable that an increased unemployment rate, decreased supply due a reduced
capacity utilization rate, and decreased demand due to reduced temporary private
consumption, results in a reduction in the GDP.
Investment in public works increases ination by a factor of 1.65 and deation by afactor of 5.26 (Fujii, 2012). The aforementioned macroeconometric model estimated asimilar effect on ination and deation. However, our advanced model accounts for theination/deation effect from the GDP gap between supply and demand and the differencein the multiplier effect. We measured the effect of resilience investment by focusing onchanges in the GDP, tax revenue, and labor population using the advanced macroeconomic
simulation model. We also considered the impact of resilience investment on Japan’s current
deationary and inationary trends; in a hypothetical situation, there is no distinction
between ination and deation.
2.3 Estimation Results of Macroeconomic Model
The macroeconomic model proposed in this research is composed of many sub-models.
In this paper, the authors present some of the estimation results for important economic
variables related to macroeconomic simulation. These results are shown in Tables 4, 5, 6, 7,
and 8. First, Table 4 shows potential GDP as a production function. As adjusted R2 is high,the estimated results represent a sufciently good t. Second, Table 5 shows private nal
consumption expenditures, represented of the consumption function, and this model is alsoa sufciently good t. Table 6 presents the estimated results related to the labor force. The
estimated results of models of indirect and current taxes are show in Tables 7 and 8. Tax
revenue was dened as the sum of these taxes. In this study, current taxes included tax
income and wealth, and indirect taxes were based on the SNA data provided by the JapaneseCabinet Ofce; however, social insurance premiums were not included in this model.

The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

47

Table 4 Estimated Parameters of the Potential GDP Model
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The Journal of Econometric Study of Northeast Asia

3. Analysis
3.1 Investment of 200 trillion yen
As shown in Figure 3, when 200 trillion yen is invested for resilience, there is no GDP
uctuation over the subsequent 10 years [Case A: no earthquake and no investment].
However, there is a risk of earthquake in a real situation. If an earthquake occurs, the GDP
decreases. As seen in the third case [Case C: average earthquake and no investment], theGDP is reduced greatly after 2014, to 470 trillion yen, because of the possibility that a
Nankai Trough earthquake will occur in 2013 or 2014. The difference between cases A and
C is the expected value of the economic damage caused by the earthquake. This difference
is approximately 60 trillion yen per year, with a cumulative cost of approximately 510
trillion yen after 10 years as shown in Table 9. When calculated per capita, individual
income will have been reduced by approximately 4 million yen over a period of 10 years.
Our analysis also found that approximately 550 trillion yen would be lost per capita, with
GDP increasing to 710 trillion yen. It is estimated that it may require ~23 years for the line
separating cases A and C to be crossed. This would mean a return of the GDP to its originallevel following a cumulative decline of 720 trillion yen and a loss of tax revenue amounting
to approximately 50 trillion yen. Additionally, it is possible that the labor force and theavailable xed capital would be damaged by the disaster, which may decrease the GDP,
leading to ination, and thereby partially suppress the multiplier effect. However, in thisstudy, the effects of the disaster on both the demand and the supply sides were calculated
based on data on the economic damage related to the Great East Japan Earthquake, and soon. Thus, the demand and supply GDPs estimated in this paper may not be realistic.
This analysis considered only an average earthquake; the possibility of more serious
damage by a major earthquake should also be considered. Therefore, a simulation with an
occurrence probability of approximately 10% damage, a “worst-case” scenario, was also
computed.
In the fth case [Case E: massive earthquake and no investment], the GDP will
decline, nally reaching 450 trillion yen. The total reduction in the GDP over 10 years will
equal 740 trillion yen, a 6 million yen loss in income per capita. Tax revenue losses over 10
Figure 3 Estimated GDP (with a resilience investment of 200 trillion yen)

The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

49

Table 9 Macroeconomic Effects of Resilience Investment(with a resilience investment of 200 trillion yen)
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years will equal 85 trillion yen. Computed values are cumulative over one decade, but when
one considers that the damage will continue for 11 more years, the seriousness of such adisaster for the Japanese economy becomes more apparent.
Predictions indicate that the probability of a major earthquake is very high, lending
credence to predictions of signicant Japanese economic losses in the future (i.e., losses ofapproximately 4-6 million yen per person over 10 years).
This study also modeled the extent to which damage could be mitigated through thepromotion of resilience investment. The investment scenario considered involved 20 trillion
yen over a 10-year period. As mentioned above, the GDP loss caused by a major earthquake
would be approximately 60 trillion yen per year in the rst case. However, as presented inin Figure 3, the fourth case [Case D: average earthquake and investment of 200 trillionyen], which involved a 200 trillion yen investment in a resilience system, indicated that
economic growth would continue, that the economy would overcome the damage, and that
the GDP would increase by 600 trillion yen over the subsequent 10 years. This suggests that
resilience investment not only minimizes economic damage and lls the gaps associatedwith deation but also leads to economic growth. Thus, these data show that a resilience
investment of 200 trillion yen would result in economic growth in Japan as well as minimize
the damage caused by a major earthquake. The positive impact was calculated as 710
trillion yen over one decade and as approximately 1,870 trillion yen over 20 years when wecompared cases C and D. Table 11 presents a comparison of the ratio of tax revenues tonominal GDP with and without a resilience investment 10 years earlier. The data reveal a
3.3% improvement in the absence of a resilience investment. Additionally, Table 10
summarizes the multiplier effect of the resilience investment per year. These data indicatethat the maximum value of the multiplier given the risk of an earthquake is 4.25.
Our estimates also indicated that resilience investment would result in an increase intax revenues. Simulation results indicated that increased tax revenues would total 200
trillion yen after 13 years. The positive effects demonstrated by simulation analysis would
require an enormous investment on the part of the Japanese government. However, as thistax analysis shows, a 200 trillion yen nancial expenditure could be recouped within 13
years.
The maximum estimated damage resulting from a major earthquake would include
150,000 deaths in the case of a Capital earthquake and 320,000 deaths in the case of a

50

The Journal of Econometric Study of Northeast Asia

Table 10 Multiplier Effect of Investment per Year(with a resilience investment of 200 trillion yen)
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Table 11 Tax Revenue-to-nominal GDP Ratio 10 Years after Resilience Investment(with a resilience investment of 200 trillion yen)
1RL QYHVWPHQW,QYHVWPHQW
1R HDUWKTXDNH 






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

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

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Nankai Trough earthquake. Direct and indirect damages would also increase signicantly
as the scale of the earthquake increased.
Without resilience investment, we estimate that the GDP would decline by 70 trillion
yen over the 10 years following a maximum-scale earthquake. Additionally, a decline in tax
revenue of approximately 11 trillion yen, along with negative effects on national life andeconomic activity in Japan, would also be expected.
With a resilience investment of 200 trillion yen, the GDP would continue to gradually
increase at a level similar to that estimated for the economic activities following theoccurrence of an average-scale earthquake, even in the case of a maximum earthquake
event. The cumulative 10-year GDP would increase to nearly 800 trillion yen as comparedwith the case where no resilience investment occurred. Additionally, the tax revenue-tonominal GDP ratio would improve, reaching 2.3%, and the multiplier effect of the resilience
investment would increase to 4.79. Additionally, the difference in tax revenues would equal
nearly 120 trillion yen over 10 years, and the 200 trillion yen of resilience investment could
be recovered from the resulting increase in tax revenues after 15 years.
To summarize, a resilience investment of 200 trillion yen over 10 years would
minimize the damage caused by an earthquake, ll the deationary gap by expanding
domestic demand, and grow the Japanese economy, even in the event of a Capital earthquake
or Nankai Trough earthquake. Thus, an investment of 200 trillion yen can result in bothresilience and efciency in a deationary economy.

The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

51

3.2 Investment of 100 trillion yen
We next considered the case in which the government invests 100 trillion yen inresilience over a period of 10 years. The scale of the hypothetical earthquake was the sameas in the 200-trillion investment scenario. The simulation results are presented in Figures 4
and 5. When 100 trillion yen is invested for resilience, the trend in the GDP differs
signicantly from that in the 200-trillion investment model. The difference in the 10-year
cumulative GDP between the 100-trillion investment cases (Case B and D) and the noinvestment cases (Case A and C) equaled 280 trillion yen. A comparison of the differences
between cases F and B and between cases E and A revealed that the difference in the
cumulative 10-year GDP was ~420 trillion yen as shown in Table 12. The positive impactof a 100-trillion investment is 420 trillion yen in cases of an average earthquake (Case C
and D). However, when 200 trillion yen is spent for resilience, the positive impact on the
GDP was calculated to be 710 trillion yen over a decade. This indicates that an additional
investment of 100 trillion yen makes a signicant difference in the scale of the economic
impact.
Figure 4 Estimated GDP (with a resilience investment of 100 trillion yen)

Figure 5 Estimated GDP (with a resilience investment of 200 trillion yen, no deation)

52

The Journal of Econometric Study of Northeast Asia

Table 12 Macroeconomic Effects of Resilience Investment(with a resilience investment of 100 trillion yen)
Case

Resilience

DisasterInvestment

Cumulative GDP

Cumulative tax

Workforce afterfor 10 Years

revenue for 10 Years

10 years(trillion yen)(trillion yen)(ten thousand)Case ANo

No

5,313.8

677.0

6,404.1

Case BNo

100 trillion yen

5,483.5

717.1

6,449.7

Case C

Average ScaleNo

4,799.4

611.2

6,341.0

Case D

Average Scale

100 trillion yen

5,247.1

655.6

6,404.0

Case E

Huge ScaleNo

4,572.1

554.7

6,279.2

Case F

Huge Scale

100 trillion yen

5,164.5624.5

6,380.1

In each of the three cases (Case B, D and F) that assumed an investment of 100 trillion
yen, there was either no change or a small decline in the GDP trends, according to the scaleof earthquake. This result was notably different from that of the of 200-trillion investment
scenarios, which produced an increasing trend. Under conditions of a massive earthquake,the GDP might decline by 450 trillion yen, which would inuence the Japanesemacroeconomy. Additionally, Table 14 summarizes the tax revenue-to-nominal GDP ratio,
which would increase to 0.1%, and Table 13 summarizes the multiplier effect of resilience
investment per year. This result indicates that the maximum value of the multiplier underconditions of the risk of an earthquake would be 5.28.
Table 13 Multiplier Effect of Investment per Year(with a resilience investment of 100 trillion yen)
1RHDUWKTXDNH


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5HDO
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Table 14 Tax Revenue-to-nominal GDP Ratio 10 Years after Resilience Investment(with a resilience investment of 100 trillion yen)
1R HDUWKTXDNH $YHUDJH HDUWKTXDNH +XJHHDUWKTXDNH 

1RL QYHVWPHQW,QYHVWPHQW     The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

53

These results indicate that a resilience investment of 100 trillion yen would not be
sufcient to prevent deation in the event of a major earthquake. There was no change in
the GDP even in case B; over time, the GDP would decline due to decreases in investment,
especially in 2023, when the resilience investment ended. It would be impossible to cover
the deationary gap with an investment of 100 trillion yen, but such an investment would
not lead to economic growth. We projected that tax revenues would increase by approximately
40 trillion yen over 10 years; this is lower than the tax revenues in the case of a 200-trillion
investment, but is higher than it would be if no investment were made.
If a major earthquake were to hit the country when it was not emerging fromdeationary economic conditions, economic activities would be decreased by direct andindirect disaster damage and result in decreased demand and an increase in the deationary
gap. Ultimately, such a quake would probably lead to a decrease in the GDP.
To summarize, it would be difcult to achieve resilience and efciency following amajor earthquake with an investment of 100 trillion yen. Simulation results indicated that
this level of investment could negatively inuence Japanese nancial conditions.
3.3 Investment without Deation
In the previous section, we considered resilience investment in circumstances ofdeation and concluded that large-scale public investment could cover the deationary gap,
resulting in both resilience and efciency. We also simulated the macroeconomic effect ofresilience investment assuming that Japan was in a situation of ination without adeationary gap.
Again, we considered the case of a resilience investment of 200 trillion yen over 10
years (See Figure 5 and Table 15). Were a major earthquake to occur in a situation where
the government has promoted a base scenario corresponding to the third case [Case C:
average earthquake and no investment], the GDP would decline by 40 trillion yen with acumulative cost of 420 trillion yen after 10 years and of 520 trillion yen after 20 years. Thisis a serious result, but it is an acceptable risk when compared with the deationary model(510 trillion yen after 10 years and 710 trillion yen after 20 years). When compared withcase D (i.e., average earthquake and investment of 100 trillion yen), the positive impact wascalculated as 710 trillion yen over 10 years, and the maximum value of the multiplier of theinvestment would increase to 2.58. However, these effects are smaller than they would bewith deation (see Table 16).
Conversely, it is possible to reduce risk with resilience investment. Comparisons
between the fourth and second scenarios [Case D: average earthquake and investment of
200 trillion yen and case B: no earthquake and investment of 200 trillion yen] and between
the third and rst scenarios [Case C: average earthquake and no investment and case A: noearthquake and no investment] showed approximately 220 trillion yen cumulative over 10
years, and GDP of 21 trillion yen in 2023. This comparison demonstrates the remediationof damages to the macroeconomy when compared with the case in which no resilience
investment is made. Moreover, after 10 years, the GDP would be at a higher level than thecurrent GDP. Even when we considered a maximum damage situation, the GDP would
remain at the current level after 10 years.
However, when the case involving ination is compared with that involving deation,

54

The Journal of Econometric Study of Northeast Asia

we can see that the effect of resilience investment is signicantly smaller in the former, andthe trend in the GDP also differs greatly between the scenarios. If we promote resilience
investment when there is no deationary gap, the GDP will increase due to the expansionof domestic demand caused by 10 years of investing, but the GDP will decrease wheninvestment ends. Additionally, as seen in Figure 5, the growth of the GDP would decrease
due to “crowding out.” Private investment is also strong in the inationary case, where no
deationary gap exists. In a deationary environment, large-scale government funding
places nancing pressure on the private sector. This negates any economic stimulus effect,
Table 15 Macroeconomic Effects of Resilience Investment(with a resilience investment of 200 trillion yen, no deation):RUNIRUFHDIWHU

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WULOOLRQ\HQ 

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Table 16 Multiplier Effect of Investment per Year(with a resilience investment of 200 trillion yen, no deation)
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Table 17 Tax Revenue-to-nominal GDP Ratio 10 Years after Resilience Investment(with a resilience investment of 200 trillion yen, no deation)


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The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

55

even when the government makes a huge investment. This was observed only in thedeationary case; in such a scenario, the earthquake damage is minimized and, at the sametime, grows the Japanese economy. These effects do not exist under inationary conditions.
The simulation also indicated that tax revenues would also increase due to GDP
growth of 60 trillion yen over 10 years; there is a decline in the GDP after the investment
period ends, with an attendant decrease in tax revenues. Such decreased tax revenues would
make it difcult to recover an investment of 200 trillion yen. The basic trends when an
investment of 100 trillion yen is made under deationary conditions are similar to thosewhen an investment of 200 trillion yen is made, as shown in Figure 6 and Table 18, 19, 20.
However, as noted at the beginning of this section, economic conditions characterized
by the presence of ination and the absence of a deationary gap do not exist in reality. Thisvirtual case was presented to compare the macroeconomic effects of resilience investment
with those of deation.

Figure 6 Estimated GDP (with a resilience investment of 100 trillion yen, no deation)

Table 18 Macroeconomic Effects of Resilience Investment(with a resilience investment of 100 trillion yen, no deation)
&DVH



'LVDVWHU

5HVLOLHQFH
,QYHVWPHQW&XPXODWLYH*'3 &XPXODWLYHWD[IRU<HDUV:RUNIRUFHDIWHU

UHYHQXHIRU<HDUV \HDUVWULOOLRQ\HQ 

WULOOLRQ\HQ 

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56

The Journal of Econometric Study of Northeast Asia

Table 19 Multiplier Effect of Investment per Year(with a resilience investment of 100 trillion yen, no deation)
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

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Table 20 Tax Revenue-to-nominal GDP Ratio 10 Years after resilience investment(with a resilience investment of 100 trillion yen, no deation)
1R,QYHVWPHQW,QYHVWPHQW 

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

4. Conclusion
In this study, we theoretically and quantitatively examined the conditions that would
lead to economic resilience, efciency, and economic growth in the event of a futuredisaster. Using an advanced macroeconomic simulation model, we analyzed the effects ofresilience investments of 200 trillion and 100 trillion yen, simulating the conditions of aCapital earthquake and a Nankai Trough earthquake. Four main ndings were obtained
from this study.
First, using predictions published in a Japanese government report, we calculated that
the GDP would probably decrease to 60 trillion yen as a result of either a Capital earthquake
or Nankai Trough earthquake event. The 10-year cumulative value would decrease sharply
to 510 trillion yen. Notably, this estimated loss is a stochastic average value expected froma variety of situations including both the occurrence and the absence of an earthquake.
These results strongly indicate the need for preparation that views a future earthquake eventas the default condition.
Appropriate resilience investment related to the occurrence of an earthquake canminimize damage and also facilitate an escape from deationary conditions. Economic
growth can also be maintained in spite of a major earthquake event. An investment of 200
trillion yen for 10 years, will reduce damage to the macroeconomy and also act as aneffective economic stimulus. Our study estimated that this level of investment would
contribute 600 trillion yen to the GDP by 2025. However, the third nding of this study was

The Macroeconomic Effectiveness of Resilience Investment in the Context of Earthquake Risk

57

that an investment of 100 trillion yen would be insufcient to ll the deationary gap
caused by a disaster and would have only a minor economic stimulus effect.
We also considered a hypothetical situation where no deationary gap exists.
Simulations demonstrated that resilience in the context of disaster could be achieved
through investment, but that the economic stimulus effect would be limited by “crowding
out.” However, an investment of 100 trillion yen over a 10-year period could substantiallymitigate damage to the GDP. It is important to note that although the model parameters
were estimated based on real data, the numerical results described above were calculated
based on specic assumptions. Therefore, further study is needed to verify whether therequired level of investment depends on the extent of damage to the GDP.
By modifying the scale of investment to ll the deationary gap, it is possible not only
to build a resilience system but also to maximize efciency in the service of economic
growth.
Based on the scenarios described above, we conclude that assertions that “economic
efciency (rationality) and resilience conict with each other and that there is a trade-off is
between these two” are not valid under conditions of deation and in situations involving ahigh risk of an earthquake. In these situations, sufcient resilience investment can lead toefcient economic growth and the development of a system that protects the economy
against the impact of earthquakes. As a result of Japan’s deationary economic environment
and the probability of a major disaster, the nation faces signicant economic risk. Therefore,a policy of public investment for economic growth and resilience is strongly indicated.
Future work is needed to test and elaborate on the presuppositions of this study.
Additional work is also needed to determine the most effective levels of investment invarious regions. This study demonstrates that resilience investment can contribute tobuilding a robust national economy capable of withstanding risk and emerging fromdeation.

Acknowledgements
We are grateful for the advice of Prof. Satoshi Fujii of Kyoto University. We also wish to
thank Seiichi Hino of The Institute of Behavioral Science, for advice during the analysis
phase.
References
Expert Committee for Capital Earthquake (2005), Damage Estimation, Japanese Central Disaster
Prevention Council
Fujii, S. (2012), “Business Benet Analysis of Public Works by the Central Government in Deation,”
Infrastructure Planning Research Proceedings, 46
Hino, S., T. Momma, A. Koike, T. Nakano and S. Fujii (2012), “Keynesian Analysis of the Public
Investment Effect Under Conditions of Endogenous Ination-Deation,” Journal of JSCE

, F4, vol.

68, No. 4, pp. I_21-I_32.
Hendricks and Singhal (2005), “The Effect of Supply Chain Disruptions on Long-Term Shareholder
Value Protability, and Share Price Volatility,” Supply Chain Magazine.
Japanese Cabinet Ofce (2009), A Study on the Reconstruction Measures for the Big Earthquake
Japanese Cabinet Ofce (2011), Enhance the Power Essential for the Japanese Economy2011 Annual

58

The Journal of Econometric Study of Northeast Asia

Economic and Fiscal Report (economic and scal policy minister report).
Japanese Cabinet Ofce (2012), The Private Sector Capital Stock Bulletin.
Japanese Ministry of Health, Labor and Welfare (2012), An Analysis of the Labor Economy.
Japanese Ministry of Internal Affairs and Communications (2012), Family Income and Expenditure
Survey (household income and expenditure Edition).
Japanese Ministry of Land, Infrastructure and Transport (2010), Study of National Cargo Net (Logistics
Census).
Kansai Institute for Social and Economic Research (2011), Prediction of Stock Damage Costs of the Great
East Japan Earthquake.
Material from a meeting of the Japanese Central Disaster Management Council (1988), Japanese Central
Disaster Management Council.
Mizuho Research Institute (2011), Interpreting the Trade Balance after the Great Earthquake: Is Trade theDecit Transient? [This reference is missing information.]
Mori, T., and Kanda, Y. (2013), “Analysis of the Macroeconomic Effectiveness of Resilient Investment
Related to the Risk of the Great Earthquake,” Proceedings of Infrastructure Planning, JSCE, Vol.
47, CD-ROM.
Nankai Trough Earthquake Study Measures Working Group (2012), About the Estimated Damage of a
Nankai Trough Earthquake (1st report), Japanese Central Disaster Prevention Council.
Nankai Trough Earthquake Study Measures Working Group (2013), About the Estimated Damage of a
Nankai Trough Earthquake (2nd report), Japanese Central Disaster Prevention Council.
The Bank of Tokyo-Mitsubishi UFJ (2011), Part 2the Economic Impact of the Great East Japan
Earthquake: Analysis from the Demand Side.
The Foundation for Real Estate Information Network for East Japan, (2012), Trends in the Tokyo
Metropolitan Area Real Estate Market.
The Headquarters for Earthquake Research Promotion (2012), “National Seismic Hazard Maps in 2010,”
Guide and Commentary, p. 25.

The Journal of Econometric Study of Northeast Asia Vol. 9, No. 2, 201459

A Survey of the Arguments Concerning China’s GDP Statistics*
Jie Li**

Abstract
This paper examines the arguments concerning China’s GDP statistics and thethree retroactive revisions of GDP by the National Bureau of Statistics of China. The
survey of the past two decades shows the possibility that China’s ofcial GDP gures
have been underestimated conservatively rather than overestimated as the media havesuggested since 2000. This paper also considers the consistency between Gross Regional
Product and GDP in Japan and China.

K EYWORDS : China’s ofcial GDP, Gross Regional Product, System of National Accounts,
System of Material Product Balances
JEL classications: P24

1. Introduction
Since its economic reform in 1978, China has achieved high economic growth.
However, the transition to a socialist market economy from a planned economy has also
been a process of moving to a System of National Accounts (SNA hereafter) from a System
of Material Product Balances (MPS hereafter). Against this background, interest in China’s
GDP statistics is high, although distrust in these gures is also strong.
This paper examines the arguments concerning China’s GDP statistics. The topics
covered include the upward adjustments of 30% or more in China’s ofcial GDP by theWorld Bank in its publications from 1994 to 1998 and the upward adjustment of approximately
10% by Maddison (1998). The paper also assesses the various criticisms and commentaries
on China’s GDP statistics by scholars and the media in the United States, Europe, Japan,
and China since 2000. In addition, the rebuttal of some of these criticisms and adjustments
and the three retroactive revisions of GDP by the National Bureau of Statistics of China(NBS hereafter) are discussed. The presented survey shows that China’s GDP statistics may
have been underestimated conservatively rather than overestimated as the media suggest.
This paper also considers the consistency between the Gross Regional Product (GRP
hereafter) estimated by local governments and GDP estimated by the central governments
in Japan and China as well as the problems of estimating GDP based on the concept of MPS.

2. Upward Adjustments to China’s GDP by the World Bank between
1994 and 1998
The national income statistics provided by the MPS system in the planned economy* This paper is funded by the Grant-in-Aid for Scientic Research (C) (Project Number 23530247).
** Faculty of Economics, Saitama University, Japan. E-mail: lij@mail.saitama-u.ac.jp

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The Journal of Econometric Study of Northeast Asia

period in China were well known. Since the economic reform, however, because services
such as nance and insurance, real estate, communications and broadcasting, education,and research have grown rapidly, NBS has launched a study of GDP, which has been central
to the SNA system since the early 1980s. In order to overcome the defects in the MPS
system, which did not cover the production activities of non-physical services, NBS began
to estimate GDP as an index in 1985. In 1993, NBS abolished estimates based on the MPS
system and moved to the SNA system. This action was inuenced by the external changes
in the early 1990s in the former Soviet Union and in Eastern European countries that nolonger conformed to the MPS system as well as the internal changes made at the 14th Party
Congress, which set the introduction of market mechanisms as the reform target for China’s
economic system.
The World Bank (1991) report declared that there are shortcomings in the fundamental
concepts, survey scope, and survey methods of the provision of the National Accounts
Statistics in China. Specically, the fundamental concept is deeply rooted in MPS, thesurvey scope still focuses on material production, and the survey method provides traditional
administrative production reporting. In addition, the price system still shows many of thecharacteristics of a planned economy, while the prices of many products are under thegovernment’s control. Therefore, all these factors have led to the underestimation of GDP
statistics and the overestimation of economic growth.
Based on this report, the World Bank (1993) upwardly adjusted its 1992 GDP gurefor China by more than 30%1. This upward adjustment continued until 1998. In January
1999, China’s Ministry of Finance and NBS visited the World Bank and submitted a request
to cancel the adjustment to China’s GDP statistics. In March of the same year, the WorldBank visited Beijing to consult on this issue with NBS.
Table 1 shows the evaluations and adjustments to China’s GDP statistics by the WorldBank and the rebuttal of these adjustments by NBS2. The World Bank’s adjustments weremade from three aspects. First, an adjustment based on the inconsistencies in the data in
China’s statistical system (1.6% increase in GDP). Second, scope adjustments based on thebalance of input-output tables (11.7% increase in GDP). Third, combined valuation
adjustments owing to the impact of the price system (18.3% increase in GDP). For eachadjustment item, with the exception of some items such as housing services3, NBS counteredsequentially.

1

At the time, because there was no great difference between the GDP and GNP of China, the World Bank’s
adjustments were made based on GDP statistics.
2
The rebuttal by NBS is derived from the papers by Xu, a central member of the Chinese consultation team who
was later promoted to the national accounts manager of NBS. For example, Xu (1999a) was awarded China’s ‘9th
(2000) Sun Yefang Economic Science Paper Award’ and ‘5th (2000) National Statistics Scientic Research
excellent results (paper) rst prize’. The consultation history is shown in most detail in Xu (2000).
3
For the underestimation of China’s housing services, see Li (2013b, c).

A Survey of the Arguments Concerning China’s GDP Statistics

61

Table 1 Evaluations and Adjustments by the World Bank and the Rebuttal by NBS
Evaluation and Adjustments
by the World Bank
1. Consistency Adjustments
Item

Rebuttal by NBS(1)Households’
Grain Use

China’s statistical practices havegenerallyvaluedruralhouseholds’
consumption of grain produced on the farm at
prices lower than free-market prices. Its
assumption here results in a 20% increase in
the valuation of all grain consumption. The
resulting effect on household consumption
expenditure is an increase of 1.6% and theeffect on GDP is 0.8%.

According to the stipulation of
calculating China’s agriculture statistics in
1995, farmers’ use of their own grain should
be calculated according to the comprehensive
average selling price, namely the average
price of all kinds of grain either purchased by
the government or sold on the market. This
price reects the purchasing prices of thegovernment and market prices. In recent
years, bumper harvests in China have lowered
the market price of grain to such a degree thatthe government has had to set higher
purchasing prices in order to protect theinterests of farmers. Therefore, thecomprehensive average price cannot be lower
than market prices. The assumption of the low
prices of own-use grain does not tally with thecurrent situation; the 20% upward adjustment
will thus lead to an overestimation of GDP.

(2) Change inInventory

Change in inventory consists of output
that cannot be sold (at the price at which
output is calculated). As a result, change ininventory is larger than it would be underconsistent reporting. The report assumes thatchange in inventory is nearly one-third lower
than the reported gure, which results in a
1.6% downward adjustment to GDP.

In the late 1980s, a downward
adjustment to change in inventory was
acceptable. However, since the 14th Party
Congress, which targeted the introduction ofmarket mechanisms to reform China’s
economic system, many changes haveoccurred. The decision making of enterprises
is mainly dominated by market demand andprot maximization. Abandoning products or
selling at low prices happens less often than
before. Therefore, the adjustment rate is nolonger reasonable.

(3) Welfare
The World Bank assumes that 10% ofServices within the labour force in enterprises is engaged inEnterprises
providing these services, and that this labour
force shifts to marketized services, leading toa 1.6% increase in GDP.

In recent years, one of the mostsignicant components of enterprise reform
has been the marketizing of welfare services
in enterprises; the proportion of employees ofenterprises that engages in these services isdeclining gradually. Therefore, the assumed
10% is too high.

(4)
Depreciation
Charges

China tends to use relatively low
depreciation charges because it assumes long
lives for xed assets based more on physicalwear and tear than obsolescence. Therefore,
the World Bank assumes a 31% increase indepreciation charges. However, the charges
have no effect on GDP.

In administrative units and non-prot
institutions, an increase in depreciation would
lead directly to an increase in gross value
added. Therefore, the adjustments ofdepreciation inuence GDP.

(5)
Government
Subsidies forEnterprise
LossesChina’s statistical conventions record
this as a negative component in total GDP. The
World Bank recognizes that it should betreating those items as government purchasesof goods and services for distribution to theirtargeted recipients, and thus that this should
be a positive item included in the government’s
nal expenditure. As such, the government’s
nal expenditure increases by 7% and total
GDP rises by 0.8%.

No comments.

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The Journal of Econometric Study of Northeast Asia

2. Scope Adjustments(1)
Grain Output

The grain planted areas areunderreported by as much as one-tenth to onethird (compared with planted areas measured
by using satellite photos). At the same time,the yield estimates of the grain crops insampled areas may be too high. Combining
these two factors, it assumes a 10% upward
adjustment in grain output, and the effect on
GDP is 0.9%.

The data estimated from satellite
images include elds with an angle over 25
degrees, ood lands, irrigation canals andditches, and roads between elds. In practice,those elds should not be regarded as normal
planted areas. The difference from China’s
ofcial data is thus not as large as that
estimated by the World Bank.
Moreover, in addition to the survey ofgrain output through agriculture statistics, therural household survey also compiles a tableof the agriculture production of rural
households and a balance table of the grain ofrural households. These tables reect the
production of rural households, stock of grain
at the beginning of the year, income of grain
during the year, expenditure on grain during
the year, and stock of grain at the end of theyear. Based on the facts above, the data on
grain output tallies with the real situation.

(2)
Vegetable
OutputThe output value calculated does notreect the fact that output in physical terms
varies from period to period or that market
forces encourage production in nonpeak
seasons when prices are higher. Moreover,there is no accurate measure of the planted
area. Considering these factors, a 30% upward
adjustment for vegetable output is made.

In fact, the regular agriculture statisticsof China tend to overestimate agriculture
production. The results of the agriculture
census indicate that the output of meat in the1996 annual report was 22% higher than real
production. The overestimation of the amountof pigs, oxen, and sheep was 20.7%, 21.1%,
and 21.8%, respectively. This leads to theoverestimation of total output and the value
added of agriculture. Therefore, a downward
adjustment of GDP should be made.

(3)
Township andVillage
Industry

Industry output at or below the village
level seems to be moderately underreported
because such enterprises generally have fewer
nancial records and are not usually coveredby the regular statistical reporting system
directly managed by NBS. The rapid growth
in township and village industry and somedegree of tax evasion have also resulted inunderreporting. However, some local ofcials
tend to overreport output in order to embellishtheir accomplishments. Combining thesefactors, it assumes a 10% to 15% upward
adjustment in this area, with the effect on
GDP an increase of 0.6%.

The results of the third industrial census
of China show that the total output value of
township and village industrial enterprises(including township, village level, cooperative,
private and individual rural industrial
enterprises) from the regular statistics wasoverestimated by 1,800 billion yuan (1995).
This accounts for 40% of the total output
value of rural industry. This is contradictory
to the judgement of the World Bank; therefore,a downward rather than an upward adjustment
should be made.

(4)
Rural Services

The shortage of resources in rural areas
to supervise sample data collection, to ensure
adequate
incentives
forrespondent
participation, and to process the results meansthat it is conceivable that survey-based datareect as little as two-thirds of actual rural
householdservice-relatedtransactions.
According to this assumption, it assumes a
50% upward adjustment for human services(education, health, and social services) and a
60% upward adjustment for other services(transport, commerce, and entertainment).
The effect on GDP is a rise of 6.5%.

The judgement on China’s rural service
statistics and data adjustment are based on thestatistical situation in the late 1980s or early
1990s. However, the rst tertiary census
conducted from 1993 to 1995 surveyed allservices sectors, including rural services. At
the same time, upward adjustments were madeaccording to historical GDP data. In fact, theadjustment is even stronger than that made by
the World Bank.

A Survey of the Arguments Concerning China’s GDP Statistics(5)
HousingServices

Data on housing services suffer fromproblems of both scope and valuation. Urban
and rural housing services present concerns
over inadequate statistical system scope.
China estimates the value added of housing
services from data on square meters of
housing by using different categories and persquare meter rental-rate approximations basedon depreciation in the structures according toestimates of original construction costs. In
general, both construction cost estimates anddepreciation rates are low. Nor do surveys
cover the housing services of rural-registeredresidents that live in cities. The mostsignicant issue is the inadequate survey
coverage of rural housing areas. Beginningwith a rough estimate that the statistical
network of China captures from two-thirds tothree-quarters of all housing in China, theassumed adjustment is 40%, and the effect on
GDP is 1.5%.

63

No comments.

3. Valuation Adjustments
Because of China’s distorted pricesystem, rates of return to capital and land aremuch higher for industry than they are forother sectors. The highest rates of return are
for consumer manufacturing, while the lowest
are in services and the coal industry. Farming
also has relatively low rates of return to capitaland land. Further price reform in China would
alter this pattern of sector protability. The
World Bank aims to adjust the rates of returnto capital and land of each sector to become
close to the national average. The importanceof the textiles industry in China’s foreign trademeans that the price change for textiles isassumed to be zero. The prices for housing
services and other real estate are upwardly
adjusted and the prices for consumer
manufacturing other than textiles downwardly
adjusted. Based on the 1987 input-output
table, these adjustments lead to an 18.3%
upward adjustment of GDP.

Since the beginning of the 1990s, China
has targeted establishing a socialist market
price mechanism. Breakthrough was made in
price reforms, and the distorted price structure
has clearly improved. For instance, from 1990
to 1997, prices in the services sector and coalindustry, which are regarded to have thelowest rates of return to land and capital by theWorld Bank, increased by 222% and 206%,
respectively.
Pricesin
consumermanufacturing and the textiles industry,
which are regarded to have the highest rates ofreturn to land and capital, only increased by
96.8% and 60.3%, respectively.
Moreover, according to an international
price comparison of 33 representative
commodities conducted by a related ministry,in November 1998, the prices for 22 kinds of
selected commodities are higher than those on
the world market accounting for 69%.
Tremendous changes have been made in
price mechanism, price scale, and pricestructure. If the World Bank still relies on the1987 input-output table to make priceadjustments to various sectors, it will also leadto the overestimation of GDP.

Note: This table is based on World Bank (1993) and Xu (1999a, 1999b, 2000).

64

The Journal of Econometric Study of Northeast Asia

Table 2 China’s GNP per capita according to the World Bank (in U.S. dollars)
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Note: Data on 1998 is the expected publication gure before consultation.
Source: Xu (2000).

In addition to the consultation with NBS, the program was prepared by NBS for theWorld Bank mission, visiting poor areas in Henan Province to consider living standards
such as health and education levels. Through this consultation and consideration, the WorldBank concluded that the basis for adjusting GDP no longer existed. It indicated that it would
follow a typical method for releasing China’s GNP per capita in its publications by directly
using China’s ofcial data and that it would no longer make any adjustments. After thisconsultation, the World Bank also used China’s ofcial data to amend its previously adjusted
gures of China’s GNP per capita.
Table 2 compares China’s GNP per capita gures published by the World Bank before
and after the consultation. It shows that until 1998, the World Bank upwardly adjusted
China’s GDP by more than 30%, whereas it estimated GDP in subsequent years by relying
on the adjusted GDP in 1992 and the ofcial growth rates of GDP. Note that the gures after
consultation are the ofcial GDP values converted into US dollars at the exchange rate.

3. A djustments to China’s GDP by Maddison (1998)
Maddison (1995) examined Chinese economic performance in general, while his later
book specialized in China’s economic growth (Maddison, 1998). In his study of China’s
GDP in 1952–1995, Maddison used the 1987 input-output table of China to adjust the value
added of some sectors4 and extrapolated these results to other years. According to thequantitative analysis, he concluded that China’s ofcial statistics overestimated economic
growth but underestimated GDP.
Moreover, during China’s economic reform and opening up period (1952–1977), its
annual average growth rate should have been 7.5% instead of the 9.9% quoted in the ofcial
statistics. Similarly, for the 1978–1995 period, the annual average growth rate should havebeen 4.4% rather than 6.1% and 5.6% instead of 7.6% for 1952 to 1995. In terms of sectors,
agricultural growth was underestimated, while the ination rates of industry and otherservices were underestimated; therefore, the growth rates of these sectors were
overestimated.
In terms of the volume of GDP in the base year (1987), the ofcial gure was 1,196.25
billion yuan compared with Maddison’s 1,319.28 billion yuan. The latter was 123.03 yuan
higher than the former, or 10.3% more. In terms of sectors, the value added of agricultural
was adjusted upward by 19% and that of other services by 33% compared with the ofcial
4

The estimates for mining and manufacturing provided by Maddison (1998) are based on the study of Wu (1997).

A Survey of the Arguments Concerning China’s GDP Statistics

65

statistics.
Interestingly, having thoroughly studied Maddison’s estimates of economic growth inChina from 1952 to 1995, Xu (1999b) pointed out that the upward adjustments in agriculturaland other services of Maddison were based on the misinterpretation of facts5. This nding
was similar to the rebuttal made for the upward adjustments in China’s GDP by the WorldBank.
To avoid China’s economic growth being perceived as a threat to the world by othercountries, Deng Xiaoping presented the famous foreign policy of ‘keeping a low prole’.
However, this guideline may have been applied to create GDP statistics.

4. Arguments concerning China’s GDP since 2000
Since 2000, the high growth in the global economy has led to China’s economy
growing stronger day by day. As the interest of the world in China’s economy increased,
China’s GDP statistics began to receive more attention. As NBS wanted, much of thecriticism of China’s GDP statistics was that they had been overestimated rather thanunderestimated as the World Bank said.
In the wake of the distrust of China’s GDP statistics in the mass media around theworld, Rawski (2001) questioned whether China’s economy had actually achieved a highgrowth rate (7–8% between 1998 and 2001 according to the ofcial statistics). The criticism
was based on inconsistencies in government statistics. Given the decrease in the employment
growth rate and energy consumption, he concluded that the true economic growth ratewould have been between 2% and 4% in this period. Rawski’s (2001) ndings were widely
published by major media outlets in the UK6 and the US7,8. In Japan, this topic was also
greatly taken up9,10.
The response to these criticisms also varied. Against Rawski’s (2001) nding of amismatch between energy consumption data and the economic growth rate, Ren (2002)
discussed the relationship between the energy consumption growth rate and economic
growth in Germany, the UK, the US, Japan, and South Korea. The author pointed out that
all these countries had also found mismatches between these two indices over time. He alsoquestioned Rawski’s (2001) way of setting the upper limit of economic growth by using the
growth rate of domestic air passenger trafc. Xu (2003) also discussed the ndings ofRawski (2001) and the many commentaries concerning it, and pointed out that Rawski(2001) had been mistaken for China’s statistical survey system.
Klein and Ozmucur (2002) conducted a principal component analysis on Chinese
statistical data on energy, iron and steel, transportation, communication, agriculture,exports and inputs, government spending, wages, and ination. The result of the analysis
showed that the change to the ofcial GDP and the change to the principal component werehighly consistent. The authors also pointed out that the determinants of economic growth
are diverse and that it is impossible to interpret the overall economic situation in a large
5

See Xu (1999b) for the view of NBS here.
See Economist (2002).
7
See Liu (2002).
8
See Balfour (2002).
9
See SAPIO editorial department (2002).
10
See Iwase (2002).
6

66

The Journal of Econometric Study of Northeast Asia

country such as China by examining individual indicators.
In Japan, after the ‘China distrust’ special feature, numerous articles were presented,
such as Onishi (2002), Zhang (2002), and Ogawa (2003a, b). In particular, 1998 became afocus. While the Chinese government had targeted a growth rate of 8% that year, the ofcial
statistics were slightly below 0.2%. Suspicion of statistics doctoring was discussed becauseof this.
As an extension of the discussion about the distrust of China’s GDP statistics, Kojima(2003) criticized the lack of consistency between the GDP statistics published by the central
and local governments. In addition, Takahashi (2004) questioned whether Chinese GDP
retrospective estimates verify the creation of China’s GDP based on statistical tests. He
pointed out that China’s GDP statistics have been analogized based on the total concept ofsocial production in the MPS and that these are fundamentally different from value added,
the essential feature of GDP in the SNA concept. Therefore, China’s GDP statistics are notcomparable with those of developed countries. These points are discussed further below.
4.1 Consistency between GDP and GRP
After Kojima (2003), this issue was taken up widely in the Japanese media11. The
criticisms may have stemmed from the difference between Japan and China in terms of therelationship between the central and regional statistics.
In Japan, the mechanism to create statistics is decentralized in each ministry in
response to the administrative needs at the central level, while the centralized mechanism
is strong between the central and regional levels.
By contrast, at the central level in China, the mechanism to create statistics in NBS iscentralized, but it is decentralized in the context of the central and regional levels. Although
regional statistics-creating organizations are susceptible to the involvement of region
governments, they have considerable independence from the central statistics organization12.
Detractors might imagine the process of creating GDP to be as follows: GRP in eachregion is estimated rst and then the GDP of the country is calculated as an aggregate
value. Therefore, the total value of GRP must match GDP. However, generally, the statisticalresources possessed in each region are relatively poor compared with the national level.
Therefore, few countries adopt this estimation method.
SNA creates GDP manually in order to cover the country’s overall economic position.
The description of regional accounts runs to only two pages at the end of the 800-plus-pagemanual. The difculty of regional accounts is captured in the following statement:‘Allocating the transactions of multi-regional units between various regions raises
more difculties. Even when these transactions are physically locatable, like output, it isnecessary to actually value intra-corporate ows between establishments located indifferent regions.’ (from 1993SNA, 19.91)
11

See Maie (2005). During this study, I conducted an email interview with the Mainichi Economist editorial
department and received the following response: The authenticity of Chinese GDP has long been pointed out. The
growth rates of January to September this year by region were published in late October. However, lower than
average growth rates for the country were only seen in Beijing and Shanghai; therefore, integrity problems havebeen noted.
12
On China’s statistical system, see Zhao and Yamada (1993).

A Survey of the Arguments Concerning China’s GDP Statistics

67

Further, the manual recommends creating regional accounts as follows:‘It is up to the countries themselves to devise their own regional accounts and statisticalindicators, taking into consideration their specic circumstances, data system and resources
which might be devoted to this work.’ (from 1993SNA, 19.96)
In Japan, GDP statistics are positioned as the most important government statistics
based on the Statistics Law, but GRP is calculated by each prefecture independently. ThisGDP expenditure approach is used as a reference value in the country, but because therelevant statistics cannot be supported in each province, GRP production has to be estimated.
For this process, GDP is rst estimated and then a proportion of GRP is estimated by using
the country-level value. Therefore, the GRP of each province is published a year later thanGDP.
Given the inconsistency between GDP in the government statistics and the GRP ofeach province estimated as independent businesses, after the ination problem of GRP in
the early 1990s, the Economic and Social Research Institute (Cabinet Ofce, Governmentof Japan) aimed to match GDP and the sum of the GRP as closely as possible and publish
this together13.
In China, GDP has been estimated from 1985, while the GRP of provinces and cities
has been estimated independent of GDP consistently. In other words, NBS estimates GDP,
whereas each region’s statistical organization estimates GRP. Further, the basic statistics
estimated are also different between the central government and local governments.
In China’s traditional reporting system, statistical tables aggregated the statisticaladministrations, but these were susceptible to illegal operations in each aggregation stage.
In 1990, NBS reviewed the aggregation method, and the important statistical tables began
to use the direct counting method. NBS also founded a research team that reported directly.
Table 3 shows the difference between GDP and the sum of GRP in China and Japan.
In both countries, the sum of GRP is larger than GDP for all years. The difference in China
is much larger than that in Japan, particularly in 2002 to 2004, as noted by Maie (2005) andKojima (2003).
As a way of assessing this problem with China’s GDP, the arguments that GRP has
been overestimated are common14. In other words, regional statistical organizations
overestimate the statistics for local governments. However, this is only one aspect. This
padding and the insufciency of data on the economic situation are severe issues. In fact, in2004 the difference between GDP and GRP reached 19.26%; however, based on the resultsof the rst economic census, China’s GDP was revised up to 16.81%, closing the differencewith GRP. Moreover, it has been pointed out that this upward revision is not enough,because there is a lot of leakage in the economic census15. The same expansion was offeredin 2008 in the second economic census.
Although the central government would like to ‘keep a low prole’, local governments
are obsessed about improving their performance, and thus they do not cooperate. This may
be the reason why it has been widely reported that only local statistics are overestimated.
13
For example, the exchange of views is encouraged between the heads of prefectural accounts at regular training
meetings. The proportion of the regional accounts is estimated by using that of the national accounts.
14
For recent discussions, see Xiao (2010) and Ding (2010).
15
See Chen (2006) and Maie (2006).

68

The Journal of Econometric Study of Northeast Asia

Table 3 Comparison of the difference between GDP and the sum of GRP (%)<HDU'LIIHUHQFHLQ-DSDQ'LIIHUHQFHLQ&KLQD

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Note: Difference is calculated as the sum of GRP/GDP – 1 × 100. In addition, in the economic censuses of 2004 and2008, no * means the number estimated before the economic census, while * means the number estimated after the
economic census.
Sources: For Japan, the National Accounts and Prefectural Accounts by the Economic and Social Research Institute.
For China, 2000 to 2004 estimates are based on China Statistical Yearbook (2005); 2004* to 2008 estimates are basedon China Statistical Yearbook (2009); and 2008* to 2010 estimates are based on China Statistical Yearbook (2011).

4.2 Estimates based on the MPS concept
According to Takahashi (2004), under SNA, activities traded in the market are dened
as production boundaries in the system. However, as MPS focuses on physical production,
production is limited to agriculture, forestry, sheries, industry, construction, and theservices directly related to the production of these goods. In other words, retail trade,wholesale trade, and freight transport are included in production, but nancial services,
health services, public utilities, and education are considered to be relocation16.
Until 1991, China used an indirect method based on the estimates of the MPS concept
to create GDP. For the value added of physical production, this involved adding theconsumption of xed capital to the net output of the MPS concept and deducting the input
of non-material services. That of non-physical production was estimated by using nancial
information on the government, tax revenue documents, and statistics for employment andsalaries17. Since 1992, however, this approach has shifted to a method of estimating GDP
directly from various statistics.
In 1971, the MPS system was approved by the United Nations as a suitable international
system that should be widely spread alongside SNA. Therefore, it is not possible to criticize
the estimation method of China that derived GDP from MPS estimates. However, theconcerns about the comparability of GDP statistics between developed countries and Chinaraised by Takahashi (2004) are similar to those about China’s GDP underestimate raised byWorld Bank (1991).
In summary, the argument concerning China’s GDP is that the media including
Chinese media is overwhelmingly of the belief that GDP is overestimated, whereas someexperts think the opposite.
16
17

See United Nations (1971).
For the estimation method of GDP in the initial introduction of SNA, see Yue (1989), Zhang (1993), and Xu (2000).

A Survey of the Arguments Concerning China’s GDP Statistics

69

5. The trend of retroactive amendments to China’s GDP
After the rst economic census in 2004, China’s GDP was revised upward by 16.8%.
This revision was reported widely in the Japanese media. Actually, China’s GDP has been
retroactively amended three times, as shown in Table 4. In Japan, the basis of GDP is theestimates of GDP under the expenditure approach; the difference from the estimates of
GDP under the production approach is then recorded as a statistical discrepancy. However,China publishes GDP estimates (expenditure approach) as a reference, whereas the basis of
GDP is the aggregate of the value added sector, which is estimated by using the income
approach or the production approach18.
Table 4 shows the retroactive amendments of China’s GDP production series. Chinabegan to estimate GDP in 1985 (historical data on 1978 to 1984 were estimated in 1986–
1988). After economic reform, the privately owned tertiary industry showed the highest
growth, such as the commercial, food and drinks, and transportation sectors. Since the
MPS system had been employed for a long time, the capture of statistics on the actual
situation, especially given the expansion and diversication in the tertiary industry, wasvery poor. As stated before, the underestimation of value added in the tertiary industry,
especially in the non-physical services industry, was often pointed out. In response, the rst
tertiary industry census was conducted in 1993, after which amendments to GDP weremade as far back as 1978. Based on the upward adjustment of 32% for the tertiary industry’svalue added, 1993 GDP was revised upward by 10%, as shown in Table 4. The adjustment
rate of 10% was considerable internationally, but still low compared with the upwardrevision of 32% by the World Bank (1993).
Thereafter, the rst economic census on all economic activities in the secondary andtertiary industries was performed in 2004 and secondary amendments to GDP were also
made. Furthermore, there were other changes, such as the census recorded the additional
paid services performed by housekeeping staff, introduced FISIM, and changed thedepreciation rate used for owner-occupied housing19. While the media were reporting that
China’s GDP had been overestimated20, the upward revision of 16.8% to 2004 GDP was
published in December 2005; 92.6% of the total revision was due to the tertiary industry.
Moreover, the ratio of tertiary industry GDP rose from 31.9% to 40.7%.
In spite of this signicant upward revision, the media claimed that the revised GDP
was still underestimated. For example, Maie (2006) stated the following: ‘Because small
and medium-sized entities have been developed, especially in China, there is a possibilityof leaking in the economic census. In addition, some SMEs not included in the scope of thestatistical survey, such as shops that have 60 or fewer employees, hotel of 40 people or less,transportation rms, and construction rms that are not equipped with a licence.’
Further, according to Chen (2006): ‘The expansion of GDP was increased by theservice industry, which was included in the economic census for the rst time.’ He added:‘There is still a lot of leakage in the survey. Although the range of services has expanded,it has not grasped the outdated idea. In addition, since the ratio of tertiary industry GDP in
India is 55%, the revised values may still be underestimated.’
In addition, the underestimation of housing services in China was also pointed out by
18

For the differences in the GDP estimation methods of Japan and China, see Li (2012).
For the content of the revision, see Xu (2006).
20
See Chen (2006).
19

70

The Journal of Econometric Study of Northeast Asia

Table 4 The adjustment of historical Chinese GDP data by NBS
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         $GMXVWPHQW$GMXVWPHQW
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Note: Adjustment rate is revised GDP/pre-revised GDP – 1 × 100.
Sources: *1. The 1978 to 1993 gures are based on China Statistical Yearbook (1994) ; 1994 to 2004 are based on
China Statistical Yearbook (1995); and 2005 to 2008 are based on China Statistical Yearbook (2009).*2. Revised
GDP after the rst tertiary industry census is based on China Statistical Yearbook (2005) .*3. Revised GDP after
the rst economic census is based on China Statistical Yearbook (2006) .*4. Revised GDP after the rst economic
census is based on China Statistical Yearbook (2010).

World Bank (1993). From 2% in the previous revision, the ratio of real estate industry value
added in GDP was revised up signicantly to 4.5%, but still much lower than the 12.2% inJapan. In terms of value, from 271.2 billion yuan in the previous revision, China’s realestate industry value added grew by 2.6 times to 717.4 billion yuan, but this is still less than
one-sixth of Japan21.
After the second economic census in 2008, third amendments to GDP were made,resulting in an increase of 4.4%. As with the earlier revisions, mostly because of the upwardrevision of the tertiary industry, the ratio of the tertiary industry value added GDP was
41.8%, representing a rise of 1.7% but still much lower than Japan (71%).
Table 5 shows the retroactive amendments of Japanese estimates of GDP (under the
expenditure approach). For data comparability, I limit comparison estimates to 1993 SNA
21

For the underestimation of housing services in China, see Li (2013a, b).

A Survey of the Arguments Concerning China’s GDP Statistics

71

Table 5 The adjustment of historical Japanese GDP data by the Economic and Social
Research Institute (in billions of yen)*'3EDVH

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
Note: Adjustment rate calculated as before.
Sources: *1. Based on Annual Report on National Accounts (2005); Economic and Social Research Institute, Cabinet
Ofce.*2. Based on Annual Report on National Accounts (2006).*3. Based on Annual Report on National Accounts(2011).*4. Based on Annual Report on National Accounts (2012); In view of the comparability to the GDP base 2000,
FISIM is excluded here.

only. The adjustment ratio of China is as large as that shown in Table 4, but that of Japan ismuch smaller than China, as shown in Table 5. The adjustment trends for Japan and China
are reversed: all downward in Japan, but all upward in China.

6. Conclusion
Based on the results of the adjustments and the arguments concerning China’s GDP
statistics, the underestimate of China’s GDP pointed out by the World Bank in the early
1990s was intrinsically correct. Moreover, the arguments that China’s GDP statistics were
overestimated, which have been widely covered in the media since 2000, were deployed in
the direction in which NBS wants as a result.
Many of the arguments concerning China’s GDP statistics are related to the economic
growth rate, which is calculated based on real GDP. In this paper, I focused on nominal
GDP (current year’s prices GDP). For the methodological issues with estimating China’s
real GDP, please see Li (2013d).
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