ISSN 1880-6988

THE JOURNAL OF ECONOMETRIC STUDY
OF NORTHEAST ASIA (JESNA)

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Vol. 9 No.1 September 2013
Econometric Modeling of Multi-Country and Multi-Sector
System for the Asian Economies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hiroyuki Kosaka
Alternative Measures of Economic Inequality and Poverty
in Japan, Taiwan and Thailand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oleksandr Movshuk
Empirical Matching Functions and Job Search Competitions in China. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Liu
Estimating the Local Economic and Environmental Impacts of
Two Carbon Tax Policies: the Production and Consumption-based Accountings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ryo Eto
Yohji Uchiyama
Keiichi Okajima

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

THE JOURNAL OF ECONOMETRIC STUDY OF NORTHEAST ASIA
(JESNA)

Vol. 9 No.1

EDITIONAL COMITTEE
Honorary Chairman
Lawrence R. Klein

Editors (Board Members)
Takahiro Akita
Soshichi Kinoshita
Reiitsu Kojima
Masaaki Kuboniwa
Shuntaro Shishido*
Tatsuo Hatta

Shinichiro Tabata
Yasushi Toda
Qiyan Wang

September 2013

Contents
Page(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 Fujiwara
Osamu Nakamura
Sang-woo Nam
Haruki Niwa
Steven Rosefield
Hiroshi Sakamoto

The Journal of Econometric Study of Northeast Asia

(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

Econometric Modeling of Multi-Country and Multi-Sector
System for the Asian Economies........................................................................................... Hiroyuki Kosaka

1

Alternative Measures of Economic Inequality and Poverty
in Japan, Taiwan and Thailand........................................................................................... Oleksandr Movshuk

19

Empirical Matching Functions and Job Search Competitions in China........................................................................................... Yang Liu

33

Estimating the Local Economic and Environmental Impacts of
Two Carbon Tax Policies: the Production and Consumption-based Accountings........................................................................................... Ryo Eto
43
Yohji Uchiyama
Keiichi Okajima

The Journal of Econometric Study of Northeast Asia Vol. 9, No.1, 2013

1

Econometric Modeling of Multi-Country andMulti-Sector System for the Asian Economies
Hiroyuki Kosaka*

Abstract
This paper intends to model multi-country and multi-sector system for the Asianinternational input output system of Institute of Developing Economies from micro
foundation; a) households maximize their utility functions under budget constraint, b)
producers maximize their profits, resulting in yielding factor demands, and sector prices
in dynamic sense. In environment of multi-country(R countries exist) and multi-sector(N sectors exist), we have N international oligopolistic markets of differentiatedcommodities supplied by R producers. The model system is demand oriented.

East Asian Economy, international multi-sector model, dynamic price
K EYWORDS : formation, international product differentiation, international oligopoly
market, cost function, almost ideal demand system
JEL classification: D 58, O53

1. Introduction
After World War II, econometricians have tried to construct macroeconometricmodels mainly in the US. Recent tendency for modeling has two folds; the first movementof economic modeling post 1970s is marked by Lucas critique in 1976. Macroeconomic
modeling has been required to have micro foundation for seeking stability of “deepparameter.” The second movement is to introduce stochastic elements for the disturbanceterms of behavioral equations. Original contribution goes back to random shock theory by
R. Frisch (1933); later, F. Adelman & I. Adelman (1959) have revived the theory in their
computer simulation using Klein-Goldberger Model, and about ten years later leading
econometricians at that time as well as L. R. Klein have recongnized, using couples of large
scale econometric models, necessity of stochastic terms in the econometric models in 1969.
(See B. G. Hickman (1972))
Econometricians have constructed various types of models on national economy.
Single-country and single-sector model (SCSS model) is named macromodel, for whichmuch efforts have been directed. An attempt to approach national economy by decompose
macro-economy into several sectors has been first made by W. Leontief, which is called
single-country and multi-sector model (SCMS model). After W. Leontief, sophisticatedmodels have been made by G. Fromm & L. R. Klein (1975) and WEFA (1982). Recent CGE
model belongs to SCMS. Superiority of SCMS model to SCSS model is macro and micro
economics being united together in SCMS model. When one seeks to investigate national
economy, one sometimes needs to consider international interrelationship among countries.
This approach is called multi-country and single-sector model (MCSS model), which has* Faculty of Policy Management, Keio University
Email:hkosaka@sfc.keio.ac.jp

XXU

ITU
IT

XXJ
XXK

IT
IT

XXN
XXC

IT
IT

XXT
XXS

IT
IT

XXP
XXM

IT
IT

XXI

IT

I

DP
DP

Output

DPU
DP

J
K

DP
DP

N
C

DP
DP

T
S

DP
DP
DP

P

YCU

WS

YCI
YC

I

M

M

J
K
N
C
T
S
P

YCJ

WS
WS

YCK
YCN

WS
WS

YCC
YCT

WS
WS

YCS
YCP

WS

YCM

WS
WS
WS

3

2

DFU
DFJ
DFK
DFN
DFC
DFT
DFS
DFP
DFM
DFI
DAU

U
J

DAJ
DAK

K
N

DAN
DAC

C
T

DAT
DAS

S
M

P

DAP
DAM

I

DAI
D

OFU
OFJ
OFK
OFN
OFC
OFT
OFS
OFP
OFM
OFI
OAU
OAJ
OAK
OAN
OAC
OAT
OAS
OAP
OAM

WAI WAM WAP WAS WAT WAC WAN WAK WAJ WAU WFI WFM WFP WFS WFT WFC WFN WFK WFJ WFU

OAI
O

W

GFU

BF
BF

GFJ
GFK

BF
BF

GFN
GFC

BF
BF

GFT
GFS

BF
BF

GFP
GFM

BF
BF

GFI
GAU

BA
BA

GAJ
GAK

BA
BA

GAN
GAC

BA
BA

GAT
GAS

BA
BA

GAP

BA

GAM

BA

GAI
G

B

Note: Idn(I) is Indonesia, mls(M) is Malaysia, phl(P) is the Philippines, sgp(S) is Singapore, tha(T) is Thailand, chn(C) is China, twn(N) is Taiwan, kor(K) is south Korea, jpn(J) is Japan,
usa(U) is the United States; EG is exports to Hong Kong, EO is exports to the European Union, EW is exports to the rest of the world, Q is statistical discrepancies; B is international
freight and insurance, G is imports from Hong Kong, O is imports from the European Union, W is imports from the rest of the world, D is import duties; WS is wages and salaries, YC is
operation surplus, DP is depreciations, and IT is indirect taxes less subsidies.

EW
EO
EG
U
J

F
F
F

K
N
C

F
F
F

T
S
P

F
F
F

M
I
U

F
X
X

J
K
N

X
X
X

C
T
S

X
X
X
usa

M

X
jpn

X

X
kor

UM

X
twn

X
X

I

P

XXJ

XXU
Q

U

Q
EW

U
U

EO
EG

U
UU

F
F

UJ
UK

F
F

UN
UC

F
F

UT
US

F
F

UP
UM

F
F

UI
UU

X
X

UJ
UK

X
X

UN
UC

X
X

UT
US

X

UI

X

XXK

XCI
chn

JM

XTM
XTI
tha

X

JP

X

UP

J
J

Q
EW
EO

J
J
JU

EG
F
F

JJ
JK
JN

F
F
F

JC
JT
JS

F
F
F

JP
JM
JI

F
F
X

JU
JJ
JK

X
X
X

JN
JC
JT

X
X

XXN

JI

KP

X

KM

X

KI

X

JS

K
K

Q
EW
EO

K
K

EG

KU

F
F

KJ
KK
KN

F
F
F

KC
KT
KS

F
F
F

KP
KM
KI

F
F
X

KU
KJ
KK

X
X
X

KN
KC
KT

X
X
X

KS

XXC
QC

N
N

EWC
EOC

N
NU

N

EGC
FCU
FCJ

NJ
NK

FCK
FCN

NN
NC

FCC
FCT

NT
NS

FCS
FCP

NP
NM

FCM
FCI

NI
NU

XCU
XCJ

NJ
NK

XCK
XCN

NN
NC

XCC
XCT

NT
NS

XCS

NM

NP

XCP
XCM

NI

XXS

XXT
QT
EWT
EOT
EGT
FTU
FTJ
FTK
FTN
FTC
FTT
FTS
FTP
FTM
FTI
XTU
XTJ
XTK
XTN
XTC
XTT
XTS

XXP

XSM
XSI

XTP

QS
EWS
EOS
EGS
FSU
FSJ
FSK
FSN
FSC
FST
FSS
FSP
FSM
FSI
XSU
XSJ
XSK
XSN
XSC
XST
XSS

XXM

sgp

XSP

QP
EWP
EOP
EGP

EGM EOM EWM QM
FMU

FPU
FPJ

FMJ
FMK

FPK
FPN

FMN
FMC

FPC
FPT

FMT
FMS

FPS
FPP

FMP
FMM

FPM
FPI

FMI
XMU

XPU
XPJ

XMJ
XMK

XPK
XPN

XMN
XMC

XPC
XPT

XMT
XMS

XPS
XPP
XPM

XMP
XMM

XPI

mls

XMI

FIM
FII
XIU
XIJ
XIK
XIN
XIC
XIT
XIS
XIP
XIM
XII
idn

phl

XX
XXI

Output

Q
QI
EW
EWI
EO
EOI

Export (E)

EG
EGI
usa
FIU
jpn
FIJ
kor
FIK
twn
FIN
chn
FIC
tha
FIT

Final demand (F)

sgp
FIS
phl
FIP
mls
idn
usa
jpn
kor
twn
chn
tha

Intermediate demand (X)

sgp
phl
mls

Asian international input output tables have been published in dollar term for every
five years ;tables of four time digit (1985, 1990, 1995 and 2000) are now available. (See IDE(1993), IDE (1998), IDE (2001), IDE (2006a), IDE (2006b)) We are going to convert tablesof nominal dollar term into those of local currency and of constant price in advance. The
reasons of conversion are explained in focusing the dubiousness on domestic economictransaction in dollar term.
a) Economic Decision Making in Real Term and in Local Currency
Economic decision making in model has to be formulated in real term. In line with this wehave two alternatives: real term in dollar and real term in local currency. Most private andpublic economic agents are considered to make decisions in local currency; for examples,
typical decision makings in production and in consumption are made in real term of local
currency.
b) Foreign Exchange Market of Local Currency
People in Asian countries actually use dollar in international transaction, and at the sametime they use local currency in domestic transaction; as a result, they need to exchangemoney at foreign exchange market.
c) Monetary Policy using Local Currency
Individual country has right to issue local currency under the name of monetary policy.
Generally, Asian international input output table have two aspects; a) the one is on
demand side, and b) the other supply side. In line with this, we show layouts of converted
tables; i.e., Table 1 is a total layout of Asian international input output table.

idn

2.1 Tables of Local Currency and of Constant Price

The Layout of the Asian International
Input-Output
Table
Table
1 The Layoutof the Asian International Input-Output Table

2. Multi-Country and Multi-Sector Modeling for an Asian International
Input Output System

Table 1:been pioneered by L. R. Klein’s Project LINK. The fourth type model is multi-country andmulti-sector model (MCMS model), which has been first made by W. Leontief & F. Duchin(1983) Superiority of MCMS model to MCSS model is to be able to argue international
trade such as inter-industry trade or intra-industry trade within MCMS system.
On MCMS, we have now three approaches; a) linking domestic SCMS within the
trade by goods (See S. Kinoshita, et al. (1982), C. Almon (1991), F. G. Adams, B. Gangnes& S. Shishido (1993), and K. Uno (2002), b) international CGE by T. W. Hertel (1997)(GTAP model) and by W. J. McKibbin & P. J. Wilcoxen (1999) (G-Cubed model), and c)modeling MCMS on the data of international input output table (our model falls in thiscategory). In the late 1970s, an Asian international input output table was first created by
Institute of Developing Economies of Japan.
The next section will show an Asian international input output table and total pictureof our analytical framework, section 3 will explain household’s behavior, and section 4
producer’s behavior for MCMS system from microeconomic foundation. Section 5 is simple
comments on modeling for the rest of behaviors in MCMS, and in the last we conclude.

Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economies

Intermediate input (X)The Journal of Econometric Study of Northeast Asia

Value added2

4

The Journal of Econometric Study of Northeast Asia

Our model for MCMS system has several features.
a) Model in Local Currency and in Real Term
Most behavioral equations in our model are presented in local currency and in real term.
b) Differ from CGE
Basic drawback of CGE is not to utilize information from sample data in the estimation of
behavioral equations; instead, they use calibration using one period data.
c) Endogenize Domestic Saving
Household saving, namely future consumption, is taken explicitly in our householdcommodity.
d) Attach New Kind of Innovation to Producer’s Cost Function
Cost function has price of the current sector besides output and input prices.
e) Formulate Dynamic Price Determination
Price is formulated in dynamic way in link with profit maximization.
2.2 Total Picture of MCMS
We try to construct MCMS system, having an Asian international input output table ofInstitute of Developing Economies (IDE) in mind. The IDE’s Asian IO Tables in dollar
term are converted into tables in local currency and in constant price. We already havedetailed converting process from dollar based original nominal Asian IO tables into thetables of local currency and of constant price in T. Yano and H. Kosaka (2008).
The importance is to give platform of unified system where macro- and microeconomics could be merged. International trade of inter-industry or intra-industry requires
framework of multi-country and multi-sector; so that, the common place must be provided.
In exposing MCMS system, arguments closely concern with international trade. Traditional
theory of international trade has centered on international exchange of goods between
different markets among dissimilar countries based on constant returns to scale and factor
endowment as inter-industry trade. But, T. Negishi (1969a) has opened a possibility ofinternational trade based on increasing returns to scale from Marshallian externality. Ten
years later, P. R. Krugman (1979) has proposed another possibility of international trade of
Chamberlain’s monopolistic competition following Dixit-Stiglitz formulation of industrialorganization, also based on increasing returns to scale. Third possibility has been made by
J. A. Brander & P. Krugman (1983) on the concept of Cournot oligopoly on the homogeneous
market. These three attempts are called intra-industry trade. Now we come to fourth
possibility. It is well known that automobile industry, exchanging automobiles between
advanced countries as bilateral trade, is a typical industry producing highly differentiatedgoods of horizontal sense in the same market. (See, for example, P. K. Goldberg (1995))
Then the fourth possibility of intra-industry trade is given rise to in the market of horizontal
differentiation in the presence of increasing returns to scale. Such cases are frequently seen;in addition to automobile industry, air transportation service or financial service among
advanced and similar countries. Besides supply side of international trade, demand side isalready familiar in similar tastes across countries as Linderian effect by S. B. Linder (1961).
Our framework on international trade has, therefore, two folds:a) Oligopoly Market of Horizontal Differentiation
World markets of inhomogeneous commodities are introduced into MCMS system.
b) Multi-Commodity Markets of Block Recursive Interconnection

Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economies

5

The differentiated markets are interconnected in block recursive way by input output nexus.
In the first we assume the following.
Assumption 01: Country and Sector
We suppose to have R countries (or regions), each having N sectors. In applying MCMS
model to Asian input output system, included countries and regions are Indonesia,
Singapore, Malaysia, Thailand, Philippines, China, Korea, Taiwan, Japan, and US(i.e.
R=10). Included sectors are aggregated into six categories: 1)Agriculture, livestock,
forestry, and fishery; 2)Mining, quarrying, and utilities (electricity, gas, and water supply);3)Manufacturing; 4)Construction; 5)Trade and transportation; 6)Services (i.e. N=6).
Assumption 02: Oligopoly Market of Horizontal Differentiation with R Suppliers
The h-th country is supposed to have unique firm producing single i-th commodity ofhhorizontally differentiation, featured by border, with different price P i which is expressedin local currency. Then i-th commodity market has totally R suppliers.
Assumption 03: N Oligopoly Markets of Interconnection
From the Assumptions 01-02 we have N world commodity markets which are interconnectedin block-recursive way by intermediate transactions, each commodity market being
horizontally differentiated.
2.3 World Commodity Market of Horizontal Differentiation
The unique firm in h-th country provides horizontally differentiated i-th commodity
for the world oligopolistic market; as a result, R differentiated prices coexist. As is wellknown in input output system, equation below shows market equilibrium that unique firmof producing i-th commodity in h-th country faces with; namely, left hand side stands for
supply and right hand side for demand, and both are expressed in constant price and in local
currency of h-th country.

hXi : i-th Commodity produced by Oligopoly Firm in h-th Countryhk
xij : Intermediate Demand for i-th Commodity in h-th Countryby j-th Oligopoly Firm in k-th Countryhk
CP i : Private Consumption for i-th Commodity of h-th Countryby k-th Countryhk
G i : Government Consumption for i-th Commodity of h-th Countryby k-th Countryhk
I i : Private Investment for i-th Commodity of h-th Countryby k-th Countryhk
IV i : Inventory Investment for i-th Commodity of h-th Countryby k-th CountryhEi : Export for i-th Commodity of h-th CountryhQi : Statistical Discrepancy for i-th Commodity of h-th Country

(2.1)

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The Journal of Econometric Study of Northeast Asia

Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economies

3. Household Behavior
This subsection explains private consumption CP hki and related behaviors of householdfrom micro foundation dealing with household in k-th country as a unit. Suppose householdkearns wage from working in production of j-th sector in k-th country, so that wage rate w jkand labor demand L j of j-th sector in k-th country are determined endogenously. As earnedkkwage and property income Y r totalizes disposable income M , we have the accounting
identity.
Definition of Nominal Disposable Income in k-th Country


(3.1)

For theoretical development for consumption by commodity, we have already Stone’s
linear expenditure system (LES), Rotterdam model and an ideal demand system (AIDS) by
A. S. Deaton & J. Muellbauer (1980). Dual feature of AIDS approach is, like an analogy ofargument that cost function of production a prior given leads to factor demands via
Shephard’s lemma, that given household expenditure function a prior leads to demands ofcommodities via Shephard’s lemma.
Thus expenditure function possesses all the information on commodity demand in
itself. In considering factors governing commodity demand besides income and prices, wemust inquire for expenditure function.
a) Cohort and Life Cycle
While cohort asks the time when consumer was born, life cycle asks consumer’s age.
Effects of cohort and life cycle on consumption are seen in categorized consumptions byages. As both factors would shifts household utility, expenditure function would havefactors inside. A. S. Deaton & C. Paxson(1994) have investigated inequality of consumption
from the viewpoint of cohort and life cycle.
b) Habit Formation - Dynamics in Consumption Persistence of consumption for particular commodities comes from habit formation in
consumption. Expenditure function for expressing habit formation holds consumption withtime lag. (See, for example, L. A. Blanciforti and R. D. Green (1983))
c) Family
Difference of family structure would affect economic activity, and also consumption ofcommodity. In the pioneering work of E. Engel, family charactering index is made in“equivalence scale,” which is put into expenditure function. (See R. Ray (1986))d) Advertisement
Advertisement or sale promotion activity to stimulate demands of consumers is big in thereal world; unfortunately, economics has been inclined to ignore it in theoreticaldevelopment. As advertisement would enter household utility, expenditure function would
have such variables. (See, E. A. Selvanathan and K. W. Clements (1995))e) Population
It is often said decrease of production population of age15-age64 tends to be responsible forstalemate of Japanese economy recent years. This is an evidence of expenditure function
dependent on population (See R. Ray (1996))f) Consumption of Leisure - Labor Supply -7

C. L. Ballard, D. Fullerton, J. B. Shoven and J. Whalley (1985) introduced leisure inhousehold utility; then, they deduced labor supply with commodity consumption.
g) Recent Tendency for Food Consumption
Recent tendency of caring health shows distortion in disaggregated food consumption as isseen in vegetarians or consumer in favor of foods less cholesterol. Study reflecting thistendency by D. J. Brown & L. Schrader (1990) included index of cholesterol in the utilitiesof these consumers.
h) Future Consumption - Household Saving As a final extension we are going to introduce future consumption in the lastcommodity, namely household saving. Following C. L. Ballard, D. Fullerton, J. B. Shoven
and J. Whalley (1985), we endogenize future consumption in much the same way.
Argument like linear expenditure system (LES) without future consumption is that
consumer expends all the income; but, in reality they have saving. This approach isconsidered to be in two-stepped; a) saving decision is predetermined, and secondly b)
expenditure for commodities is made afterwards. Yet, in this approach, saving process is
described in implicit. We intend to endogenize household saving in explicit; we state
disposable income partly goes to consumptions of N commodities, and partly goes toincrease of saving (future consumption), which is treated as (N+1)-th commodity. For thispurpose we have to define price of future consumption in the first.
Definition of Price of Future Consumption in k-th Countryk
Price of future consumption p f in k-th country is defined as weighted average of domestic
consumption for N goods at recent year.



(3.2)

kk

CPi : Consumption of i-th Commodity produced by i-th Sector
in k-th Country in recent yearkpi : Price of i-th Commodity in k-th Country
Note that we use, in weighting coefficients of (3.2), prices of domestic goods ignoring theother countries’ prices. Note also that we possibly take geometric average or rational
expectation for future price in (3.2) instead of arithmetic average.
Now we face with optimal decision on allocation of disposable income over (N+1)
commodities in k-th country’s household; the k-th country’s household is supposed tohk
decide optimal allocation of disposable income over i-th commodity C i and futurekconsumption C f by maximizing household utility under its budget constraint expressed in
nominal k-th country’s currency.
(3.3)h: Price of p i received

8

The Journal of Econometric Study of Northeast Asia

Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economiesby k-th Country: Rate of Export Subsidy of i-th Commodity in h-th Countryshk
CPi : k-th Household Consumption of i-th Commodity producedby i-th Sector in h-th Countryk
CPf : k-th Household Future Consumptionkhe , e : Foreign Exchange Rates of k-th and h-th Country per Dollar respectively
Future consumption leads to an increase of saving.

9

4. Producer’s Behaviorhi

ΔS k = pf CP kf

(3.4)

S k = S -1k +ΔS k

(3.5)

Equation (3.5) determines total amount of saving (S k); yet, we leave unmentioned to thecontents of saving, namely, its portfolio. Total saving will become primary deposit; as aresult, it might lead to variable of financial model. Property income is determined by thefollowing equation.
k

k

k

k

k

Yr =ζ0 +ζ1 (r S -1)(3.6)

r k : Long-term Interest Rate in k-th CountryhkkNow, in determining optimal demand for i-th commodity CPi and future consumption CPf
in k-th country, we will employ AIDS model by A.S.Deaton & J.Muellbauer(1980).
AIDS for MCMS System
Define vectors and matrix (T stands for transpose);

Then, nominal share model of M k is;(3.7)
in whichand Гi is i-th row vector of Г. The case
i=N×R＋1 is of future consumption. Relative price in share equation in (3.7) is essential inview of commodity consumption across countries. Notify that estimation of individual〜k. And, factors ofequation (3.7) is interdependent on each other via parameter vector α
shifting consumer demand such as advertisement, sale promotion activities, product〜k. Note also that AIDS
innovation and transportation are assumed to be all expressed in α
model needs to have three conditions:∑wi =1, ∑αi =1, ∑βi =0.

4.1 Designing Multi-Sector ModelFirst we have to distinguish in viewing world market is in equilibrium or in
disequilibrium. As is seen in the preceding section, the market is in equilibrium and in an
oligopoly with product differentiation of R suppliers. In the world j-th commodity market,
k (D)
k (S)
excess demand Xj > Xj forces price to go up or cut off a part of demand; inversely,
k (D)
k (S)
excess supply Xj < Xj would decrease supply to meet demand. Such mechanism works
for production to stock; but, supply meets demand automaticaly in order production.
In the following, we intend to see factor demand and price determination behaviors;
how are these two described in link with rational economic behaviors? An attempt to model
multi-sector system from micro-foundation was first made by L. Johansen (1960); profit
maximization (hereafter PM in short) make factor demand under perfect competition, sothat he has no mechanism to determine price (then price remains exogenous). M. Saito(1971) states; PM makes factor demand in perfect competition with constant return to scale,and demand/supply nexus determines price in dynamics like cobweb model. And
K. Tsujimura & M. Kuroda (1974) designed; SFS production function makes factor demand
under economy of scale, and PM determines price in static way under imperfect competition.
Traditional microeconomics PM describes price in the absence of intermediate input. CGE
model depicts that PM makes factor demand, and price is made by accounting identity (See,for example, C. L. Ballard et al. (1985)). The paper provides cost function for factor demand
via Shephard’s lemma, and provides PM under imperfect competition for price determination.
4.2 Factor Demand - Cost Function Approach Following W. E. Diewert & K. J. Fox (2008), in a given cost function, we deduce
factor demands by Shephard’s lemma, which lead to price determination. For this we takelong-run cost function having capital stock inside. Now develop Bertrant competition with
k (D)
k (S)
price as strategic variable for aggregated profit. Demand Xj and supply Xj are clearly
distinguished.
The j-th firm has two agents, each collaborating for production; upper agent (agent 1)
is aiming at price setting by profit maximization, and lower agent (agent 2) is aiming at
deciding factor demand by cost minimization. Cost minimization is firstly explained.
The second agent allocates factors optimally by cost minimization under productionk (S)command Xj and given prices.

kCj : Cost Function of j-th industry in k-th countrykhk
x〜j = {x ij ; i,j = 1,2,.., N;h,k = 1,2,.., R}hk*pi : price of i-th product of h-th country received by k-th countrykr : capital cost in k-th country

(4.1)

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Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economies

After the lower decision is first made by agent 2, then upper decision is made by agent 1
using the optimal first decision. The scheme is analogical to Stackelberg game in ankoligopolistic market; a) agent 2(Stackelberg follower) makes decision using price P andk (S)production Xj as parameter values, and b) the agent 1(Stackerberg leader) makes optimal
decision for price and production using cost function.
Specification of Cost Function
We will employ an approach of deriving factor demand equation via Shephard lemma
given cost function a priori. Generally cost function shows no special kind of behavior; then,translog cost function, ignoring more than third order terms in Taylor series expansion of thefunctions, has been widely used, which function was first posed for production function by
L. R. Christensen, D. W. Jorgenson and L. J. Lau (1973). However, the cost function shouldhave economic rationality. The generalized Leontief cost function by M. A. Fuss (1977)――
should be remarked; c (y, p, t,)=∑i,j hij (y, t) √ pi √ pj in which pi, y and hij (y,t) are factor
price, production, and unspecified symmetric concave function respectively. As M. A. Fuss
did not specify hij (y,t) in his cost function, a number of contributions have been made byvarious authors. Current paper intends to have long-run cost function having capital inside.
k (S)
k (K)
k (S)
k
k
k
k
k
(Xj , P k, wj , r k, t) as cost function;
Instead of C j (Xj , P , wj , r , t), now write Cj
hk
k (K)
hk*
k
k (K)
k
k (CM)k (K)= ∂Cj /∂r k. Now introduce
accordingly x ij =∂Cj /∂pi , L j = ∂Cj /∂wj , Kj〜kadjusting process for capital formation, resulting in the cost function C j , as R.S.Pindyck &k (CM)
is to be adjusted. Desired
J.J.Rotemberg (1983) have done; optimal capital demand Kjk*k*klevel of capital Kj is assumed to have ∂Kj /∂Kj = 0.



(4.2)

Since the first order condition for (4.2) is c j(k1) (K j -K j ) + c j(k2) (K j -K j,-1) = 0, we obtain;
k

k

k*

k

k

k



11

k (S)

Xj : Output of j-th Sector in k-th Country
ηkpμ : Price of μ-th Sector inη-th Country received by k-th Countrykwj : Wage Rate of j-th Sector in k-th Countrykr : Capital Cost in k-th Countryt : Time Index used to capture Effects of Disembodied Technical Change
k
k
k
k
k
k
k
k
k
b1ημ (p), b1ημ (X), b1ημ (t), b2 (w), b2 (X), b2 (t), b3 (K), b3 (X), b3 (t) :Unkown Parameters
Innovation attached to Cost Function
We will assume technical progress which is attached to the cost function and its
corresponding factor demand equations. The term of time trend expresses technicalprogress of Hicks neutrality. Including Hicks technical progress, S. Shishido & O. Nakamura(1992) classified three kinds of technical progress.
A Type: Price Independent Technical ProgressThis type is related to S of R. Stone’s RAS method. The progress not saves particularfactors such as labor, but saves all factors independently from prices of factors andproduction price. They exemplify a) reducing cost by introducing innovative technology, b)
quality control or total quality control and c) IT innovation. Cost function holds term oftime trend for expressing this technical progress.
B Type: Technical Progress depending on Output Price
Cost reducing behavior, done by management efforts, reacts to reduction of output price,
but does not aim at reducing particular factor demands. These behaviors, caused by
globalization and currency appreciation mainly after 1990s, could be seen in mostmanufacturing industries in Japan.
C Type: Technical Progress depending on Input PriceThis type is related to R of R. Stone’s RAS method, and is aroused from rise of factor pricesuch as material (e.g., rise of oil price), labor and capital.
Cost function embodying B-type technology is exposed in account of abovek (e)consideration in the following way by entering pj .

(4.3)

The equation (4.3) is depicting noted stock adjusting rule.(See, for example, L. R. Kleink*k (CM)k*k (CM)(1983)) It might be possible to put Kj = Kj,-1 or to put Kj = Kj
afterwards. For empirical
study, function hij (y,t) should be specified, and we follow S. Nakamura (1990); he named
Generalized Ozaki cost function. In the absence of hij (y,t)(i=/j) , the Generalized Ozaki
cost function for our MCMS is simplified to the following, in which the second and thirdterms refers to wage and capital respectively. Terms of time trend stands for Hicks neutral
technological change.


p

k (e)
j

: Expected Price of p

(4.5)

k
j

Now, by Shephard’s lemma, we obtain the intermediate, labor and capital demand
equations respectively.
(4.6)
(4.4)

(4.7)
(4.8)

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These equations have alternative expressions for estimation.

Total Factor Productivity(TFP)
TFP of j-th sector in k-th country is straightforward.


(4.9)



(4.10)


Substitution and Complementarity of Intermediate Demands
Definition of substitution and complementarity for two inputs is stated.
∂xijhk /∂pμηk* > 0 : substitutive
μ=/ i (4.12)

xijhk : Factor Demand of i-th Commodity in h-th Country by j-th Sector in k-th Country(4.13)

Economy of Scale(4.14)

MCjk : Marginal Cost
Rate of Technical Progress
Formula of technical progress of j-th sector in k-th country in virtue of cost function is
product of two elements associated with cost function where left hand side is unknown, butelements of right hand side are computed in ease.

(4.16)

(4.15)(4.17)

4.3 Dynamic Pricing of Oligopoly Firms with Product Differentiation
Inputting the estimated coefficients of factor demand equations into cost function, one
can calculate the cost function empirically. Profit maximization in use of the cost function
leads to monopoly price, but the price does not coincide with the current price; profit
maximization could not describe the current price, so that another mechanism other thandirect profit maximization has to be provided. Following the line, T. Shibata & H. Kosaka(2011), in Japanese interregional input output system, and H. Kosaka (2011), in Asianinternational input output system, have introduced conjectural variation in profit
maximization to explain the current price. Yet, this paper intends to explain the current
price in dynamic way in the absence of conjectural variation. Profit maximization states;
〜k
k
k
k(D)
k(S)
k
k (D)
k
k
map x πj = map x {pj Xj -C j (Xj , P , wj , r , t)} , in which Xj stands for perceived demandk (D)function by T. Negishi (1961); the variable Xj might be real demand function or subjective
demand curve differing from the former. The demand function works in profit maximization.
Once A. W. Phillips (1954) has proposed proportionate, derivative and integrated
policies as policy response functions in an argument of macroeconomic stabilization policy
within multiplier-acceleration model; inversely it would be possible to pose various social
welfare functions which are pairs to policy response functions. Utilizing the A. W. Phillips’
idea, one could add quadratic loss of price to profit; then, one could design pricedetermination in dynamical way in maximizing profit related objective function. Pricesetting behavior of oligopolistic firms is assumed to be in disfavor of drastic price change.
The quadratic loss is two kinds: increasing rate and difference. The loss of increasing rate
k
k
k
k
k
is to seek (pj - pj,-1) / pj,-1 to certain level b, which is interpreted to force (pj - (1+b) pj,-1) =0.
k
k
k
k
2
k
The behavior is modeled to put cc1 (pj - cc2 pj,-1) (cp1 > 0) to objective function. The difference
k
k
k
k 2
k
objective is to have cp3 (pj - pj,-1 - cp4) (cp3 > 0), which is also interpreted to put pricekkkkdifference to particular level cp4 in gaining finally pj - pj,-1 = cp4. Accordingly, profit related
objective is altered to the following;kjpμηk* : Price of μ-th Commodity in η-th Country received by k-th Country
Allen’s elasticity of substitution in MCMS is the following.

(4.11)

Equation (4.6)-(4.8) or (4.9)-(4.11) are estimated in use of an Asian international input
output data; we note that the cost function (4.4) can be directly estimable if sufficient data
is available. Additional comments are; shift factor of intermediate demand is expressed inkk
logb1hi (p) by transportation distance, product innovation, and is expressed in logb2 (w) byimmigration. Once factor demand equations are estimated from empirical data assumingSD
Xj = Xj = Xj , cost function is estimable indirectly from inserting coefficients of factor
demand equations to the cost function.

∂xijhk /∂pμηk* < 0 : complementary

13

k
j



(4.18)

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Econometric Modeling of Multi-Country and Multi-Sector System for the Asian Economiesk*

The variable Xj stands for normal production, namely denominator for making unit profit;k*k∂Xj /∂pj = 0 is assumed. Optimization makes the first order condition;

15

expected price, which follows W. J. McKibbin & J. Nguyen (2004).

(4.23)p


(4.19)k (e): expected priceDefinition of Non Occupation RatiokkLabor force LF in k-th country less total occupation labor of j-th sector L j in k-th countrykmakes non occupation labor, and non occupation ratio UR is easily defined:

Re-arranging terms for price give the dynamic price formation equation.
(4.24)

5. Other Variables
(4.20)

As is seen above, many authors created sophisticated demand mechanism by goods onk (D)Xj; among others, AIDS model by A. S. Deaton & J. Muellbauer (1980) should be
k (D)
k
k
k (D)
k
notified. But, our subjective demand is simplified to ∂Xj /∂pj =βj × (Xj / pj ) ;k*k (D)furthermore we have Xj = Xj . Demand increase may have supply increase, so that we
suppose supply increase will positive response to price level; as a result, we suppose
k (S)
k (D)
k k
∂Xj /∂Xj =δj pj . These assumptions, inserting to (4.20), will give us the following
price determination equation in a final form for practical estimation.
(4.21)The equation indicates; a) increase of marginal cost may cause increase of price in (4.21),kkkb) increase of scale economy may decrease price by MCj = ACj /SEj , and the Lerner’skkkkmonopoly index LXj = (pj - MCj ) / pj will have positive impact on price increase bykkk
MCj = pj (1 + LXj ) .
Definition of Price at Macro Level−k(4.22)k

X j : Xj at base year

Price at macro level is weighted average of sector prices, in which sector productions inaverage weights are used at base year.
Wage Rate
Wage rate of j-th sector in k-th country is simply determined by productivity of labor and

Fixed investment for each industry is already argued, which might be domestic one;yet, overseas investment of multi-national corporation via foreign direct investment (FDI)should be taken by having another cost function of overseas production. But, this enlarged
framework is postponed for future study.
It would be possible to treat inventory investment for short-term behavior as exogenous
variable; but, if one counts inventory cost to cost function, one could endogenize thebehavior.
On government expenditure, four kinds of modeling could be suggested; a) exogenous
variable, b) simple conventional modeling of fiscal policy response function, c) micro
foundation of setting government utility function like household, and finally d) dealing
government as the leader of Stackelberg game using optimal control theory by G. C. Chow
(1975) Among the third approach, T. W. Hertel (1997) in GTAP assumes household has
Cobb-Douglas utility allocating private consumption, saving and government expenditure
simultaneously. The fifth possibility is, focusing defense spending of government
expenditure, to endogenize military spending, which would unveil link of economic
behaviors with international relation. (See L. R. Klein, M. Gronicki and H. Kosaka (1992),
H. Kosaka (1993))
Export to the other region is promising; unfortunately, the export to EU and BRICS ison the way. (See A. R. Hoen (2002)) In the final step multi-country and multi-sector systemcover the whole world.

6. Concluding Remarks
We finish developing multi-country and multi-sector model for the Asian international
input output system of Institute of Developing Economies from micro foundation in demandoriented view.

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The Journal of Econometric Study of Northeast Asia Vol. 9, No.1, 2013

19

Alternative Measures of Economic Inequality and Poverty
in Japan, Taiwan and Thailand
Oleksandr Movshuk*

Abstract
Using household surveys in Japan, Taiwan and Thailand, the paper estimates thedegree of economic inequality and poverty with respect to income and consumption,with three major conclusions. First, the paper found that estimates of inequality are
lower when measured by consumption, and the divergence was particularly large forJapan and Taiwan. Second, the paper provides new evidence that it is important toadjust for differences in household size when measuring economic inequality. In
particular, I found spurious upward trends in economic inequality of Japan and Taiwan,
which largely disappeared after adjusting for the number of equivalent household
members. Third, I found a broad agreement between historical trends in economic
inequality and poverty rate, with much lower poverty with respect to consumption
expenditures compared with more widely-used measures that rely on disposable income.

K EYWORDS : economic inequality, poverty
JEL classification: D63, I32

1. Introduction
While it is still most common to evaluate the degree of economic inequality withrespect to income, this conventional approach has been criticized in recent years (Attanasio
et al., 2011, Kohara and Ohtake (2011)). The use of income-based inequality measures has
several serious problems, such as incomplete reporting of income in household surveys to
avoid taxation (especially for incomes from the shadow economy), and the failure to accountproperly for living standards of aged household members, who may have low incomes, but
still could afford relatively high standards of living, by running down the stock of previously
accumulated savings.
These problems are less severe when inequality is examined with respect toconsumption. The seminal paper by Cutler and Katz (1991) was among the first to pay
attention to this alternative measure of economic well-being, and pointed out that economic
inequality in the United States is much less severe when measured with respect toconsumption, rather than income. Attanasio et al. (2011) also provided more recent
inequality estimates for the United States, and basically confirmed the earlier results of
Cuttler and Katz (1991) that consumption inequality in the United States is much less severein comparison with income-based measures.
A number of studies have applied this comparative approach to Japanese household
data. For example, Kohara and Ohtake (2010) studied changes in the poverty rate, definedby the headcount ratio of individuals who have less than one-half of median income (ormedian consumption). Similarly to previous studies with U.S. household data, the paper* University of Toyama. Email: movshuk@eco.u-toyama.ac.jp. I am very grateful to an anonymous referee for alarge number of useful suggestions. Any remaining errors and omissions are mine alone.

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Alternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

found that the degree of poverty in Japan was less severe with respect to consumption. For
example, when the poverty rate was measured by consumption expenditures, the ratio
remained stable at around 7.5 percent, while income-based calculations produced thepoverty rate of almost 10 percent. Subsequently, Ohtake and Kohara (2011) used a different
survey of Japanese households, and confirmed that economic inequality in Japan is lower
with respect to consumption. They also considered the sensitivity of an alternative index ofinequality, the Gini coefficient, and concluded that the Gini coefficient also shows a lower
inequality when it is applied to consumption, rather than income.
In this paper, I will extend the approach of Ohtake and Kohara (2011) to international
comparisons of economic inequality in Japan, Taiwan and Thailand, and examine thesensitivity of measured inequality with respect to alternative measures of economic wellbeing (such as disposable income or consumption expenditures). In addition, the paper will
study the sensitivity of measured economic inequality to adjustments for differences inhousehold size. Over the recent decades, Japan, Taiwan and Thailand have experienced adramatic demographic transformation that greatly reduced the size of typical households,
and this historical trend makes these countries well-suited for studying the possible bias ininequality measures when differences in household size are neglected.
This paper deals with three major gaps in previous studies of economic inequality andpoverty in Japan, Taiwan and Thailand (a number of representative studies are summarizedin Table 1). First, previous studies typically dealt with inequality or poverty in a single
country; when international comparisons were made (like, for example, in Tachibanaki and
Urakawa (2006)), they usually covered only developed economies. In consequence, there isa particular lack of studies that made international comparisons of Japan with other Asian
economies. Second, while consumption inequality was examined for Japanese household
data by Ohtake and Kohara (2010, 2011), there remains little evidence for other Asian
economies, including Taiwan and Thailand. Hsieh (2004) estimated consumption inequality
in Taiwan, but only with respect to housing expenditures. On the other hand, Kurita andKurosaki (2011) estimated inequality for total consumption expenditures, but they usedprovinces, rather than households, as the unit of analysis. Third, while the adjustment fordifferent household size were often used in studies for Japanese data (in particular, byTable 1 Previous
studies on inequality and poverty in Japan, Taiwan and Thailand
Table 1 Previous studies on inequality and poverty in Japan, Taiwan and Thailand
Paper(a) Japan
Ohtake (2005)
Oishi (2006)
Tachibanaki,
Urakawa (2006)
Ohtake (2008)
Ohtake, Kohara(2010)
Ohtake, Kohara(2011)(b) Taiwan
Hsieh (2004)(c)Thailand
Ikemoto (1991)

Unit of analysis Income inequality

Consumption inequality

Adjustment forequivalence scales

Poverty rate

Households and Yes (disposable income) Yes (total consumption) Yes (square root of
Yes (disposable income
income quintiles
household members)
and total consumption)
Households
Yes (disposable income) No
Yes (square root of
Yes (disposable income)household members)
Households
Yes (disposable income) No
Yes (various adjustment Yes (disposable income)factors)
No
Households
Yes (disposable income) Yes (total consumption) Yes (square root ofhousehold members)
Households and Yes (disposable income) Yes (total consumption) Yes (square root of
Yes (disposable income
income quintiles
household members)
and total consumption)
Households
Yes (disposable income) Yes (total consumption) Yes (square root of
Yes (disposable income
household members)
and total consumption)
Households

Yes (disposable income) Yes (housing
consumption)

No

Households and Yes (before-tax income) No
No
income deciles
Motonishi (2006)
Provinces
Yes (before-tax income) No
No
Kurita and Kurosaki ProvincesNo
Yes (total consumption) No(2011)

21

Ohtake (2005) and Tachibanaki and Urakawa (2006)), the adjustment was not widely usedin previous inequality studies for Taiwan and Thailand.
This paper attempted to address the major gaps in previous studies, and calculated anumber of alternative measures of inequality and poverty in Japan, Taiwan and Thailand.
To preview main findings of this paper, first, I found that consumption inequality was
substantially lower compared with income inequality, and the difference was particularly
large for Japan and Taiwan. Second, the paper provides evidence that it is important toadjust inequality measures for differences in household size. In particular, without such an
adjustments, inequality indices could produce a spurious upward trend, which diaperedwhen income or consumption was normalized by the number of equivalent adults. Third, I
found that there was a broad agreement between historical trends in economic inequality
and poverty rate, with much lower poverty when it was calculated with respect toconsumption expenditures, rather than disposable income.
The paper is organized as follows. Section 2 introduces two inequality measures thatwill be examined in this paper, the Gini coefficient and the poverty rate. Section 3 describes
major adjustments for household size, including estimates of equivalence scales. Section 4
describes major features of household surveys for Japan, Taiwan and Thailand, while
Section 5 reports alternative measures of economic inequality and poverty. Section 6summarizes major conclusions of this study.

2. Measures of economic inequality.
2.1 Gini coefficient
The Gini coefficient is one of the most common measures of economic inequality. The
coefficient varies in the range between zero and one, which identify the opposite cases ofperfect equality and inequality, respectively. The coefficient is derived from the Lorenz
curve, which measures the distribution of economic resources across individuals orhouseholds.
Figure 1 illustrates the dependence of the Gini coefficient on particular shapes of theLorentz curves, using actual distribution of income among Taiwanese and Thai households
in 2004 and 1992, respectively. The horizontal axis shows the cumulative proportion of
Figure 1 Derivation of the Gini coefficient from the Lorenz curve(a) Taiwan (2004)(b) Thailand (1992)
1

1

.75

.75

A

.5

No

.5

A

B

.25

.25

B

No
No
Yes (total consumption)

0

0
0

.25

.5

.75

1

0

.25

.5

.75

1

22

The Journal of Econometric Study of Northeast Asia

Alternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

households that ranges from 0 to 1. Similarly, the vertical axis shows the cumulative
percentage of income that corresponds to a given population share on the horizontal axis.
In the extreme case of perfectly equal income distribution, the relationship between
the cumulative shares in population and in income becomes linear (so, for example, 75
percent of households account for exactly 75 percent of the total income). In consequence,the Lorentz curve is located right on the main diagonal between the origin (0,0) and theupper-right point (1,1). With unequal distribution of income, a given percentage ofindividuals will correspond to a larger share of income, and the Lorentz curve will bend
downwards from the main diagonal. Using the example above, 75 percent of households
would account for a larger share (say, 90 percent ) of the total income.
The Gini coefficient takes advantage of this property of the Lorenz curve, andmeasures inequality by how much the Lorentz curve deviates from the linear pattern ofperfect equality. The coefficient compares the area between the main diagonal and actual
Lorentz curve (denoted by area A in Figure 1), and compares it with the total area below themain diagonal (which in Figure 1 contains areas A and B):
(1)In the case of perfect equality, area A is reduced to zero, and so the Gini coefficientalso becomes zero. Conversely, in the case of perfect inequality, the Lorentz curve would
stretch from the origin (0, 0) to point (0, 1) and then to point (1, 1), with area A coinciding
with area B, so the Gini coefficient will approach one.
Actual Lorenz curve in Figure 1 show that the Lorentz curve is much closer to themain diagonal in Taiwan compared with Thailand, indicating a more equal income
distribution in former case. In consequence, the Gini coefficient for Taiwan was 0.338,
compared with 0.538 for Thailand.
Note that formula (1) gives only the intuition behind the Gini coefficient, and not theactual numerical formula to calculate the coefficient. Several alternative approaches areavailable, such as the following formula:(2)with n denoting the sample size, i and j indexing for individuals or households, with yi
and yj denoting corresponding income or consumption.
In this formula, all possible differences between individuals i and j are evaluated, andnormalized by the sample average −y . The total number of comparisons between individuals
2i and j are given by n , while the multiplication by 2 in the denominator adjusts for thenumerical identity between yi -yj and yj -yi .
The standard calculation method in equation (2) is intuitive, but unfortunately theformula requires a large number of computations. For example, some household surveys in22
9Thailand include around 35,000 households, which means that n =35,000 ≈1.2×10
differences in yi -yj should be calculated, squared and summed up. Even with modern
personal computers, this involves substantial computational cost.

|

|

|

|

|

|



(3)

23

Formulas (2) and (3) produce identical values of the Gini coefficient, but formula (3)has a major advantage of much smaller computational burden that is proportional to the2sample size n, but not n , as in the case of formula (2).
The Gini coefficient can estimate economic inequality with different measures of
economic welfare. Though the coefficient has been commonly used to study income
inequality, for a number of reasons consumption may be a better indicator of economic
inequality than income.
First, in terms of conceptual relevance to measuring welfare, consumption may be abetter indicator, or actual living conditions of individuals or households (Deaton, 1997). In
particular, the life cycle theory of saving distinguishes aged individuals that have reduced
incomes after retirement, but nevertheless could relatively unchanged standards of living,which are fined from the stock of savings, accumulated before retirement. If income is used
to measure economic inequality, the effect from the aged population would bias the Gini
coefficient towards higher inequality, in comparison with inequality estimates that arecalculated from consumption expenditures.
Second, it is common for income to be more volatile than consumption in household
surveys. The problem is particularly serious for developing economies, where respondents
may be unwilling to state their full incomes (especially their earnings in the shadow
economies) to avoid taxation. In contrast, consumptions expenditures are generally not
subject to taxation, and this greatly alleviates the problem of under-reporting among survey
respondents.
2.2. Poverty rate
In general, the Gini coefficient measures the overall distribution of economic resourcesamong households and individuals, and provides little information whether rising inequality
is due to either increased poverty among the poor, or increased wealth among the rich. In
contrast, the poverty rate focuses on the poorest part of the population, and measures theshare of the poor individuals in the total population. The poor are defined with respect totypical levels of income or consumption, using the median value across individuals orhouseholds. The poverty line is then defined by some ratio of the median income andconsumption, with the most common choice of 50 percent of the median. Individuals orhouseholds with income or consumption below the poverty line are classified as the poor,and their number is counted. The poverty rate is simply the ratio of this count to the total
population.
2.3. Adjustment for differences in household size.
As reported in Table 1, it is still very common to measure economic inequality acrosshouseholds, without adjustments for differences in household size. However, this approach
may be highly misleading in countries that go through demographic transformations that
reduce the size of typical household. Figure 2 illustrates such demographic changes inJapan, Taiwan and Thailand. For example, the size of average Taiwanese household
decreased from around 5 to 3.5 household members between the late 1970s and the mid2000s. Similar declines took place in Japan and Thailand, with all countries currently

24

The Journal of Econometric Study of Northeast Asia

Alternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

Figure2 Historicalchangechange inof ofaveragehousehold
Figure2 Historicalinthe
thesizesize
averagehousehold
5.0

4.5

25

Unfortunately, it was not practical in this study to apply the OECD scale to all
household data due to the lack of detailed information about the age composition ofhousehold members. Instead, I followed the approach of Ohtake and Kohara (2010, 2011),
which normalizes income or consumption by the square root of household size. Like theOECD scale, this approach also assigns a progressively smaller factor for families withmore members.

4.0

3. Data
3.5

3.0
1978

1981

1984

1987

1990

Japan

Taiwan

1993

1996

1999

2002

2005

Thailandhaving less than 3.5 household members.
In general, this change reflects two demographic transitions: first, the declining birthrate and second, the gradual disappearance of three-generation households which used to
be a traditional family pattern in Asia. If these changes in household compositions are
ignored, economic inequality may be measured with a serious bias. For example, supposethat relatively affluent households also have a relatively large size. If inequality is measured
across households only, the differences in household size will create a positive bias ininequality compared with inequality measures that adjust for differences in household size.
On the other hand, if poor households have tendency to be larger in size, then inequality
measures per households will be smaller compared with corresponding indices acrossindividuals.
There are two major ways to take into account differences in the household size. The
first approach uses estimates inequality using income or consumption, normalized by the
number of household members. This approach, however, tends to underestimate theeconomic well-being of larger families, because it essentially assumes the same living costs
for each additional household member. However, some living costs may have a significant
economy of scale (most notably, housing costs), which reduce the additional cost with eachextra household member.
The second approach explicitly takes into account these economies of scale in living
costs. Specifically, rather than assuming equal extra costs, the approach applies a set ofdecreasing factors that are the largest for the first member of household, but then decrease
for successive household members.
Several equivalence scales have been proposed, with major alternatives summarized
by Fernández-Villaverde and Krueger (2007). A common equivalence scale was suggestedby the Organization of Economic Cooperation and Development (OECD), in which the firsthousehold member gets a factor of 1.0. The remaining adult members receive a factor of 0.7,
while children receive a uniform factor of 0.5 (OECD, 2008). With this equivalence scale,the family of two adults and two children would include 2.7 equivalent members, rather
than 4 in the first approach.

Economic inequality is based on household surveys in Japan, Taiwan and Thailandthat collect extensive information about household income and consumption. In general,
consumption was the total expenditures of households on goods and services, while income
was defined as after-tax income, most of which consist of wage income, social security
payments and income from real estate and financial assets.
For Japan, I used annual data from the Family Income and Expenditure Survey(FIES), with sample period from 1983 to 2004. The survey is a representative sample ofaround 8,000 households. One limitation of the survey is that up to the late 1980s, itcollected data only for workers' households with two or more members, effectively excluding
two important household types households: singles, and households with retired household
heads.
For Taiwan, I used annual household data from the Survey of Personal Income
Distribution from 1978 to 2004. The survey had a larger sample size than the FIES,
including about 16,000 households up to the early 1990s, and then declining only marginallyto approximately 14,000 households in recent years.
Household data for Thailand were taken from the Socio-Economic Survey of Thailand
between 1986 and 2004. The survey was conducted in irregular intervals over time, initiallyevery two years between 1986 and 1998, then annually between 1999 and 2002, and once
again every two years after 2002. Earlier surveys included around 12,000 households, andtheir number increased substantially in recent years, reaching more than 35,000 households
in 2004.
While the Gini coefficient in Taiwan and Thailand was calculated with the raw
household data, it was not possible in the case of Japan, where the publicly-available data
are not for households per se, but 10 deciles of income, ranging from the poorest to therichest ones. Calculations with such measures produce the ‘pseudo-Gini’ coefficient ofinequality, and are remain a common ‘second-best’ option when the original raw data are
not available (as, for example, in Ikemoto (1991), Ohtake (2005), Khan and Sen (2006),
Ohtake and Kohara (2010)).

4. Results
4.1. Estimates of economic inequality..
Figure 3 reports estimates of the Gini coefficient for three definitions of income: theunadjusted total household income, income per household member, and income per

26

The Journal of Econometric Study of Northeast Asia

Alternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

27

equivalent household member (namely, the square root of the number of household
members).
As shown in Panel (a), the Gini coefficient in Japan for the total household income was
gradually increasing since the early 1980s, and reached 0.30 in the late 1990s, with only aslight decline in subsequent years. Eventually, the coefficient dropped to 0.28 in mid-2000s,
which was close to its level in the mid-1980s. However, when income was normalized by
the number of household members, the Gini coefficient moved downward and remained
below 0.25 for most of the sample period. Moreover, the increasing trend in inequality

largely disappeared for the second definition of income, with the Gini coefficient evendeclining since the late 1990s. Finally, when income was adjusted for the number of
equivalent adults, the Gini coefficient became largely flat, and did not deviate much fromits long-term level of around 0.26. Overall, these divergent trends in income inequality
demonstrate that it is important to make proper adjustments for the changing size ofJapanese households. In particular, the use of unadjusted income data for total households
resulted in spurious rise in income inequality, which completely disappeared after
adjustments for differences in household size.

Figure3 EstimatestheGini
Ginicoefficient
coefficientfor income
Figure3 Estimates ofof thefor income(a) Japan
(a)
Japan

Figure4 4EstimatesofthetheGiniGini
coefficientfor consumption
FigureEstimates ofcoefficientfor consumption(a)(a)Japan
Japan

0.350

0.200

0.300

0.150

0.250

0.100

0.050

0.200
1983

1986

Total

1989

1992

Per member

1995

1998

2001

1983

2004

1986
Total

Per equival. member

1989

1992

Per member(b) Taiwan(b)
Taiwan

1995

1998

2001

2004

Per equival. member(b)(b)Taiwan
Taiwan

0.400

0.350

0.350

0.300

0.300

0.250

0.200

0.250

1978 1981 1984 1987 1990 1993 1996 1999 2002 2005

1978 1981 1984 1987 1990 1993 1996 1999 2002 2005
Total

Per member

Per equival. member

Total

Per member(c) Thailand
Thailand(c)(c)(c)Thailand
Thailand

0.600

0.500

0.550

0.450

0.500

0.400

0.450
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Total

Per member

Per equival. member

Per equival. member

0.350

1986

1988

Total

1990

1992

1994

Per member

1996

1998

2000

2002

Per equival. member

2004

28

The Journal of Econometric Study of Northeast Asia

Alternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

Panel (b) of Figure 3 reports alternative estimates of income inequality in Taiwan,
with four noteworthy results. First, the Gini coefficient for the total household income
showed a significant upward trend, from 0.27 in the late 1970s to 0.33 in the early 2000s.
Second, the adjustment for equivalent household members greatly reduced the degree ofincome inequality, with the Gini coefficient dropping by about 0.030 (or by about 10
percentage points) over the most of the sample period. Third, the Asian financial crises
produced a slight increase in all three alternative measures of income inequality, but theeffect was alleviated by the mid-2000s. Fourth, when measured by income per equivalent
Figure 5 Poverty headcount ratio
Figure 5 Poverty headcount ratio(a)(a)Japan
Japan
10
9
8
7
6
5
4
3
2
1984

1989
Income

1994

1999

2004

Consumption(b)(b)Taiwan
Taiwan
10
9
8
7

29

adult, the income inequality in Taiwan remained slightly higher (at around 0.28-0.30)
compared with Japan, where the Gini coefficient continued to stay close to the long-termlevel at around 0.25.
Panel (c) of Figure 3 displays changes in alternative measures of income inequality for
Thailand. Overall, not much difference is evident among three alternative Gini coefficients,
all of which remained at much higher levels compared to both Japan and Taiwan. Like in
Taiwan, the impact of Asian financial crises is evident, with inequality rapidly rising in
1998-1999. Also similarly to Taiwan, the effect of the financial crises turned out shortlived, and largely disappeared by the mid-2000s, with all three Gini coefficients revertingto their pre-crises levels.
Figure 4 reports estimates of the Gini coefficient for household consumption. Similarly
to results in Figure 3, three definitions of consumption are used: for the total household, perhousehold member, and per equivalent adult member.
Four major results are noteworthy in Figure 4. First, the extent of consumption
inequality was the lowest in Japan, and was followed by Taiwan, with Thailand showing thehighest inequality. Second, adjustments for household size were particularly important inJapan and Taiwan. For example, the Gini coefficient for the total household consumption
produced an increasing trend, which was no longer evident when consumption was
normalized by the number of households members, or by the number of equivalent adults.
Third, the effect of the Asian financial crises was much smaller on consumption inequality,with a distinct increase evident only in Thailand. Fourth, while Gini coefficients for income
and consumption were broadly similar in Taiwan and Thailand, this was not the case inJapan, where inequality was much lower in terms of consumption expenditures. Thisparticular discrepancy may be attributed a higher proportion in Japan of aged households
that have low incomes, but relatively high consumption expenditures, which are financed byrunning down the stock of life-time savings.

65

4.1. Estimates of economic inequality.

4
3
2
1978

1981

1984

1987
Income

1990

1993

1996

1999

2002

Consumption(c)(c)Thailand
Thailand
25

20

15

10

5
1986

1992
Income1998

2001

Consumption

Note: the poverty rate for Japan is from Ohtake and Kohara (2011).

Figure 5 reports estimates for the poverty rate with respect to income and consumptions,both of which are normalized by the number of equivalent adults. Due to the lack of the raw
household data for Japan, it was not possible to estimate the Japanese poverty rate; instead,the figure reports estimates of the poverty rate from Ohtake and Kohara (2011, p. 149).
Three noteworthy findings can be identified. First, in each of three countries, thepoverty rate by consumption is consistently lower than the poverty rate by income. For
example, Panel (a) shows that the Japan’s poverty rate by income is around 8.0 - 8.5 percent,while the rate by consumption drops to around 5.5 – 6.0 percent. Second, the wedge between
two alternative measures of poverty is particularly large in Thailand, by around 7-8
percentage points. Third, the rate of poverty in each country turned out remarkably stable
over time, with little deviations of long-term values.

5. Conclusions
This paper examined relative changes in income and consumption inequality in Japan,
Taiwan, and Thailand, and also changes in relative poverty rates with respect to income and30

The Journal of Econometric Study of Northeast Asia

consumption. Three major conclusions can be identified.
First, when calculating inequality indices, it is important to take into account thechanging composition of households, in particular, the historical decline in the size oftypical households. When the adjustment was not done, and Gini coefficients were
calculated for incomes or consumption for households as a whole, there was often a spurious
trend of increasing inequality, especially in Japan and Taiwan. However, when incomes or
consumption were normalized by the number of household members, the increasing trend
in the Gini coefficient often disappeared.
Second, the adjustment for the number of household members also showed that Japanand Taiwan are much more similar in their level of income inequality, especially for the
adjustment with the square root of the number of household members. On the other hand,
economic inequality in Thailand always remained higher than in Japan and Taiwan.
Similarly, the poverty rate in Japan and Taiwan turned out quite similar, with much larger
incidence of poverty in Thailand.
Third, results for consumption inequality in Japan, Taiwan and Thailand showed abroadly similar pattern to income inequality. The only noteworthy difference is that
consumption inequality in Japan was much lower than income inequality, possibly due to alarger share in Japan of aged households that have low incomes, but which could afford
high standards of living, which are financed by running down the stock of previously
accumulated savings.
References
Attanasio, Orazio, Battistin Erich and Padula Mario (2011), Inequality in Living Standards since 1980:
Income Tells Only a Small Part of the Story, The AEI Press.
Cutler, D., and L. Katz (1991). “Rising Inequality?” American Economic Review, 82, 2, pp. 546-551
Deaton, Angus (1997), The Analysis of Household Surveys, World Bank Publications.
Fernández-Villaverde, Jesús and Dirk Krueger (2007). “Consumption over the Life Cycle: Facts from
Consumer Expenditure Survey Data,” Review of Economics and Statistics, 89, 3, pp. 552–565.
Hsieh, Bor-Ming (2004), “ The Distribution of Household Income and Housing Consumption between
Tenure in Taiwan,” Paper presented at the 9th Asian Real Estate Society International Conference,
Delhi (India), 9-12 August, 2004.
Ikemoto, Yukio (1991), Income Distribution in Thailand: Its Changes, Causes, and Structure, Tokyo:
Institute of Developing Economies.
Khan A. R. and B. Sen (2006), “The Structure and Distribution of Personal Income and Poverty
Reduction: A Case Study of Bangladesh during the 1990s,” In James K. Boyce et al. (eds.), Human
Development in the Era of Globalization. Essays in Honor of Keith B. Griffin, Edward Elgar, pp.
301-324.
Kurita, Kyosuke and Takashi Kurosaki (2011). “Dynamics of Growth, Poverty and Inequality: Panel
Analysis of Regional Data from Thailand and the Philippines,” Asian Economic Journal, 25, 1, pp.
3-33.
Motonishi, Taizo (2006), “Why has income inequality in Thailand increased? An analysis using surveys
from 1975 to 1998”, Japan and the World Economy, 18, 4, pp. 464–487.
OECD (2008), Growing Unequal ? Income Distribution and Poverty in OECD Countries, Paris.
Ohtake, and Miki Kohara (2010), “Income Inequality,” in Higuchi Y. (eds.) Labor Market and Income
Distribution (Roudo Shijou to Shotoku Bunpai), Tokyo: Keio University Press, pp. 253-285 (inAlternative Measures of Economic Inequality and Poverty in Japan, Taiwan and Thailand

31

Japanese).
Ohtake, and Miki Kohara (2011), “Poverty Rate and Income, Financial Asset Inequality,” in Iwai, K.,
Seko, M. and Okina, Y. (eds.) Financial Crisis and Macro Economy (Kin-yu Kiki to Macro Keizai),Tokyo: Tokyo University Press, pp. 137-153 (in Japanese).
Ohtake, Fumio (2005), Inequality in Japan, Nihon Keizai Shinbunsha (in Japanese).
Ohtake, Fumio (2008), “Inequality in Japan,” Asian Economic Policy Review, 3, 1, pp. 87-109.
Oishi, Akiko (2006), “Income Inequality: Trends and Problems (Shotoku Kakusa no o-Doukou to
Mondai),” in: Kaizuka K. (eds.) Research of Economic Inequality (Keizai Kakusa no Kenkyu),
Choou Keizaisha, pp. 19-36 (in Japanese).
Tachibanaki, Toshiaki and Kunio Urakawa (2006), A Study of Poverty in Japan (Nihon no Hinkon
Kenkyu), University of Tokyo Press (in Japanese)

The Journal of Econometric Study of Northeast Asia Vol. 9, No.1, 201333

Empirical Matching Functions
and Job Search Competitions in China
Yang Liu*

Abstract
Our study bridges the gap of matching function estimation of the Chinese labormarket, and provides empirical evidence for matching functions with three heterogeneousgroups of job seekers in China. We fi nd that new hires are not only determined by thecontribution of job seekers and vacancies, but also by congestion externalities fromother groups of job seekers. Moreover, the result highlights competition among the threegroups of job seekers in the matching process, and indicates the potential influences of
productivity, job-search services, and economic reform shocks on their matchingefficiencies.

K EYWORDS : China, job-worker matching function, congestion externalities, matchingefficiencies, productivity
JEL classification: J64, O53

1. Introduction
The matching model has been used widely in labor market issues. It provides a tool for
fractional unemployment analysis, and enables the modeling of the contribution of job
seekers and vacancies to new hires in an incomplete labor market (See Diamond andMaskin (1979), Blanchard and Diamond (1989), and Pissarides (2000)). Althoughconsiderable effort has been made to estimate matching functions for numerous countries,
China has been ignored. In order to fill this gap, we specify and estimate matching models
for the Chinese labor market and consider heterogeneous job seekers in the matchingprocess.
The segmentation of job seekers is usually based on whether they are employed or
unemployed, depending on their employment status (van Ours (1995), Hynninen (2009),
etc.). In the urban Chinese labor market, there is another group of job seekers: rural-urban
migrants. They are different from employed and unemployed residents because of the strict
household registration system. They do not receive unemployment benefits because they are
employed for farm work in rural areas, and are not officially recognized as involuntary
unemployed persons even if they are unable to find employment. These migrants are more
likely to accept a job than permanent residents. Thus, numerous Chinese studies divide job
seekers in urban areas into three groups: employed, unemployed, and migrant workers(Knight and Song (1995), Xie (2008), etc.) It must be noted that unemployed persons inurban China only include unemployed urban residents. The three above mentioned jobseeker groups seek employment in the same vacancy pool and possibly compete with eachother. In our study, we examine the matching processes of each group as well as competition
among the three groups.
* Graduate School of Economics, Kyoto University, Japan; liuyang9669@gmail.com

34

The Journal of Econometric Study of Northeast Asia

The theoretical background of our study is the job-worker matching model. The
conventional aggregate matching function is a regression of new hires on unemployedpersons and vacancies1. Recently, certain studies have found that biases could arise if
employed job seekers and other non-unemployed job seekers are ignored (Broersma andvan Ours (1999), Petrongolo and Pissarides (2001), Sunde (2007)). Further, other recent
studies have noted that the conventional matching function is influenced by the proportionof heterogeneous job seekers (Hynninen 2009). Thus, in this empirical study of China, weintroduce heterogeneous factors of Chinese labor market to the conventional matching
function. Further, matching efficiencies are often influenced by exogenous factors; theestimated scales of the matching function enable us to examine the determinants ofmatching efficiencies of China.
Our data of job vacancies, job seekers, and new hires has been obtained from local
labor agencies in China. Every city has its own labor agencies that are operated by thegovernment, and certain cities also have privately-owned labor agencies. Labor agencies
provide job services not only at their public halls but also through the internet andnewspapers. The quarterly report of the Chinese labor market encompasses job agencies of
100 large cities in the country, and the annual report includes all the official and private job
agencies in all the cities of China. In this study, we use provincial data from the annual
report of the Chinese labor market. The time period for the data is 1996–2008, with a crosssection of 29 Chinese provinces.

2. Previous Studies
An empirical matching model for the Chinese labor market has not been previously
attempted. However, the job-worker matching problem of China has been mentioned incertain studies. Although China is currently experiencing an unprecedented economic
boom with high job creation, the unemployment rate continues to be high. Knight and Song(1999) found that the redundancy policies in the late 1990s lead to millions of unemployedurban workers who faced numerous difficulties in the labor market. They seemed to have
many difficulties to adapt themselves to new jobs in the market economy. The competitions
among job-seeker groups have also been observed (Xie, G. (2003)).
For the study of other countries, there has been considerable discussion on theheterogeneity of job seekers in previous studies. However, the studies that utilize empiricalmatching functions for this purpose are not common because of data limitations (Petrongolo
and Pissarides (2001)). Burgess (1993) examined the competition provided by employedjob seekers for the unemployed, using the replacement ratio, the proportion of employed
aged 16–19, and other factors to measure the propensity of the employed to engage insearch. Van Ours (1995) developed two types of matching function forms to distinguish thecase that employed and unemployed job seekers search in the same pool of vacancies andthe case that they search in different pools, using a dataset of pooled 24 observations (8regions over discrete 3 years) in Netherlands. Further, Broersma and van Ours (1999) usedapproximations for the non-unemployed job seekers (for instance, it is assumed that 10% of
In empirical literature, matches are usually assumed to be equal to new hires (Petrongolo and Pissarides (2001)).
In our study, we do not consider the job-worker contacts that do not result in employment and assume the reject rate
after contact is unrelated to job vacancy and job seekers.

1

Empirical Matching Functions and Job Search Competitions in China

35

the employed work force searches for another job). A more recent study, Hynninen (2009),
although do not have data for new hires of each job-seeker group, they introduce compositionof job-seeker groups into the total matching function, and found significant heterogeneityof job seekers in matching process. Using different methods to overcome data limitations,previous studies found that it is important to account for the behavior of non-unemployedjob seekers in empirical matching functions. This is the starting point of our study.
It is noteworthy that using registered job seeker and vacancies in local labor offices
and other public job exchanges is the most common method to collect data for matching
function estimation. Although some studies pointed out that there could be workers and job
flows outside the local labor office, a more complete dataset usually does not exist. The
results obtained by those dataset highly support the theory and usually consist with eachother even in different countries; therefore, they are widely accepted.

3. Empirical Matching Functions of the Three Job-seeker Groups
In this section, we estimate matching functions for the three job-seeker groups inChina, and further examine competition among these groups.
3.1 Model
The conventional aggregate matching function is H u = aUαVβ, where H u represents
new hires from among the unemployed, U represents unemployed job seekers, and V
represents the total notified job vacancies (Pissarides (2001)). It must be noted that theestimates could be biased if there are job seekers other than unemployed persons. Therefore,
we not only consider the contributions of job seekers and vacancies to the matching result
but also introduce variables of congestion externalities, which are important factors in thematching process. The terms of congestion externalities are based on Ibourk, etc. (2004).
The general matching functions for each job-seeker group are given below:
u

α

β

e

e α

m

m α

H = AuU u EUV u,β

H = Ae (S ) e EEV e, andβ

H = Am (S ) m EMV m,uemwhere H , H , and H represent new hires from unemployed, employed, and urban-ruralemmigrant job seekers, respectively. U, S , and S represent unemployed, employed, and ruralurban migrant job seekers, respectively. Further, Au, Ae, and Am are matching efficiencies of
unemployed, employed, and migrant job seekers, respectively.
EUV, EEV, and EMV are efficient job vacancies for unemployed, employed, andmigrant job seekers, respectively; they are defined in the following manner:

,

, and

,

where λ is significantly positive if the other two groups of job seekers cause congestion in
job vacancies. We take logarithms of the three general matching functions and use the
Taylor approximation to assume thatin EUV, as

36

The Journal of Econometric Study of Northeast Asia

Empirical Matching Functions and Job Search Competitions in China

well as similar terms in EEV, and EMV. Accordingly, the matching functions can be
expressed in the following manner2.

Table 1 Results of Matching Function Estimation for the Chinese Labor Market

Table 1 Results of Matching Function Estimation for the Chinese Labor Market(1) Dependant variable: ln H itu(a) lnHitu =αu lnUiu +βu lnVit +δue R ite +δum R itm + ciu + ctu +εitu,
e
it

e
iu

eu

u
it

em

m
it

e
i

e
t

Instruments*: ( ln U it , ln Vit ) lnSit , lnSite , lnSitueit(b) lnH =αe lnS +βe lnVit +δ R +δ R + c + c +ε , and(c) lnH itm =αm lnS itm +βm lnVit +δmu R itu +δme R ite + cim + ctm +εitm,
where

,

, and

(Sit is the total number of job seekers).

These variables can be explained as indices of the congestion externalities from othergroups of job seekers. αu, βu, αe, βe, αm, βm, δue, δum, δeu, δem, δmu, and δme arecoefficients. If other groups of job seekers cause congestion in seeking jobs, the ratios ofother groups will have negative effects on new hires; thus, their coefficients will besignificantly negative.
3.2 Dataln U itln Vit
m

S
S

Se
S

Adj.R2.
Obsers.
p-value

The Model
3SLS(1) 3SLS(2)

OLS

TSLS

0.31***

0.38***

0.38***

0.31***

0.30***

0.54***

(4.7)

(7.4)

(7.4)

(4.2)

(4.4)

(5.0)

0.42***

0.34***

0.34***

0.43***

0.43***

0.25**

(4.6)

(6.5)

(6.5)

(4.1)

(4.3)

(2.7)

-0.56*** -0.42**-0.42**-0.56**-0.53**――(-3.0)-0.34**(-2.6)
0.95
179
0.00

(-2.7)-0.24**(-2.3)
0.96
183
0.00

(-2.6)-0.34**(-2.3)
0.95
179
0.00

(-2.7)-0.39**(-2.8)
0.95
179
0.00

(-2.8)-0.24**(-2.3)
0.96
183
0.00

Comparison
GMM

3SLS*

――
0.96
179
0.00

H0 :    
1

The data for this study has been sourced from approximately 30,000 public and private
labor agencies in China (NBS (1999-2008)). The dataset provides the annual number of job
seekers belonging to different groups, flow of new hires from each group, and job vacancies
at the provincial level. The period is 1996–2008, and the analysis is conducted for a crosssection of 29 Chinese provinces3. The data for migrants has not been reported separately forthe period 1999–2004; thus, the adjusted periods are 1996–1999 and 2005–2008.
3.3 Results
In this study, we examined the endogeneity problem in the Chinese labor market using
the Durbin and Wu-Hausman tests. The null hypothesis that the variable under consideration
can be treated as exogenous is rejected in eq. (b), but not in eq. (a) and (c). Therefore, weestimate a 3SLS(1) specification with instruments in all equations and a 3SLS(2)specification with instruments only in eq. (b). Further, the relevance and exogeneity of
instruments have been examined. For the sake of comparison, we also report the results of
OLS, TSLS, GMM, as well as a specification where effects of other job-seeker groups are
ignored (in the last column of 3SLS*). The results are reported in Table 1.
The results reveal that all the job-seeker groups and vacancies have statisticallysignificant positive coefficients, and most of the congestion externality terms have significant
negative coefficients. Further, it is indicated that a greater number of job seekers or vacancies
lead to a greater number of new hires, which supports the matching theory. Moreover, thematching processes are often affected by the congestion externalities of other groups of job
seekers, which is consistent with our expectation. Furthermore, it is evident that thecomparative estimates in the last column of 3SLS* (effects of other groups of job seekers
ue S eum S mue S eum S mlogEUV = logV + log (1-λ -λ ) ≈ logV -λ -λ , and the same as logEEV and logEMV.
S
S
S
S
3
Hong Kong, Macau, Xinjiang, Tibet, and Taiwan are not included.

2(2) Dependant variable: ln H ite
Instruments: ( ln Vit ) lnSit , lnSitm , lnSitu
The Model
3SLS(1) 3SLS(2)

OLS

TSLS

0.06***(3.1)
0.89***(7.4)-5.10***(-8.8)-5.02***(-8.4)
0.87

0.05***(3.1)
0.89***(7.4)-5.10***(-8.8)-5.01***(-8.4)
0.87

0.05**(2.8)
1.18***(6.6)-5.22***(－8.9)-4.72***(-7.6)
0.86

0.06***(4.7)
1.23***(8.1)-5.30***(-11.1)-4.95***(-9.3)
0.86

0.11***(5.5)
1.29***(7.3)――

Adj.R.

0.05***(3.1)
1.17***(7.4)-5.21***(-10.0)-4.69***(-8.5)
0.86

Obser.
p-value

184
0.16

184
0.67

184
0.67

184
0.20

184
0.05

184
0.02

ln S eln Vit

Sm
S
U it
S

Comparison
GMM

3SLS*

――
0.78

H0 :    1(3) Dependant variable: ln H m

Instruments: ( ln Sitm , ln Vit ) lnSit , lnSite , lnSitu

ln S mlnVUSSe

The Model
3SLS(1) 3SLS(2)OLS

Comparison
TSLS
GMM

0.40**(2.6)
0.45***(2.9)-1.15**(-2.1)-0.59

0.52***(7.1)
0.40***(5.2)-0.74**(-1.9)-0.26

0.44**(2.5)
0.42**(2.3)-1.04*(-1.7)-0.44

0.52***(7.1)
0.40***(5.2)-0.74**(-1.9)-0.26

0.38**(2.1)
0.44**(2.2)-1.20*(-1.7)-0.70

3SLS*
0.52***(3.4)
0.45**
(2.3)
――
――

37

Obser.

38

184

184

184

p-valueof Econometric
0.16
0.67 of Northeast
0.67
0.20
The Journal
Study
Asia

184

184

184

0.05

0.02

Empirical Matching Functions and Job Search Competitions in China

3SLS*

and 71% unemployed residents) stated that competition from migrants reduces their job
opportunities. Further, congestion externalities to migrants particularly from unemployedurban residents also exist. The reason for this could be that city policies protect theirresidents and occasionally make it compulsory for enterprises to employ a certain proportionof unemployed residents (Knight and Song (2005)).
In this section we estimated the empirical matching functions of China, and confirmed
the competitions among job-seeker groups. It must be noted that the matching process is not
only influenced by congestion externalities of other job seekers, but also determined by theefficiency of job-worker matching within the group. In the next section, we examine thematching efficiencies of the three job seeker groups.

39

H0 :    1(3) Dependant variable: ln H m

Instruments: ( ln Sitm , ln Vit ) lnSit , lnSite , lnSitu

ln S m

lnV
U
S
Se
S

Adj.R.
Obser.
p-value

The Model
3SLS(1) 3SLS(2)OLS

Comparison
TSLS
GMM

0.40**(2.6)
0.45***(2.9)-1.15**(-2.1)-0.59(-1.3)
0.93
181
0.08

0.52***(7.1)
0.40***(5.2)-0.74**(-1.9)-0.26(-0.7)
0.93
185
0.32

0.44**(2.5)
0.42**(2.3)-1.04*(-1.7)-0.44(-0.9)
0.93
181
0.14

0.52***(7.1)
0.40***(5.2)-0.74**(-1.9)-0.26(-0.7)
0.93
185
0.31

0.38**(2.1)
0.44**(2.2)-1.20*(-1.7)-0.70(-1.6)
0.92
181
0.26

0.52***(3.4)
0.45**(2.3)――――

4. Determinants of Matching Efficiencies of Each Job-Seeker Group

0.92
181
0.64

Matching efficiency is defined as the technology variable in matching functions(variables Au, Ae, and Am in our model). There is no existing theoretical framework fordetermining matching efficiency, and previous studies often examined potential
determinants on the basis of the actual situation (Destefanis, S. and R. Fonseca (2007)). In
China’s case, the potential determinants could be labor productivity growth—ΔPRO—(Cahun and Zylberberg 2004), job search services provided by government and private
agencies—SEV—(Petrongolo and Pissarides 2001), and economic reform shocks—RES.
Further, the determinants of matching efficiency may also differ among the three jobseeker groups. Productivity growth could lead to difficulties in finding appropriate jobs if
the group of workers undergoes little training; on the other hand, it could benefit the groupthat undergoes special training that is demanded by new jobs. In China, an important
employment policy is to provide job training to unemployed residents. However, on theother hand, the economic reform in late 1990s destroyed millions of inefficient jobs ofurban residents, and created new jobs. This threatened the original resident workers, whileproviding opportunities to migrant workers. We use regression to examine the possible
determinants of matching efficiency for each job-seeker group.
In empirical literature, matching efficiency is usually estimated through dummy
variables of period, regions, or both (Blanchard and Diamond 1989, etc.). Accordingly, we
c +c
c +c
c +c
obtain the matching efficiency of each job-seeker group as Au = e , Ae = e , and Am = e
for employed, unemployed and migrant job seekers, respectively. We chose specification (1)for our empirical matching functions.
The data pertaining to job search services is obtained from regional job agencies, andwe use annual layoff and unemployment inflow during the economic reform period as theproxy variable of economic reform shocks. Note that economic reform came to an end in
the early 2000s; thus, the reform shocks do not influence matching efficiency after 2004.
Therefore, we divide our work into two periods: 1997–1998, which is the peak period of
economic reform with reform shocks, and 2005–2008 when the period of reform was overand there were no economic reform shocks. The observations have been recorded for across section of 29 Chinese province and the results are reported in Table 2.
M
Coefficients of ΔPROit in the equations of A it are significantly negative, whichindicates that the productivity growth has a rather significant negative effect on the matchingefficiency of migrants in both the periods. The reason for this could be that the education

H0 :    
1Notes: * endogenous variables are in parentheses.

are ignored) are biased, particularly in the unemployed and employed job-seeker groups.
Therefore, we can conclude that in the case that congestion externalities are significant, theconventional matching function form could lead to misspecification.
In this study, we also examined returns to scale since it is often of interest in studies
of matching functions. We found that the null hypothesis of constant returns to scale isrejected decisively in the matching function of unemployed job seekers; however, it cannot
be rejected in the matching functions of employed job seekers and migrants. The estimates
and test results indicate that there could be decreasing returns to scale for unemployed job
seekers (the sum of coefficients of and is less than one) and constant returns to scale for
employed job seekers and migrants.
Further, the results of our model (Specifications (1) and (2)) offer the following
indications as empirical evidence of China’s labor market. First, among the three groups,
the group of rural-urban migrants have the largest impact on the other two groups (–0.56***ue
and –0.42** in eq. (1) of lnH it , and –5.21*** and –5.10*** in eq. (2) of lnH it , Specs. (1) and(2), respectively). Second, the group of employed job-seekers is most greatly influenced bycongestion externalities (–5.21*** and –4.69*** in Spec. (1) and –5.10*** and –5.02*** inSpec. (2)). Third, externalities of employed job seekers reduces new hires from among theunemployed job-seekers, while there is no significant effect on rural-urban migrantsum(–0.34** and –0.24** in eq. (1) of lnH it and –0.59 and –0.26 in eq. (3) of lnH it ).
It is not surprising that rural-urban migrants in China greatly influence other jobseeker groups and receive few congestion externalities from employed job seekers. Firms
prefer migrants because of their lower labor and monitor costs. The survey in Guihua Xie(2008) reported that 66% of interviewed urban residents (including 63% employed residents

u
i

u
t

e
i

e
t

m
i

m
t

40

The Journal of Econometric Study of Northeast Asia

Empirical Matching Functions and Job Search Competitions in China

Table 2 Determinants of Matching Efficiency in the Chinese Labor Market

Table 2 Determinants of Matching Efficiency in the Chinese Labor Market
Period: 1997–1998
Dependent

A

AitE

AitM

PROit5.4  10 5 *(1.8)

7.9  10 5(0.6)-6.4  10 5 ***(-2.9)

SEVit

4.0  10 4 ***(4.4)-5.0  10 4(-1.2)

2.0  10 4 ***
(5.7)

-3.8*
(-1.9)
4.4**(2.8)
1.1***

18.1 **(2.0)-18.1 **(-2.6)
2.4***

2.4(1.6)-4.0**(-3.6)
1.4***

(5.7)
0.26

(3.0)
0.11

(10.5)
0.37

Variables

Uit

Indep. Varia.

RESit
RESi ,t 1
Constant
Adj.R2.

Period: 2006–2008
Dependent
Variables

AU

AE

AM

PROit3.1  10 5 **(2.6)-6.1  10 5 **(-2.1)-2.6  10 5 ***(-2.4)

SEVit

2.1  10 4 ***(5.3)-2.0  10 4 **(-2.8)

1.3  10 4 ***
(3.7)

0.9**
(11.3)
0.27

2.2***(11.2)
0.10

1.2***(16.3)
0.17

Indep. Varia.

Constant
Adj.R2.

level of rural migrants is rather low, and most of them do not receive sufficient job-training;
thus, they may find it difficult to adapt to new jobs that require higher skills and greaterUproductivity. However, ΔPROit has significant positive coefficients in the equations of A it.
It is indicated that higher productivity growth leads to a higher level of matching efficiency
of urban residents, which could be a result of the job-training subsidy provided to residents.
For the employed job-seeker group, although the productivity growth does not have asignificant effect on matching efficiency in the period 1997–1998, it causes a significant
reduction in the matching efficiency after the economic reform. This is because when there
is a growth in productivity, employed workers may find it difficult to adapt to skills
demanded by new jobs.
Further, job search services—SEVit —has rather significant positive coefficients in theunemployed and migrant job-seeker groups, which indicates that job search services in
China contribute to an increase in matching efficiency in these groups. However, it appears
that job search services do not increase matching efficiency of employed job-seekers andhave even led to a decrease in matching efficiency for this group in the period 2006–2008.
One possible reason for this is that more job-searching services encourage more on-the-job
searches, which leads to congestion within the group of employed job seekers.

41

Finally, the result indicates that economic reform shocks also influence matchingefficiencies. The direct impact is a significant negative effect on the matching efficiencies
of unemployed residents (–3.8*). The reason for this could be that residents are not able toadapt to new jobs immediately. However, the effect of reform shocks becomes positive(4.4**) over a period of time. The most important reason for this could be the re-employment
promotion policy for unemployed urban residents during the economic reform process. On
the other hand, reform shocks have an immediate positive effect on employed and ruralurban migrant job-seekers (18.1** and 2.4) as they are not threatened by job destruction andcould benefit from newly created jobs. However, this effect becomes negative over a periodof time (–18.1 ** and –4.0**) for the possible reason that the job-seekers in these groups do
not receive job-training subsidies and new jobs are given to trained unemployed residents
through government policies.

5. Conclusion
We estimated matching functions of unemployed, employed, and migrant job seekers
in urban China. We find that the number of new hires is not only determined by thecontribution of job seekers and vacancies, but also by congestion externalities from othergroups of job seekers. The estimates of congestion externalities are rather significant, andnot considering these externalities could lead to misspecification.
Further, we observed heterogeneities of the three job-seeker groups in the matchingprocess. First, the degrees of congestion externalities differ among the three groups: ruralurban migrants cause the greatest congestion externalities in other groups, and employedjob seekers receive larger congestion externalities than the other two groups. Second, theinfluences of matching efficiencies also vary greatly. Although unemployed job seekers
underwent job relocation during the economic restructuring in the 1990s, they received
most government support for skill training and re-employment. Both productivity growthand job-search services improve their matching efficiency. Moreover, migrant job seekers
also benefit from job-search services; however, their matching efficiencies decline asproductivity grows because of their low skills and inadequate job training. Further,productivity growth had a negative effect on employed job seekers in the 2000s, and thereason for this could be the lack of further job training to adapt to new jobs.
Overall, we conclude that it is important to incorporate non-unemployed job seekers
into the matching process and consider the heterogeneities of job-seeker groups in China.
Future research could include a more detailed segmentation of job seekers, effect ofendogenous job creation, and determinants of equilibrium unemployment of China.
References
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Broersma, L. (1997), “Competition between Employed and Unemployed Job Searchers: is There a
Difference between the UK and The Netherlands?” Applied Economics Letters, 4, pp. 199–203.
Broersma, L. and van Ours, J. (1999) “Job Searchers, Job Matches and the Elasticity of Matching,”

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The Journal of Econometric Study of Northeast Asia

Labour Economics, 6, pp. 77–93.
Burgess, S. M. (1993), “a Model of Competition Between Unemployed and Employed Job Searchers: an
Application to the Unemployment Outflow Rate in Britain,” Economic Journal, 103, pp. 1190-1204.
Cahuc, P. and A. Zylberberg (2004), Labor Economics, Cambridge, Mass.: MIT Press.
Destefanis, S. and R. Fonseca (2007), “Matching Efficiency and Labour Market Reform in Italy: A
Macroeconometric Assessment,” Labour, 21, 1, pp. 57–84.
Diamond, P. A. and E. S. Maskin (1979), “An Equilibrium Analysis of Search and Breach of Contract,”
Bell Journal of Economics, 10, 1, pp. 282-316.
Fahr, R. and U. Sunde (2005), “Job and Vacancy Competition in Empirical Matching Functions,” Labour
Economics, 12, 6, pp. 773-780.
Green, W. H. (2008), Econometric Analysis, sixth edition, Prentice Hall.
Hynninen, S. M.(2009),“Heterogeneity of Job Seekers in Labour Market Matching”, Applied Economics
Letters, 16, 18, pp. 1819 - 1823.
Ibourk, A., B. Maillard, S. Perelman, H. Sneessens (2004), “Aggregate Matching Efficiency: a Stochastic
Production Frontier Approach,” Empirica, 31, pp.1-25.
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p. 131.
Lindeboom, M., Jan C. Van Ours, and R. Gusta (1994), “Matching Employers and Workers: an Empirical
Analysis on the Effectiveness of Search,” Oxford Economic Papers, 46, pp. 45-67.
Mumford, K. and P. N. Smith (1999), “The Hiring Function Reconsidered: on Closing the Circle,” Oxford
Bulletin of Economics and Statistics, 61, 3, pp. 343-64.
National Bureau of Statistics of China (NBS) (1999-2008), China Labor Statistical Yearbook 1999-2008,
China Statistics Press.
Petrongolo, B. and Pissarides, C. (2001), “Looking into the Black Box: a Survey of the Matching
Function,” Journal of Economic Literature, 39, pp. 390–431.
Pissarides, C. A. (2000), Equilibrium Unemployment Theory, 2nd ed., Cambridge (MA): MIT Press.
Sunde, U. (2007), “Empirical Matching Functions: Searchers, Vacancies, and (Un-)biased Elasticities,”
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The Journal of Econometric Study of Northeast Asia Vol. 9, No.1, 2013

43

Estimating the Local Economic and Environmental Impacts of
Two Carbon Tax Policies:
the Production and Consumption-based Accountings
Ryo Eto*, Yohji Uchiyama**, Keiichi Okajima**

Abstract
We estimate effects of two carbon tax policies on economy and CO2 emissions in
Ibaraki Prefecture by combining a regional computable general equilibrium model anda two-region input-output model. The carbon tax is levied on the basis of Producer Pays
Principle (PPP) and User Pays Principle (UPP) which corresponds with the productionbased accounting and the consumption-based accounting respectively. The revenue
from the carbon tax is recycled to reduce a direct tax. Three main results are obtained
from the study. First, although the carbon tax based on the PPP attains higher CO 2
emissions reduction and lower economic loss within the prefecture than the carbon tax
based on the UPP, it is found that these are attained through carbon leakage. Second,carbon tax based on the UPP prevents carbon leakage and reduces CO 2 emissions morelargely at national level in consumption-based accounting. Third, the carbon tax
corresponding with the accounting method reduces CO2 emissions more largely. This
study has an important implication for future local emissions accounting methods andcorresponding policies.

K EYWORDS : emission account, carbon tax, carbon leakage, interregional trade, regional
computable general equilibrium model, two-region input-output model
JEL classification: C68, D58, H71, H73, P25, Q54, Q58, R15

1. Introduction
Local governments are responsible for stimulating industries and households to reduce
CO2 emissions. This is especially true in Japan where the sources of national CO2 emissions
necessarily fall under the local government. When environmental policies are implemented
unilaterally by a local government, the policy may result in increased emissions in the restof the country, in what is referred to as “carbon leakage”. Although Barker et al. (2007)
show that carbon leakage from six EU member states that implemented an environmental
tax is unlikely to be substantial, carbon leakage is more likely to occur at the local level
where there are lower transport costs and more comparable local market conditions.
To prevent carbon leakage at the local level, CO2 emissions should be accounted on thebasis of the consumption accounting principle, wherein a consuming region is responsible
for the CO2 emitted in the process of producing the energy, goods and services it consumes.
However, carbon leakage is likely to occur, given that the current accounting method forCO2 emissions is based on the production accounting principle, wherein the producing* Department of Risk Engineering, Graduate School of System and Information Engineering, University ofTsukuba** Department of Risk Engineering, Faculty of System and Information Engineering, University of Tsukuba 1-1-1,
Tenno-dai, Tsukuba Ibaraki 305-8573, Japan
Corresponding author. Tel & Fax: +81-29-853-5134;
E-mail address: r0820570@risk.tsukuba.ac.jp (R. Eto).

44

The Journal of Econometric Study of Northeast Asia

region is responsible for the CO2 it emits through the production of energy, goods, andservices. Princen (1999) discusses how consumption ranks among population andtechnology as a major driver of global environmental change. CO2 emissions are generated
through the final use of all goods and services even imported ones and in this context, anumber of studies discuss the consumption-based accounting method for setting climate
policy at the national level (Kondo and Moriguchi, 1998; Machado et al., 2001; Munksgaard
and Pedersen, 2001; Ferng, 2003; Sánchez–Chóliz and Duarte, 2004; Mongelli et al., 2006;
Peters and Hertwich, 2008). This accounting method is suitable for use at the local level
because the emissions produced in a prefecture differ from those generated to satisfy
consumption within the prefecture as a result of interregional and international trade. Thus,
discussion on consumption-based accounting has been emerging at the local level (Dodman,
2009; Larsen and Hertwich, 2009, 2010).
Environmental policy should be implemented in accordance with CO2 emissions
accounting methods. While the aforementioned references apply consumption-based
accounting, they set aside environmental policy implementation in consumption-based
accounting. A carbon tax is one of the most cost-effective public interventions in reducing
CO2 emissions, and it is simple to implement in accordance with the accounting methods(Peters and Hertwich, 2008). The carbon tax is generally levied on the basis of the Polluter
Pays Principle (PPP) according to which, the polluter should pay environmental costs.
Although the PPP has been accepted as a background principle with regard to environmental
policy, this acceptance does not reflect the idea that environmental change is a matter ofconsumption rather than solely of production. PPP is unfair for municipalities that export asignificant volume of energy-intensive goods; particularly, carbon leakage is likely to takeinterregional trade into account, along with PPP to reduce production costs.
As a consequence of distinguishing production and consumption responsibilities,
there arises the idea of the User Pays Principle (UPP), which advocates that the user ofcommodities should pay the environmental costs arising from their production. Thus,
products should be taxed in the region where they are consumed rather than in the region
where they are produced, unless they are also consumed there. Levying a carbon tax basedon different principles impacts economic and CO2 emissions in different way. Therefore, aconsideration of UPP points to other new issues in municipal areas, such as the emissions
embodied in interregional trade, carbon leakage, and an analysis of regional consumption
versus emissions.
In this context, this study evaluates two carbon tax policies along with a reduction ina direct tax implemented unilaterally by Ibaraki Prefecture; this study applies a regional
computable general equilibrium (CGE) model to estimate their economic and environmental
impacts on Ibaraki Prefecture in the year 2005. Revenue from the carbon tax is used toreduce a direct tax to maintain constant tax revenue. The replicated results are input in a
two-region input-output (IO) model for Ibaraki Prefecture and the rest of Japan (ROJ), toestimate the impacts on CO2 emissions according to two accounting methods. The firstcarbon tax policy is based on the PPP, which places a burden on production processes; thesecond one is based on UPP which places a burden on the entire supply side, including
imports and transfers from the ROJ. CO2 emissions are indicated under the two accounting
methods namely, production-based accounting and consumption-based accounting to
identify carbon leakage that derives from the two carbon tax policies. This paper

Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

45

complements the existing literature from a policy perspective by providing a set of results
derived by combining accounting methods and carbon taxation methods.

2. Methodology
In the context of this study, economic and environmental impacts associated with acarbon tax, calculated on the basis of PPP and UPP are estimated. CO2 emissions areaccounted on the basis of production-based accounting and consumption-based accounting.
Turner et al. (2007) argue that a multi-region input-output (MRIO) model is the mostappropriate method by which to estimate CO2 emissions when using consumption-based
accounting. Wiedmann et al. (2007) and Wiedmann (2009) review MRIO models usedwith consumption-based emissions. However, Kainuma et al. (2000) find that the IO model
overestimates CO2 emissions caused by structural changes, whenever demand and supply
behaviors change simultaneously due to an assumedly passive supply and the universal
Leontief technology. A CGE model is a more suitable tool for estimating the impacts oftaxes and recycling revenue, both of which stimulate supply and demand simultaneously
and provoke structural changes. Thus, many research studies apply a CGE model to estimate
the impacts of a carbon tax and carbon leakage (Gerlagh and Kuik, 2007) and there areseveral applications to Japan at the national level (e.g. Park, 2004; Takeda, 2007). Partridgeand Dan (1998, 2007) review applications of the CGE model to a wide range of regional
economic development policy at the local level but there has been a dearth of study of theenvironmental application of regional CGE models. Although André et al. (2005) and De
Miguel-Vélez et al. (2009) each examine the application of the regional CGE model to acarbon tax, they neglect impacts on other parts of the country.
Given this background, we apply two models, a regional CGE model and a two-region
IO model in this study. First, we apply the regional CGE model to estimate the economic
impacts of a carbon tax and recycling revenue at the prefectural level. Second, the calculated
results are input into a two-region IO model to estimate the impacts of a carbon tax oninterregional CO2 emissions. In the following subsections, the models used in this study are
explained.
2.1 Regional CGE model
The CGE model is ideally suited to estimate the impacts of a carbon tax, owing to its
multi-sector scope and sensitivity to changes in an economy’s supply-side dimension. The
CGE theory employed in this study is based on the work of Dervis et al. (1982), and It
assumes that the market is fully competitive; that is, all economic entities are price takers.
The model demands market equilibrium, a relationship of perfect equality between demandand supply in each market. Fig. 1 provides an overview of the flow of goods and related
factors in the regional CGE model.
The producer makes decisions driven by his pursuit of profit maximization conditioned
by his technological capacity. On the first nest level, the combination of labor and capital isdetermined by the Cobb Douglas function which takes into account substitution. This isexpressed in equation (1)46

The Journal of Econometric Study of Northeast Asia

Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

Figure 1 Overview of the flow of goods and related factors in the regional CGE model

Figure 1 Overview of the flow of goods and related factors in the regional CGE model
Export to ROWTransfer to ROJ

Total demand
Armington

Total export

Local demand

Total import

CET
Total production

Transfer from ROJ

Import from ROW

Leontief

CET function substitution elasticity for the exports of commodity com to the ROW and thetransfers of commodity com to the ROJ; QEDcom, the quantity of the transfers of commodity
com to the ROJ; and QE com, the quantity of the exports of commodity com to the ROW.
On the fourth nest level, total demand is supplied by the local supply, which comprises
both local demand and total imports that in turn comprise transfers from the ROJ andimports from the ROW as determined by the Armington function (Armington, 1969).
These are explained in equations (4) and (5)1

QQcom = aqcom[deltaqcom × QMD com + (1-deltaqcom) ×QD com ] ϑ (4)-ϑcom

-ϑcom -com

1

QMDcom = arcom[deltarcom × QNcom + (1-deltarcom) ×QM com] ρ (5)-ρcom

Goods nValue added47

ρcom -com

Cobb Douglas
Labor

Capitalα
QAact = adactLαact K1
act
act

(1)

act

where QAact represents the quantity of output in activity act; adact, the Cobb-Douglas
function shift parameter for the production factor in activity act; Lact, the quantity of thelabor in activity act; Kact, the quantity of the capital in activity act; and αact, the CobbDouglas parameter for the production factor in activity act, which presents consistently
with the share of Lact in value-added. Labor units can float among sectors, and their wages
are set as the adjustment variable. On the other hand, capital cannot float among sectors;
increased capital demand is absorbed by the concomitant increase in the capital price.
On the second nest level, the combination of value-added and goods is determined by
way of the Leontief function, in which the intermediate input among sectors is notsubstitutable and only the primary input-intermediate input ratio changes.
On the third nest level, total production is supplied to either local demand or exportsthat comprise exports to the rest of the world (ROW) and transfers to the ROJ as determinedby the Constant Elasticity Transformation (CET) function. These are explained in equations(2) and (3);1

τcom

τcom τ
com

QXcom = atcom[deltatcom × QED com + (1-deltatcom) ×QD com] 

(2)
1
-

QEDcom = aucom[deltaucom × QECνcom + (1-deltaucom) ×QEνcom]ν
com

com

com

(3)

where QXcom represents the quantity of commodity com; atcom, the CET function shiftparameter of commodity com; deltatcom, the CET function share parameter of commoditycom; τcom, the CET function substitution elasticity of commodity com; QEDcom, the sum of
the exports of commodity com to the ROW and the transfers of commodity com to the ROJ;
QDcom, the local demand for the local production of commodity com; aucom, the CETfunction shift parameter for the exports of commodity com to the ROW and the transfers ofcommodity com to the ROJ; deltaucom, the CET function share parameter for the exports ofcommodity com to the ROW and the transfers of commodity com to the ROJ; νcom, thewhere QQcom represents the quantity of the supply; aqcom, the Armington function shiftparameter; deltaqcom, the Armington function share parameter of commodity com; ϑ, theArmington function substitution elasticity of commodity com; QMDcom, the sum of theimports of commodity com from the ROW and the transfers of commodity com from the
ROJ; ar, the Armington function shift parameter for the imports of commodity com fromthe ROW and the transfers of commodity com from the ROJ; deltarcom, the Armington
function share parameter for the imports of commodity com from the ROW and thetransfers of commodity com from the ROJ; ρcom, the Armington function substitution
elasticity for the imports of commodity com from the ROW and the transfers of commodity
com from the ROJ; QNcom, the quantity of the transfers of commodity com from the ROJ;
and, QMcom, the quantity of the imports of commodity com from the ROW.
The model uses one representative household as a factor of private consumption. The
household income comes from labor wage, capital income, a government transfer and aremittance from the ROJ and the ROW. The household pursues utility maximization in
consumption, subject to budgetary constraints. The households’ demand is determinedaccording to its disposable income and marginal savings rate, while a ratio of sectoral
demand is fixed in nominal terms. The household demand is estimated by equation (6)P QcomHcom = βcom (1-mps)[(1-ty) Y-prpay-remitpay](6)where P Qcom represents the supply price of commodity com; Hcom, the demand of thehousehold of commodity com; βcom, a ratio of sectoral demand of the household to total
demand of the household of commodity com; mps, the marginal propensity to save; ty, thedirect tax rate; Y, the income of the household; prppay, the property payment; and, remitpay,
the remittance to the ROJ and the ROW. The sectoral investment and the government
expenditure ratio is fixed in real terms.
2.2 Carbon taxes
A carbon tax is included in this model to estimate its impacts. The carbon price isdifferentiated among commodities in accordance with the different CO2 emissions
generated by producing each commodity. The tax is levied on production prices in the caseof PPP, based on the CO2 emissions of each industry; this indicates the increase of the local

48

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Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

production price, the price of transfers to the ROJ, and the price of exports to the ROJ. Thus,
the responsibility for reducing emissions falls on the producing region. This is expressed inequation (7);PXcomQXcom (1+ttcom) = PDcomQDcom + PEDcomQEDcom (1+ttcom)

(7)

where PXcom represents the production price of commodity com; tt, the carbon tax rate ofcommodity com; PDcom, the local production price of commodity com; QDcom, local
demand for the local production of commodity com; and PEDcom, the price of the exportsof commodity com to the ROW and transfers of commodity com to the ROJ. In the case of
UPP, the tax is levied on purchase prices that correspond to the sum of the local production
price, the price of the transfers from the ROJ, and the price of the imports from the ROW.
This indicates that the carbon tax is transferred among domestic commodities, the importof commodities from the ROW, and the transfer of commodities from the ROJ according to
the IO coefficients. The responsibility for reducing emissions thus falls on the consuming
region. This is expressed in equation (8);PQcomQQcom (1+tqcom +ttcom) = (PDcomQDcom +PMDcomQMDcom) (1+tqcom +ttcom)(8)where tqcom represents the indirect tax rate of commodity com, and PMDcom the price of thesum of the imports of commodity com from the ROW and the transfers of commodity com
from the ROJ. Thus, the regions that transfer to the ROJ and export to the ROW rebate taxes
levied on the products when they are exported, while the regions that transfer from the ROJ
and import from the ROW levy the carbon tax on imported products.

49

the power generation, gas and heat supply sector; and the petroleum sector. The product of
C and TF-M̂F+E corresponds to direct CO2 emissions. The difference between total CO2
emissions and direct CO2 emissions corresponds to indirect CO2 emissions in this study.
“Total emissions” indicates only emissions in the prefecture associated with activity
in the prefecture; CO2 emissions generated from exports to the ROW are included in bothtypes of production-based CO2 emissions. This excludes CO2 emissions driven by the ROJ
and driven into the ROJ. In the same way, CO2 emissions generated by imports from the
ROW are excluded in consumption-based CO2 emissions; this relates to international
negotiations, such as those inherent in the Kyoto Protocol, the reduction targets of which
are accounted in production-based accounting. Thus, both consumption-based accounting
and production-based accounting refer to domestic CO2 emissions in this study.
2.4 Households CO2 emissions
CO2 is emitted by a household through the consumption of kerosene for heating andgasoline for private car uses. This consumption is affected by the carbon tax and the use ofrevenue from the carbon tax. Household CO2 emissions are estimated from private
consumption in the corresponding sector; as shown in equation (10)CO2hou = Chou ×P QQ

(10)where Chou represents CO2 emissions per unit of private consumption. Thus, household CO2
emissions change in accordance with changes in the private consumption of electricity andpetroleum products that are driven by the carbon tax and the use of revenue from the carbon
tax.

2.3 Two-region IO models
The IO model is a widely applied and useful tool for economic analysis at many
geographic levels, including local, regional, national and international. The interregional
IO model allows us to capture interregional and intersectoral flows of CO2 emissions
associated with products and services. We apply a two-region IO model to estimate CO2
emissions in the prefecture namely, those driven by the ROJ and those driven into the ROJ
to substitute the value calculated by the regional CGE model. Miller and Blair (2009)
provide a complete description of the method and the detail of the mathematics of theregional IO model. In this study, the method for calculating total CO2 emissions is indicatedas in equation (9)CO2ind = CindX = Cind (I-TA+M̂A) -1 (TF-M̂F+E)

2.5 CO2 emissions from electricity generation
CO2 emitted from electricity generation is distributed to each final consumer according
to the amounts of electricity consumed under laws that help promote measures to mitigate
global warming. We represent this in equation (11)CO2ele = ε× ele(11)where CO2ele represents CO2 emissions from electricity for each industry and household; ε,CO2 emission coefficients for electricity; and ele, the electricity consumption of eachindustry and household.

(9)

where Cind represents CO2 emissions per unit of production; I, the unit matrix; T, theinterregional trade coefficient; A, the input coefficient; M̂, the import coefficient; F, thefinal demand; and E, the quantity of exports to the ROW. We add direct CO2 emitted fromhouseholds and private automobiles through the use of electricity, heating oil, and gasoline
to the direct CO2 emissions of the prefecture. We estimate changes in household and private
automobile CO2 emissions as being in proportion to changes in the private consumption of

3. Data and cases
The objective region is set as Ibaraki Prefecture. Given the location of an adjacent areaof the metropolis, the prefecture mainly comprises energy-intensive industries that satisfy
the demands of the ROJ and the ROW. The strong energy-intensive base of the economy
generates a high concentration of CO2 emissions in a production process that originatesfrom transfers to the ROJ and exports to the ROW. The emissions accounting method is

50

The Journal of Econometric Study of Northeast Asia

largely relevant to the prefecture. The following subsections explain the data used and thecases developed in this study.
3.1 Economic data
The base year is 2005, the year of the most recent available IO table. The base dataconsist of a prefectural social account matrix (SAM) that depicts monetary interactions
among whole economic entities, and comprises an IO table. The SAM is constructed by
combining an Ibaraki Prefecture IO table with prefectural economic accounting, both ofwhich are derived from the Ibaraki Prefectural Government (2005 and 2006, respectively).
The IO tables for Japan cover the fiscal year, whereas the economic accountings cover thecalendar year. Combining these two datasets requires adjustments, if one is to keep the sumof raw goods and the SAM column consistent. This study first derives the total value ofintermediate inputs, value-added, intermediate demand and final demand from theprefectural economic accounting. Second, it derives their sectoral share from the prefecture
IO table, in order to estimate each sectoral value. Finally, it uses the RAS method to makethe sum of raw consistent with the sum of the column for all sectoral interactions. The
industry is disaggregated into 28 sectors in accordance with the prefectural economic
accounting.
There exist no data concerning substitution elasticity between the ROJ and the ROW.
Therefore, the Armington and CET substitution elasticity of local and ROJ commodities;
the ROJ and ROW commodities are obtained from Lee (1999). This study applies the ruleof two, in which the elasticity of substitution across imports by sources is set to equal twice
the elasticity of substitution between domestic goods and imports, as validated by Liu et al.
(2003); this serves as the substitution elasticity between the ROJ and the ROW. Although
this assumption seems ad hoc, these parameters do not affect the analytical results, since
the relative price between the ROJ and the ROW is constant. The indirect tax rate, themarginal propensity to save, and the direct tax rate are calculated from the SAM data. Nonhousehold consumption is integrated into household consumption for the demand side andinto labor for the supply side. The net operating surplus is integrated into the capital, and in
the SAM, the sum of the indirect tax and the current subsidy is expressed as an indirect tax.
The local government, the social security funds, the outpost agency, and the central
government are aggregated as a single government body since they behave in economicallyanalogous ways. This study does not employ data concerning borrowing from the ROJ andthe ROW, or foreign investment; rather, these are calculated as the difference between raw
and column of the SAM.
3.2 Environmental data
The national amount of sector-based energy consumption is available from the energybalance table compiled by the Agency for Natural Resources and Energy (2007). To convert
energy consumption into CO2 emissions, we multiply the CO2 emissions coefficientscompiled by the Ministry of Environment (2010). CO2 emission coefficients of lubrication
are set to zero, under the assumption that no lubrication is used as energy. Also, CO2
emissions coefficients of naphtha are set to zero since the consumption of naphtha occurs in

Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

51

non energy-related contexts. We use region specific information for the CO2 emission
coefficients of electricity. The CO2 emission coefficients of electricity of Ibaraki Prefecture
are derived from the Tokyo Electric Power Company which supplies electricity to theprefecture. We apply national average coefficients of electricity to derive the CO2 emission
coefficients of electricity for the ROJ. We adopt a short-term approach, so that the production
technology and pollution intensity of all sectors are assumed to be fixed.
3.3 Carbon tax and cases
A carbon tax forces the market to internalize the cost of generating CO2 emissions.
The total cost is minimized with the internalized external cost of CO2 emissions because
each polluter is free to choose the most efficient way of complying with environmental
targets. In Japan, discussions of the rate of a carbon tax are as wide-ranging as those of how
it should be implemented. At the local level, the Canadian province of British Columbia in
2008 introduced a carbon tax of CAD 30 per ton of CO2 emissions (Duff, 2008). Japan has
discussed the introduction of a carbon tax at both the national and prefectural levels. The
nonprofit organization Japan Center for a Sustainable Environment and Society (JACSES)
maintains that an efficient carbon tax rate ranges from JPY1,600 to JPY 4,000 yen per tonof CO2 emissions. On the basis of these discussions, we set a tax rate of JPY 3,000 per tonof CO2 emissions in this study.
As to the use of carbon tax revenue, we select a reduction of a direct tax. According tocurrent law, direct tax is relevant to the local tax system. We expect that the carbon tax
would create negative incentives for production, particularly among energy-intensive
industries, and that revenue from the carbon tax would have positive effects on the disposable
incomes of households.
We examine two different policies namely, the PPP case and the UPP case to identify
the economic and environmental impacts of the different taxation methods. The cases
introduce two different carbon taxation methods by two different payer principles. In the
PPP case, the carbon tax is levied on the commodities that the industries in Ibaraki
Prefecture produce. In the UPP case, meanwhile, the carbon tax is levied on the commodities
that the industries and households in Ibaraki Prefecture consume.

4. Results
Economic and environmental impacts are evaluated by combining the regional CGE
model and the two-region IO model when the carbon tax is imposed on Ibaraki Prefecture,
based on the two different payer principles. First, we focus on the impacts of the carbon tax
on the prefectural economic. Second, we identify the impacts of the carbon tax on CO2emissions in Ibaraki Prefecture and interregional CO2 emissions with ROJ.
4.1 Economic impacts
Table 1 shows the economic impacts on Ibaraki Prefecture. While private consumption
and investment increase by JPY 1,790.8 and JPY 8,342.2 million, respectively in the PPP
case, they decrease by JPY970.8 and 40,946.5 million in the UPP case. This difference

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The Journal of Econometric Study of Northeast Asia

Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

occurs for two reasons. First, in the UPP case, the share of taxation is large for the tertiaryindustries, which largely supply private consumption and investment. Energy-intensive
industries mainly export to the ROW and transfer to the ROJ, whereas the tertiary industries
largely transfer from the ROJ in the prefecture. Second, reducing the direct tax increases
the disposable income of a household and thus contributes to an increase in private
consumption. Revenue from the carbon tax is higher in the PPP case than in the UPP casein accordance with the share of the taxation among industries.
While labor decreases by JPY16,200.0 and JPY22,471.0 million in the PPP case and
UPP case respectively, the real gross regional production (GRP) decreases more moderately,
by JPY60.0 and JPY5,571.5 million, respectively. The carbon tax decreases both the real
GRP and labor, through by incrementally increasing the production costs. The reduced
direct tax contributes to an increase in private consumption and GRP; however, it also
contributes a small amount to labor. Although the carbon tax and the recycling carbon tax
revenue stimulate a structural switch from energy-intensitive industries to tertiary industries,
the overall real GRP decreases in both cases.
Table 2 shows the trade impacts on Ibaraki Prefecture. While transfers to the ROJ andexports to the ROW decrease by JPY10,501.5 and JPY2,071.5 million in the PPP case, they
increase by JPY20,178.9 and JPY4,269.1 million in the UPP case, respectively. The
increased production costs associated with the carbon tax reduce the prefecture’s
competitiveness in the PPP case. In the UPP case, however, local demand including
1 Economicimpacts
impacts on
Prefecture(JPY(JPY
million)TableTable
1 Economic
onIbaraki
IbarakiPrefecture
million)PPP

UPP

Private consumption

1,790.8-970.8Investment

8,342.2-40,946.5

Real GRP-60.0-5,571.5-16,200.0-22,471.0

intermediate demand decreases on account of the carbon tax. This encourages industries totransfer to the ROJ and export to the ROW given the relatively higher demand in the UPP
case.
The transfers from the ROJ and the imports from the ROW decrease by JPY1,582.3
and JPY1,105.6 million in the PPP case and JPY13,555.6 and JPY259.4 million in the UPP
case. The reduced local demand induced by the carbon tax, leads to lower demand outside
the prefecture, in both cases. While the lower rate of imports from the ROW is larger in the
PPP case, the rate of the transfers from the ROJ is larger in the UPP case. The imported
goods from the ROW comprise mainly material and machinery goods consumed byindustries as intermediate goods; on the other hand, the goods transferred from the ROJ
comprise mainly the tertiary industries that are mainly consumed by the household. Theintermediate demand decreases in the PPP case, and production costs are higher. Private
consumption decreases, as explained for the UPP case. Thus, different propensities areshown between imports from the ROW and transfers from the ROJ between the two cases.
4.2 CO2 emissions
Table 3 shows changes in CO2 emissions in Ibaraki Prefecture. Total CO2 emissions
decrease by 6,773.5 tCO2 and 3,048.6 tCO2 in the PPP case and UPP case respectively,
owing to the carbon tax. The carbon tax and recycling revenue derived from the carbon tax
clearly bring about a decrease in total CO2 emissions. In the UPP case, indirect CO2
emissions increase by 6,620.1tCO2, whereas the direct CO2 emissions decrease by
9,668.7tCO2. Increased prices of imports from the ROW on account of the carbon tax bringabout a relatively lower demand for imported goods than local goods in the production
sectors. Increased intermediate demand for local goods results in a lower import ratio anda higher self-sufficiency rate, both of which increase indirect CO2 emissions in the UPP
case. While the imports from the ROW decrease in the PPP case, the indirect CO2 emissions
3 Changes
CO2emissions
emissions in
Prefecture(tCO2) (tCO )Table Table
3 Changes
in inCOinIbaraki
IbarakiPrefecture
2
2
PPP

Labor

UPP

Total CO2 emissions-6,773.5-3,048.6

2 Tradeimpacts
impacts on
on Ibaraki(JPY(JPY
million)TableTable
2 TradeIbarakiPrefecture
Prefecture
million)Direct CO2 emissions-5,690.4-9,668.7

PPP

Indirect CO2 emissions-1,083.0

6,620.1

UPP

53

Transfers to ROJ-10,501.5

20,178.9

CO2 emissions driven by ROJ-598.4720.9

Exports to ROW-2,071.5

4,269.1

CO2 emissions driven into ROJ

3,865.8-15,477.9

Transfers from ROJ-1,582.3-13,555.6

Imports from ROJ-1,105.6-259.4

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Estimating the Local Economic and Environmental Impacts of Two Carbon Tax Policies: the Production and Consumption-based Accountings

decrease in the UPP case. Local goods used to satisfy intermediate demand decrease involume to a relatively greater extent than import goods from the ROW owing to the carbon
tax and based on the PPP; consequently, the import ratio increases in the PPP case. Although
the GRP falls more markedly in the UPP case, the decrease in CO2 emissions is lower thanin the PPP case. This indicates that the carbon tax on the basis of the UPP is more distorted
and inefficient with the current accounting method.
Changes in the CO2 emissions, as driven by the ROJ, are determined by changes intransfers to the ROJ. Thus, with regard to changes in transfers to the ROJ, CO2 emissions
driven by the ROJ decrease by 598.4 tCO2 in the PPP case while they increase by 720.9
tCO2 in the UPP case (Table 2). Changes in CO2 emissions driven into the ROJ are
determined by changes in transfers from the ROJ. However, while transfers from the ROJ
decrease in the PPP case -as is shown in Table 2- the CO2 emissions driven into the ROJ
increase. The machinery industry, which intrinsically induces CO2 emissions across borders
through intermediate inputs from energy-intensive industries, increases transfers from the
ROJ. The machinery industry impacts on the transfers from ROJ in monetary terms, but toonly a very small extent. CO2 emissions driven into the ROJ increase, in contrast with thedecrease in transfers from the ROJ. Increases in CO2 emissions in the ROJ on account of the
carbon tax in the prefecture indicate that carbon leakage occurs in the PPP case. On theother hand, carbon leakage is prevented in the UPP case, as CO2 emissions driven into the
ROJ decrease.
Table 4 shows the change in CO2 emissions in Ibaraki Prefecture based on two different
types of accountings; production-based accounting and consumption-based accounting. In
production-based accounting, the prefecture is responsible for the sum of the total CO2
emissions and CO2 emissions driven by the ROJ. In consumption-based accounting, theprefecture is responsible for the sum of the total CO2 emissions and CO2 emissions driven
into the ROJ. CO2 emissions decrease by 7,371.9 tCO2 in the PPP case and 2327.7 tCO2 in
the UPP case, when we account for CO2 emissions through the use of production-based
accounting; with consumption-based accounting, these items decrease by 2,907.7 tCO2 and18,526.6 tCO2 respectively. Thus, we find that a larger decline in CO2 emissions is observed
when a carbon tax is imposed in line with the emissions accounting. When a carbon tax is
imposed in a way different than that consistent with emissions accounting, the reduction
effect is decreased to one-third in the production-based accounting and one-seventh inconsumption-based accounting. This is due to the fact that transfers from the ROJ increasein the PPP case and transfers to the ROJ increase in the UPP case.
Local governments are required to choose a method of accounting for prefecture-level
CO2 emissions and take appropriate actions. The decrease in CO2 emissions in the
4 Changes
CO2 emissions
emissions basedon ondifferent
formsformsof accounting(tCO2) (tCO )baseddifferentof accounting
TableTable
4 Changes
ininCO
2
2
PPP

55

consumption-based accounting and in the UPP case is larger than that seen in productionbased accounting and in the PPP case. Since regions outside the prefecture remainunchanged, the change reflects only the impacts of the carbon tax imposed by the prefecture.
This explains how a decision by one prefecture can have knock-on effect on other prefectures.
4.3 Varying Armington function substitution elasticity and CET function substitution
elasticity
In this analysis, Armington function substitution elasticity and CET function
substitution elasticity are key determinants of the impacts of two different carbon tax
policies. However, these parameters are borrowed from a previous study (i.e., Lee,(1999)).
These parameters are estimated differently, and the resulting uncertainty might affect theresults obtained above. Sensitivity analysis is conducted for these parameters. CO2 emissions
for two tax policies, based on two accounting principles, are calculated with a 10% increasein these parameters and are shown in Table 5 and Table 6.
CO2 emissions decrease as Armington function substitution elasticity increases, since
the imports from the ROW increase. However, the impacts thereof are negligible since theArmington function substitution elasticity of all sectors, as estimated by Lee (1999),
exceeds 1.0. On the other hand, a 10% increase in CET function substitution elasticity
affects CO2 emissions to a greater extent due to the fact that CET function substitution
elasticity never exceeds 1.0, as estimated by Lee (1999). CO2 emissions increase as per bothaccounting methods when exports increase, as they respond more to the carbon tax. The
increase in the UPP case is larger, due to a larger increase in exports; this is consistent withthe results of analysis in subsection 4.1.
The sensitivity analysis generates the same observation as that in the analysis insubsection 4.2. This implies that the discussed results can be found regardless of Armington
function substation elasticity or CET function substitution elasticity.
5 Impacts
10%increase
increase ininthe
Armington
substitution
elasticity(tCO2) (tCO2 )TableTable
5 Impactsof ofa a10%
theArmington
substitution
elasticity
PPP

UPP

Production-based accounting-7,372.0-2,328.4

Consumption-based accounting-2,907.8-18,527.4

Table66 Impacts
a 10%increasein theinCET
elasticity elasticity(tCO2)
Table
Impactsofof
a 10%increase
thesubstitution
CET substitution(tCO2)
PPP

UPP

UPP

Production-based accounting-7,371.9-2,327.7

Production-based accounting-7,320.1-1,976.3

Consumption-based accounting-2,907.7-18,526.6

Consumption-based accounting-2,847.4-17,927.3

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The Journal of Econometric Study of Northeast Asia

5. Conclusion
This study addresses issues relating to carbon leakage in order to evaluate twoalternative carbon tax policies at the local level. We develop a theoretical methodology toestimate the effects of a carbon tax based on the two different emissions accounting
methods by combining a regional CGE model and a two-region IO model for the case of
Ibaraki Prefecture. In addition, we take into account recycling revenue from the carbon tax.
The following three observations are drawn from the results.
First, real GRP decreases more moderately and total CO2 emissions decrease to agreater extent in the PPP case. This reflects the industrial characteristic of Ibaraki Prefecture
which contains a high share of energy-intensive industries and where the tax would be
imposed on a relatively larger scale in the PPP case. This would contribute to a decrease inCO2 emissions there. Moreover, revenue from the carbon tax would be used to reduce thedirect tax; as a result, private consumption would increase, which would in turn accelerate
a structural switch to tertiary industries ultimately leading to a moderate decrease in real
GRP.
Second, although the PPP case indicates higher real GRP and lower total CO2
emissions than the UPP case, these are attained through carbon leakage. While transfers to
the ROJ decrease in the PPP case, they increase in the UPP case, moreover, transfers fromthe ROJ decrease on a larger scale in the UPP case than in the PPP case. Accordingly, CO2
emissions driven by the ROJ decrease in the PPP case and increase in the UPP case, andCO2 emissions driven into the ROJ increase in the PPP case and decrease in the UPP case.
In this sense, it is identified that the total CO2 emissions are reduced through carbon
leakage, making unilateral policies vulnerable in the PPP case. In addition, it has been
proved that imposing a carbon tax based on UPP prevents carbon leakage.
Third, CO2 emissions decrease to a greater extent with consumption-based accounting
in the UPP case. CO2 emissions in consumption-based accounting in the UPP case arelower than those in production-based accounting in the PPP case. In this sense, imposing acarbon tax based on the UPP is more effective than that based on the PPP, when a carbon
tax is imposed in accordance with the accounting methods; this is due to the fact thatcarbon leakage is prevented in consumption-based accounting. This occurs irrespective ofArmington substitution elasticity and CET substitution.
The results have important implications, not only from economic and environmental
viewpoints, but also from the political viewpoint. CO2 emissions are currently accounted
on a PPP basis at the local level, although this entails carbon leakage. This is due to the factthat a carbon tax on the basis of UPP incurs high political costs. Moreover, any decision by
one prefecture will have a knock-on effect on other prefectures. Since the consumers of aprefecture where a local carbon tax is imposed on a UPP can easily travel to other prefecturesto acquire high carbon content commodities, the carbon tax cannot prevent carbon leakage
at the local level, unlike with international trade. To prevent carbon leakage, first,
consumption-based accounting should be established in collaboration with other local
governments. Second, local governments need to implement policy that reduces CO2
emissions on the basis of UPP to decrease national CO2 emissions.
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