Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

APPLICATION TO THE DEVELOPING COUNTRYOF SCGE MODEL BASED ON 2-REGIONAL SAM
Shoshi MIZOKAMI
Professor
Department of Civil Engineering
Kumamoto University
2-39-1, Kurokami, Kumamoto,
860-8555 Japan
Fax: +81-96-342-3507
E-mail: smizo@gpo.kumamoto-u.ac.jp

Motoki ITOSE
Consultant
Maeda Corporation
10-26 Fujimi 2-Chome,
Chiyoda-ku , Tokyo
102-8151, Japan
Fax: +81-42-380-5771
E-mail: mustang_motoki@hotmail.com

Cristela Goce-DAKILA
Associate Professor
Economics Department
De la Salle University
2401 Taft Avenue, Manila 1004,
Philippines
Tel/Fax: +63-2-303-0867
E-mail: dakilac@dlsu.edu.phAbstract: Recently, developing countries have gradually been achieving economic
development, at the cost of regional income disparity between urban and rural areas. One
factor contributing to this is excess of transport infrastructure investment in central area andconcomitant inefficiency of inter-regional trade due to high transport costs. In these countries,
decision making on project implementation should be based not only on social efficiency
from a macro point of view but also on the micro-economic effects felt by every region andevery household. A spatial computable general equilibrium (SCGE) model, which uses
Social Accounting Matrix (SAM) with two regions and two household-income levels as the
database, is built to estimate the benefit of each region and household level from traffic
infrastructure investment. Results show that traffic infrastructure investment in urban centerscauses negative benefit in rural areas and induces further widening of the regional income
gap.
Key Words: Economic Impact, SCGE, multi-regional SAM, Developing Country
1. INTRODUCTION
The main objective of this paper is to analyze the impact of transport infrastructure
investment on inter-regional economic activity and regional economic welfare. Specifically,
it aims to: (1) discuss the importance of regional SAM in analyzing welfare effects oftransport infrastructure investment. (2) Build a SCGE model of National Capital Region(NCR) & Rest-of-the-Philippines (ROP) based on the 2-region SAM database. (3) Introduce
transportation capacity restrictions in the model via an impedance function. (4) Simulate theimpact of lower transport cost via improved transport infrastructure investment on welfare
levels of households. It will utilize a 2-region SAM and an SCGE model as analytical tools
for looking into welfare distribution effects of transport infrastructure investment projects.
To achieve the aforementioned objectives, the second section will give a basic exposition of
the creation of a 2-region SAM .The third section will present a SCGE model based on a
2-regional SAM with 2 income level household grouping for each region. The fourth section
will present simulation results of the welfare effects of improvement of transport

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

infrastructure investment on each region individually and the two-interconnected regions.
Spatial computable general equilibrium (SCGE) models evolved from the early work on
computable general equilibrium models pioneered by Shoven & Whalley, Leontief, Harberger,
and Scarf. However, modelers and empiricists felt the need to introduce space so that thelocational impact of transport policies can be completely captured. Multi-regional framework
was then adopted and the once frictionless and perfectly competitive model, now expanded toconsider transaction costs and imperfectly competitive market structures. Generally, SCGE
models assume constant returns to scale, but have of scale. (Dixit & Stiglitz: 1977).
Eventually firms, households, goods and factors are distinguished by location andtransportation costs are integrated into the model.
Most of the SCGE models developed have been in Europe, USA, Japan and other developed
economies. In Europe, Brocker developed CGEurope in 1999 which covered 800 regions.
Brocker’s model quantified regional welfare effects of transport related andfinancial-economic policies like Trans-European Networks (TENs) investments and transport
pricing. Other European SCGE models developed include the BROBISSE model for
Denmark (Caspensen et al 2000; the PINGO model in Norway (Ivanova et al 2002); theRAEM model for the Netherlands (Knaap & Oosterhaven: 2002). Other SCGE models
include those developed in Sweden (Hussain & Westin: 1997; Nordman: 1998 and Sundberg:
2002). In the USA, the most recent interregional CGE model with transport sector was thatdeveloped by Lofgren and Robinson in 1999. In Asia, Miyagi (2001) created an SCGE
model to assess the direct and indirect economic effects of a major highway link in central
Japan. Other SCGE models for Japan include those constructed by Koike et al in 2000 and
Ueda et al in 2001 which sought to analyze the economic impact of a major earth quake
which damaged the high speed rail network to Tokyo. For Korea, the work of Hewings andKim on the regional welfare impact of highways utilized an SCGE model (1999). For Brazil,
Haddad and Hewing created a spatial CGE model with a transport sector under imperfectlycompetitive markets (2003). For China, the work by Li, utilized an interregional CGE model
to determine the interrelationship among economic growth, energy use and environmental
protection. (2003). For the Philippines, no SCGE model has been estimated yet and this paper
is the first endeavor in such direction.
However, very few among the SCGE models mentioned have looked into welfare effects oftransport infrastructure investment in terms of narrowing the interregional income disparity
within a country. A recent study by Alemied et al. in 2003 explored this dimension by
analyzing the effect on interregional equity of road network in Brazil. The study concluded
that transport infrastructure could be an effective mechanism for reducing regional income
disparities. This is because the poorer regions benefit more in terms of trade creation witheconomic integration with richer regions, which have large market potential. It is therefore
within the aforementioned context that this study proceeds in determining the impact oftransport infrastructure investment on interregional equity between two regions in thePhilippines, namely the National Capital Region and the Rest-of-the-Philippines.
2. A 2-REGION SAM FOR THE PHILIPPINES
2.1 Framework of 2-Region 2-Income Level SAM
A 2-region social accounting matrix (SAM) with two types of households for each region was

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

constructed and used as database in calibrating the SCGE model. A SAM represents
transactions in a complete economic system during an accounting period. It integrates withina macroeconomic framework some detailed accounts for factors of production andinstitutions – especially households – so as to focus on the living standards of different
groups in society. (Round: 2002). When a national SAM is split into regional SAMS, the flowof income from production units to consuming units is given a spatial dimension. Thus, the2-region SAM was constructed in order to analyze economic ripple effect on regional income
disparity. It has the following disaggregation of economic agents. The regions are: Metro
Manila area (NCR: National Capital Region) and the rest of the Philippines (ROP). The
industries are primary industry, secondary industry, tertiary industry and transportationindustry. Households are classified either as high-income households or low-income
households in NCR and ROP, depending on a threshold annual income of PHP 60,000. Thereis only one government sector for both regions.
2.2 Methodology for Construction of 2-Region SAM
The 1994 2-region (NCR-ROP) interregional input-output (IRIO) table, taken from Japan
Society for the Promotion of Science (JSPS) database, was used as basis for constructing the2-region SAM. This IRIO table traces the flow of goods and services between sectors,
intra-regionally as well as inter-regionally. The 4-sector IO table in the SAM is anaggregation of the JSPS IRIO table. The following steps were undertaken in the constructionof the NCR-ROP IRIO table, in their sequential order:
Step-1: Compilation of intra-regional IO tables
Step-2: Construction of inter-regional commodity flow tables
Step-3: Integration, reconciliation and revalidation
For step-1, the compilation of intraregional IO was carried out using the following official
data sources: (1) 1994 Census of Establishments, (2) 1994 Family Income & Expenditure
Survey, (3) National Statistical Coordination Board gross regional domestic product (GRDP)
and gross regional domestic expenditure (GRDE) data set and (4) 1994 Philippines
Input-Output Table. To regionalize the 1994 Philippine national input-output table, a hybrid
approach of estimation was used. This means that primary data were combined withsecondary data using no-survey techniques. There were 5 statistical activities undertaken to
generate the NCR and ROP IO table namely: (1) compilation of industry-by-product MAKE
matrix; (2) construction of product-by-industry USE table of competitive imports type; (3)estimation of regional exports and imports using location quotient technique; (4) generationof product-by-industry use table of non-competitive imports type and (5) derivation of
product-by-product use table of non-competitive imports type. For step-2, the inter-regional
flow tables were constructed using an indirect method due to the absence of data on
inter-regional commodity trade that fits into the conceptual framework of IO accounting, i.e.
tracking the flows from producer to consumer. Existing NSO commodity flow statistics could
not be directly used because these are flows between ports of origin and destination only. The
compilation of the flow tables therefore made use of the Simple Location Quotient (SLQ)
method. An SLQ is one measure of the region’s self-sufficiency in production. If SLQi isless than unity, the region imports some of the output i from elsewhere, whether
domestically- or foreign-sourced or both. On the other hand, if SLQi is greater than 1.0, theregion exports some of its industry’s output, either to the rest of the Nation or to foreigncountries or both. If SLQ is equal to 1.0, the region is viewed as self-sufficient with respect to
output i. The SLQ approach is used in estimating the import content of transactions for those
sectors with SLQs less than unity. . Combining the NCR and ROP IO tables of the3987

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

competitive type generates the 2-region IRIO table. Reconciliation and revalidation of thepreliminary table is then effected.
To regionalize the national totals, various appropriate regional indicators were used. From thenational totals taken from official data sources, the following components of primary inputsof IO table were estimated: (1) net land rent by industry; (2) direct taxes by industry and (3)tariffs by commodity. For net land rent, indicators derived from 1994 Census of
Establishments (CE) determine a net land rent to gross output ratio. This ratio is applied to IO
output to get estimated land rent by region. For total direct tax payments, the CE revenue data
were used as proxy indicators in disaggregating total national direct tax payments by firms
into regional and spectral dimensions. For tariffs, they were obtained in proportion to CIF(cost inclusive of freight) values of commodity imports. For the other cell entries in regional
SAM, other indicators were used as deemed appropriate. For example, in the case ofhouseholds as in household direct taxes, household savings, household income fromrest-of-the-world and firm dividends to households, FIES indicators were used. In the case offirms, their savings and dividends to ROW were derived using IO output indicator. In thecase of the government sector; government expenditures were taken from IO tables; andgovernment savings were taken from IO output indicators.
Closing the SAM model with respect to households requires the estimation of vectors ofregional household incomes. In this study, household income takes into account three sourcesof family incomes: a) salaries and wages, b) entrepreneurial income and c) transfer income.
The basic sources of data are the 1994 FIES, government financial statistics by DOF andflow-of-funds data from the BSP. The 1994 FIES generates data on family income by sourceof income, by income class and by region. In addition, the FIES data provide indicators on
savings as a component of the SAM. For consistency, FIES data are reconciled with theofficial estimates by NSCB on private consumption expenditures (PCE). One limitation of
the estimation process is the lack of data on financial flows at the regional level. Hence, allfinance-related accounts are treated as exogenous accounts.
3. SCGE MODEL BASED ON 2-REGION SAM
3.1 Assumptions
The framework assumes: (1) Economy includes four commodity-producing sectors, including
one transport sector, two types of households for each region and one government sector. (2)The demand for the transport sector services is a derived demand associated with the demandof intermediate production goods. (3) There is imperfect substitutability of same type of
goods produced in spatially separated regions, based on Armington assumption. (4) The
production factors are capital and labor. Labor is able to move freely between both regions
and industries. The capital is able to move freely only among sectors, although the movement
between regions is not possible. (5) The economy has 11 markets. These are composed of 8
markets of 2-region and 4-industrial sectors and two capital markets and one labor market.
All markets are perfectly competitive markets.
3.2 Behavioral Modeling of Economic Agents(1) Commodity-Producing industries or Production Sectors (i =1, 2, 3)
The production function is of a two-level nested type, so that production decisions are made3988

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

in two stages shown in Figure-1.
Industry j
In the first stage, the overall
output level for commodity j isdecided upon by combining inputs
of a composite factor input (madeIntermediate Input
Composite Input factor
Leontief
up of labor and capital) andintermediatecommodities
consisting of the outputs of other
2
3
i =1
Labor
Capitalindustries. This first stage is
Goods consumption
characterized by fixed proportions
Cobb − Douglasof the various inputs, so thatchanges in input prices do not
NCR
ROP ・・・ NCR
ROP
induce any substitution between
Cobb − Douglas
the various input levels, andinstead impact only on the overall
Figure-1. Nested Structure of Industries’ Behavior
level of output. Thus, the firstlevel production decision is specified as of the Leontief type. In the second stage, the firm
exercises some discretion with respect to (1) the amounts of capital and labor to be combined
to produce the composite input, and (2) the sourcing of the intermediate inputs from thevarious industries. These intermediate inputs can come from any of two regions: the
National Capital Region (NCR) and Rest-of-the-Philippines (ROP). Substitution in thissecond stage is characterized as follows: (1) each percentage change in the amount of labor
input must be compensated for by a constant percentage change in the capital input; (2) for
each industry, each one percentage change in the amount of intermediate input from the NCR
must be compensated for by a one percentage change in the input coming from areas outside
the NCR. Thus, substitution in the second stage is characterized as of the Cobb-Douglas type.
Prices are classified into two types; those are producers’ and purchasers’ price. Production
sectors maximize profit under given production technologies:
Max: π sj = p sj X sj − ∑∑ q irs xijrs − (1 + τ sj )(ρ s K sj + ω Lsj )
4

2

i

r

(

)

 f js K sj , Lsj χ ijs  
, min  s  
s.t. X = min 
i
a ojs

 aij  
s
j

(

)

( ) (L )χ = A ∏ (x )f js K sj , Lsj = A0s j K sj
s
ij

(1)

s
ij

2

s
α Kj

rs
rs α ij
ijss α Ljj(2)( α +α =1 )
s
Kj

s
Lj

(3)
(4)

r

From profit maximization, the derived demands for capital, labor, and the intermediate inputare:p sjα ijrs χ ijs
rs
(5)
xij =
q irs aijs

 α Ljs ρ s s
Lj =  sα ω 
 Kj 
 α Kjs ω K sj =  s s 
α ρ 
 Lj


s
α Kj

a ojs X sj
A0s j

α Ljs

a ojs X sj
A0s j

(6)

(7)

where,
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Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

X sj : Total amount of production output by industry i in on s
x sj : Composite input factor i to industry j in region s
χ ijs : Intermediate input demand from industry i in region s to industry j in region s
K sj : Capital demand of industry j in region s
Lsj : Labor demand of industry j in region s
a 0s j : Value added rate of industry j in region s
aijs : Intermediate input coefficient of industry j in region s for intermediate good i
Aijs , A0s j , α ijrs , α Kjs , α Ljs : Technological parameters of industry j in region sp sj : Producers’ price of commodity i in region s
qirs : Purchasers’ in region s for commodity i in region r
τ sj : Indirect tax rate of industry j in region sρ s : Rate of return on capital in region sω : Wage rate
a 0s j , aijs , α ijrs , α Kjs , α Ljs and τ sj are estimated using SAM.
should be calibrated.

Aijs , A0s j are parameters which(2) Transport sector
Transport cost is the cost that is needed to ship the commodity from place of origin to placeof destination and is paid to a transport agent. We need the transport sector to estimate thetransport cost/margin as endogenous variable. The demand for transport sector services is thederived demand associated with the intra- and inter-regional shipment of production goods.
Transport cost is paid to the transport agent existing in production region. The behavior oftransport sector is formulated as minimization of transport cost under transport service
demand constraint and is depicted as follows:
4

2

i

r

(

)(

Min: ∑∑ qirs xirs4 + 1 + τ 4s ρ s K 4s + ω Ls4)

s.t. X 4s = T s(i ≠ 4)
Where, total transport service demand of transport agent is shown as follows:
4

(8)
(9)

2

T r = ∑∑ x 4rsj + ch4r + cg 4r + cI 4r + e4r − m4r
j

s

2
4
 4

= ∑ ∑∑ xijrs d rsθ irs + ∑  ∑ chirm d rh + cg ir d irg + cI ir d rI + eir d re − mir d rm  θ irs
(10)
i
j
s
i  m

The solution of the above optimization-programming problem yields to each derived demand
function of intermediate input goods, labor and capital stock for transport sector. This derived
demand function is the same as that of commodity- producing sectors.
44(3) Households
Households are classified into classes: NCR-L, NCR-H, ROP-L and ROP-H, in terms of theincome level and region. Each household attempts to maximize its utility under static
short-run conditions. This consumption behavior is illustrated in a nested structure shown in
Figure-2. At the first stage, the household determines consumption level of present goods andfuture goods. At second stage, households determine levels of composite goods using
Cobb-Douglas functional form.
The household derives utility from the consumption of various goods, which are dated
through time. For simplicity, the consumption of all future goods is aggregated into a3990

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

composite called saving. The life cycle
model of consumption implies that thehousehold optimizes utility by evening out
consumption through the life cycle, so thatthe average propensity to consume (andtherefore the average propensity to save,σm) stays constant. There is thus a fixedproportion between current consumption
and the present value of future
consumption, which is encapsulated insaving.
This reduces the utility
maximization to a one period model.

Disposable income

Future Consumption

Present Consumption

i= 1

2

3

Composite goods
NCR

ROP

・・・

NCR

ROP

Cobb − Douglas

Again, it is assumed that there is constantelasticity of substitution between the Figure-2. Nested Structure of Households’ Behavior
different commodities in consumption, and,for simplicity, consumption decision can be characterized by a Cobb-Douglas function.
Households would be indifferent between the same amount of the commodity sourced fromthe NCR and from outside the NCR. There is, therefore, a second level sourcing decision,
where the second level is likewise characterized by a constant elasticity of substitution equal
to one and constancy of returns to scale. A second level relationship is specified as being of
the Cobb-Douglas type. The utility maximization problem of household is as follows.

( )4

Max: U m = ∏ ch imi2(

ch im ≡ ∏ chirm
r

4

β ihm

( ∑ β ihm = 1 ))2β ihrm42ir( ∑ β ihrm = 1 )

(12)

r

s.t. ∑∑ q irh chirm = y hm

(13)Household’s consumption demand is as follows:
β ihm β ihrm y hm
rm
chi =
q irh
where,
2

4

s

j

(11)

i

[ (

) ]

(14)
2

4

s

j

H m = γ Km ⋅ ∑∑ ρ s 1 − η sj K sj + γ Lm ⋅ ∑∑ ω ⋅ Lsj + TrGH m + TrOH m(15)

Household disposable income is derived as follows. Direct taxes are imposed on the sum ofcapital income, labor income and transfer payments from other households. Income fromsuch sources, net of direct taxes, plus transfers from the government (which is tax-free)equals household disposable income. Household income equation takes the following form:
Yhm = (1 − τ d )(H m − TrHG m ) + TrGH m − TrOH m(16)
Thus, income allocated to consumption equals disposable income multiplied by 1 minus theaverage propensity to save.
y hm = 1 − σ m Yhm(17)
where,
TrGH m : Current transfers from general government to household segment m
TrOH m : Current transfers from oversea to household segment m
TrHO m : Current transfers from household segment m to oversea

(

)

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

η sj : Capital consumption rate of industry j in region sτ dm : Direct tax rate from household segment m to general government
σ m : Saving ratio of household segment m
H m : Total amount of income of household segment m
Yhm : Total amount of disposable income of household segment m
y hm : Total amount of consumption expenditures of household segment m
β ihm , β ihrm : Allocation parameters of consumption by household segment mγ Km , γ Lm : Allocation parameters to household segment m of capital income and wages

τ dm , σ m , β ihm , β ihrm , γ Km and γ Lm are parameters which should be calibrated by using
some statistics.

(4) Government Sector
There is only one government sector.

∑ (H − TrHG )⋅τ
4

m

m

m

d

m

2

4

s

j

The government’s inter-temporal budget constraint is:()+ ∑∑τ sj ⋅ ρ s ⋅ K sj + ω ⋅ Lrj + TrOG4

= y g + ∑ TrGH m + SG + TrGO

(18)

m =1

Total government income is composed of income from direct tax as manifested by the firstexpression on the left-hand side plus government income from indirect tax from capitalincome and household income. These are the expressions on the left-hand side of theequation. On the other hand, government expenditures consist of transfers to households fromgovernment transfers overseas, government savings and other types of government income.
Cost minimization problem of government is formulated as follows.
33

Min: E = ∏ (cg i ) ig

（ ∑ β ig = 1 ）( )（ ∑ β igr = 1 ）i
2βcgi ≡ ∏ cgir
r

s.t.

3

2

i

r

(19)

i

2

β igr

(20)

r

∑∑ q cg = y
rg
i

r
i

g

Government consumption demand (cg) is follows:
β ig β igr y g
cg ir =
qirg
where,
TrHG m : Transfer from general government to household segment m
TrOG : Transfer from general government to oversea.
TrGO : Transfer from oversea to general governmenty g : Total amount of available revenue of the government

(21)

(22)

SG : Government savings
β ig , β igr : Allocation parameters of the government consumption(5) Savings- Investment Sector
Total amount of savings consists of the consumption of fixed capital and savings by3992

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

households, general government and Rest-of-the World (R.O.W). The budget constraint is asfollows:
2

4

s

j

p I I = ∑∑ D sj + σ Yh + SG + SO
The net investment function follows the functional form of Leontief type:
 cI
cI 
I = min  1 , L , 4 
i
b4 
 b1
β iIr

( )

2

cI i = ∏ cI
r

r
i

2

( ∑ β iIr = 1 )

(23)

(24)
(25)

r

The demand of the production goods that accompanies real investment (cI) is as follow:
cI i = bi I
(26)
cI =
r
i

β iIr p iI bi I

(27)

q irI

where,
p I : Investment goods priceI : Real investment
D sj : Consumption of fixed capital or investment expenditures
SO : Savings of foreign sectorcI i : Total Investment expenditures
cI ir : Investment expenditures of each regionbi , β iIr : Technology parameter(6) Foreign Sector
The expenses of foreign sector include the purchase of imports plus labor income fromabroad plus transfers to government plus transfers to households. This is equal to revenues offoreign sector, which include exports plus labor income to the Philippines plus transfers tohouseholds plus transfers to government plus overseas savings. These are captured inequation below:
2

4

r

i

4

∑ ∑ qir 3 mir + LI + TrGO + ∑ TrOH m
m

2

4

4

r

i

m

= ∑ ∑ q i eir + LO + ∑ TrOH m + TrOG + SO
The volume of import and export are follows:X r
import volume: mir = mir *  i r * Xi  q r 5*
export volume: eir = eir *  i
r5 
qi 


(28)

(29)
(30)

where,
LI : The employer income from the Philippines
LO : The employer income to the Philippines
mir , X ir , eir , q ir 3 : Volume of import, production quantity, export volume and price of thegood i in overseas
m ir * , X ir * , eir * , q ir 3* : Volume of import, production quantity, export volume and price of thegood i in overseas in benchmark year3993

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

3.3 Determination of Price in Price Block(1) The Derived Demand for Production Factors
The derived demand equation for labor and capital are indicated below:
 α Ljs ρ s sLabor: l j =  s
 α Kj ω 



s
α Kj

 α Kjs ω sCapital: k j =  s s  α Lj ρ 

⋅a 0s j

(31)

s

A0 j

s
α Lj

⋅

a 0s j

(32)

s

A0 j

(2) The Price equation
Each production sector is a price taker. The price of goods produced by each sector fromthe zero profits condition in each region is equal to the total of the intermediate amount ofconsumption and the production factor cost including tax cost and the transport cost. Total
value of the commodity is equal to the sum of the values of intermediate inputs plus
value-added component inclusive of value-added tax, where all values are computed gross oftransport cost. The resulting price equation is as follows:
4

2

i

r

(

)(

p sj X j = ∑∑ p ir x ijrs + 1 + τ sj ω Lsj + ρ s K sj

)

(33)

By dividing values by the output level, the expression for the price level is obtained.
4

2

i

r

(

)(

)

p sj = ∑ ∑ p ir a ijrs + 1 + τ sj ω l sj + ρ s k sj[

P = I − AT ( p)(34)

] (1 + τ )(ω l + ρ k )
−1

s
j

s
j

s

s
j

(35)

where,
aijrs : The intermediate input coefficient to the area industry of area industry
θ ir : A common transport unit transformation parameterd rs : The transport distance between an/the area
The transport sector’s price relies on transport distance between and within an area and therelationship between traffic volume and transport capacity.
11L
p11 a11M
A ( p) =TO

11L
p11 a14
1 12
1 11

pa
M

3

p14 ∑ ai111θ i1 ⋅ d 11i3M

p14 ∑ ai114θ i1 ⋅ d 11i3Lp ∑a θ ⋅d

O

M

1
4

12 1
i1 i

12

p12 a1121 LM

p12 a1421 L
2 22
1 11

p a

L

M

O

i

3

12
p11 a14
L p 14 ∑ a i124θ i1 ⋅ d 12
i

O

3

2
21
p 42 ∑ a i21
1 θi ⋅ di3

M

p 42 ∑ a i214θ i2 ⋅ d 21i3p ∑a θ ⋅d
2
4

22 2
i1 i

(36)

22

i

3

M

p12 a1422 L p 42 ∑ ai224θ i2 ⋅ d 22i

3.4 Equilibrium Conditions
The equilibrium conditions in the market for goods market, transport services market and3994

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3985 - 4000, 2005

factor markets are:(1) Goods and production factor market
2

4

4

s

j

m

Goods market: X ir = ∑ ∑ x ijrs + ∑ chirm + cg ir + cI ir + eir − mir ; (i = 1,2,3)
2

4

4

s

j

m

Transport service market: X 4r = ∑ ∑ x 4rsj + ∑ ch4rm + cg 4r + cI 4r + e 4r − m 4r
2

4

s

j

(37)
(i = 4) (38)Labor market: LS = ∑∑ Lsj(39)
4

The capital market: KS s = ∑ K sj

(40)

j

(2) Goods’ price[

The producers' price: P = I − A T ( p)] (1 + τ )(ω ⋅ l + ρ ⋅ k )
−1

s
j

s
j

s

s
j

User price: q irs = p ir + p 4r θ irs d rs

(41)
(42)

Equation (42) is the most important equation in the price block. It emphasizes the role ofprice in determining equilibrium values in SCGE model.

4. CALIBRATION OF PARAMETERS AND BALANCING ALGORITHM
The estimation of parameters used in SCGE follows in Table 1. The parameters used in
Cobb-Douglas nested production function and Cobb-Douglas nested utility function are
enumerated below. It is assumed that the parameters of SCGE are set up in such a way that
Table 1 List of Parameters
Model

SectorParameter

Dimension

a0s j

2s×3j=6saKj

2s×3j=6

Ratio of value-added out of total output of industry j inregion s
Share of Capital Income Out of Total Incomes
Lj

2s×3j=6

Share of Labor Income Out of Total Income.

s
0j

A

2s×3j=6Intermediate input coefficient of industry j in region s

Aijs

Intermediate input coefficient from composite good iin industry j in region sτ sj

2s×3i×3j=18
2r×2s×3i×3j= 36
2s×3j=6

β3i×4m =12

Allocation Parameter of HH Conspn. Expenses

β ig

2r×3i×4m
=24
i =3

β igr

2r×3i=6

β iI

i=3Allocation Parameter of HH Conspn. Expenses by
RegionAllocation Parameter of Govt. Consumption Expenses
Allocation Parameter of Govt. Consumption Expenses
by Region
Allocation Parameter of Investment Sector

β iIr

2r×3i =6

Allocation Parameter of Investment Sector by RegionaProduction
Function

Industry

α ijrsHouseholdConsumption
Expenditure Government
Investmentmih

β ihrmDescription of Parameter

Technological parameter which should be calibrated
Indirect tax of Industry j in region s

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next year’s data set is reproduced exactly.
The logic behind the algorithm used to calculate equilibrium price and quantity in SCGE is asfollows. The price variable should equal total factor incomes plus the transport cost. However,transport margin is affected by haul distance, which increases as traffic volume exceeds
transport capacity. When price does not equal total primary factor income plus transport cost,
it adjusts accordingly. Then final demand adjusts to price changes, and then output adjusts tochanges in final demand. This happens until an equilibrium solution is reached where
aggregate demands equals aggregate supply and both goods and factor market equilibrate.
This happens via adjustments in factor incomes and transport margin. We skip the detail ofbalancing algorithm here because of the limitation in number of pages.

5. APPLICATION OF ECONOMIC RIPPLE EFFECT ANALYSIS CAUSED BY THE
TRAFFIC BASE UPGRADING IN THE PHILIPPINES
5.1 Setting of Simulation Cases
Alternative scenarios are drawn which reflect different situations when traffic upgrading
occurs due to transport infrastructure improvement. The setting is 10 years later than 1994,
which is 2004, where transport distance becomes shorter by 10% due to traffic baseupgrading. The assumption is that transport distance becomes long when the traffic volume
between or within an area exceeds transport capacity.
The average annual growth rates of labor supply and capital supply in the Philippines are
estimated over a period of 10 years using the following statistical sources: 2001 Philippine
Statistical Yearbook (PSNA); Labor force participation rate and employment status
1994-2000; Gross regional domestic product 1994-2000; Ministry of Public Management,
Home Affairs, Posts and Telecommunications Statistics Bureau) of the world; World
Economic growth rate (GDP) 1995-2000. These estimates are indicated in Table 2.
Table 2 Average Annual Growth Rate of Economic Indices (’94-’04)
Annual average growth rate

%

Labor

1.658
6.038
4.192

CapitalForeign Sector

NCR
ROP

3.817

5.2 Introduction of Impedance Function
The main purpose of this research is that it examines where it is efficient to do what kind of
traffic infrastructure investment, in a developing country wherein the lack of the traffic
infrastructure between the regions is the bottleneck of transport. It is a known fact that
traffic infrastructure upgrading enhances productivity in each region and encourages tradethrough lower transport margins and faster movement of goods and services. Currently
however, transport capacity is not sufficient in facilitating flows of goods and services
between National Capital Region and rest-of-the-Philippines. This is because principal road
networks, harbors and airports need upgrading.
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This study therefore assumes that Philippine traffic volume and transport capacity equilibrate
in 1994. Beyond 1994, traffic volume increases and there is need for transport capacity tomatch the increase. When traffic between the regions exceeds transport capacity, transportdistance based on transit-time distance changes in the upward direction. The user priceequation is restated below:
3

2

i

r

(

)(

)

3

2

i

r

p sj = ∑∑ p ir a ijrs + 1 + τ sj ω l sj + ρ s k sj + ∑ ∑ p ir a ijrsθ irs d rs

(43)

The expression on the extreme right-hand side of the equation above is the transport margin.
There is no conversion of transport distance with traffic volume within NCR, within ROP andfrom NCR to ROP. The impedance function is introduced with a distance variable, which
depends on traffic volume between ROP to NCR. The variable “d” is a common transport
unit transformation parameter whose value is explained by the succeeding equation.
 x R−N 
R−Nd rs = d ROP − NCR = d 0rs  R − N  x R − N ≥ x cap
x

(44)
 cap R− N
d rs = d ROP − NCR = d 0rs
x R − N p x cap
where,
d 0 : Transport distance of initial equilibrium condition in 1994
R−N
xcap
: Transport capacity between ROP-NCR of initial equilibrium condition in 1994

x R − N : Traffic volume between NCR-ROP

5.3 Transport Unit Conversion Parameters
In SCGE model, an assumption of the model is that output and income are distinguished byplace of production and residence. The quantity of transport services used over a certain
dimension of space differs by the individual income and output of the producing region. A
common transport unit conversion parameter is set up so that linear relationship exists
between the traffic and transport service quantity of combined income and output of origin
and destination.
The transport sector should be subdivided into air transport, land transport and watertransport. Because the transport sector was not subdivided into previously mentioned
categories, the estimated common transport unit conversion parameter was negative and no
exact transport cost could be estimated. It was also impossible to get a significant parameterof interconnected regions like NCR to ROP. Therefore, the only parameter estimated was thatof ROP-NCR. This link is the object of transport restriction condition. The estimation resultis shown in Table 3.
Table 3

ROP-NCR Common Transport Unit Conversion ParameterIndustry
Parameters
Value
Primary
Secondary
Tertiaryθ 1ROP − NCR
θ 2ROP − NCR
θ 3ROP − NCR

0.00495
0.000710.01018

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6. SIMULATION RESULTS
6.1 Concept of Household Welfare
Welfare is measured in terms of equivalent variation (EV). EV is the change in money
income that would put the household on the new indifference curve at the old prices. EV
measures the scale of welfare change achieved through transport infrastructure improvement,
which would put households on a higher utility level at previously existing prices. The profits
of the industry in the equilibrium condition are all zero and the welfare gains go to thehousehold. The welfare gains to low-income and high-income households depend on theirabsolute levels of income. The utility maximization problem of households yields theequivalent variation of a price change in one of the goods:u m ( N ) − u m (0 ) m (0)(0 ) m ( N )(0 ) m ( 0 )m
EV = e q , uyh− e q ,u=(45)
u m(0)
EVm : Equivalent variation of a price change on households

(

) (

)

u m ( 0 ) : Utility level of households before traffic infrastructure upgradingu m ( N ) : Utility level of households after traffic infrastructure upgrading
y hm ( 0 ) : The amount of final consumption expenditures of households before traffic
infrastructure upgrading

6.2 Effect of Transport Infrastructure Improvement on Household Welfare
Results indicate that transport infrastructure investment in NCR cause negative benefit toboth low-income and high-income households in rest-of-the-Philippines and positive benefit
to NCR households. On the other hand, transport infrastructure improvement in ROP alone,leads to welfare gains in ROP households and welfare losses to NCR households. Thislessens income disparities between NCR and ROP; if transport planning is focused on ROP.
This is deemed best for equity and efficiency. Moreover, in the case where transport
infrastructure improvement takes place within one region, whether NCR or ROP, the welfare
gains are higher for rich households than poor households. The result works the other way
round, meaning higher welfare losses for rich households than poor households when
Million Peso

Peso

6,000

1,500

4,000

1,000
2,000

500

0
-2,0000-4,000
NCR

-500
N-L

ROP
NCR

Figure-3. Regional Total Equivalent Variation

ROP

NCR-ROP

N-H

R-L

R-H

Figure-4. Equivalent Variation of HH m

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inadequate transport infrastructure investment takes place.
These results come from 4 different simulation cases where in traffic infrastructure
improvement takes place: Case-1: no improvement occurs; Case-2: Infrastructure
improvement takes place within NCR only; Case-3: Infrastructure improvement takes place
within ROP only and Case-4: Infrastructure improvement establishes better links between
ROP to NCR with transport capacity restriction. Figure-3 shows the amount of regional total
equivalent variation and Figure-4 shows the amount of equivalent variation of Household
type m by case, respectively. The interim results show that investment in NCR cause negative
benefit in ROP households, thus, causing wider income disparity gap. Moreover, investment
in ROP lessens income disparity and is deemed best for economic efficiency.

7. CONCLUSION
The preceding discussion presented the theoretical model and empirical results of traffic
infrastructure upgrading under assumption of 2-region, 2-income level per region in thePhilippines. Measurement of benefits using simulated cases of single –region infrastructure
improvements versus multi-regional infrastructure improvements lead to useful andinteresting insights.
Infrastructure improvement, which connects the rest-of-the Philippines region with the
National Capital Region, leads to optimal social welfare gains as it leads to narrowing theincome gap between rich region like NCR and other poor regions that belong to therest-of-the Philippines. Investing in transport infrastructure, which improves transportcapacity between ROP and NCR, results to higher welfare gains to households in ROP than in
NCR. This will lessen income differential between two regions and lessen rural-urban
migration, which leads to high unemployment in urban areas in NCR. Consequently,
unemployment in NCR, which is mostly urban unemployment, will go down and more
Filipinos will seek jobs or work in the rest-of- the Philippines, which is predominantly a rural
area.
However, more concrete and definitive policy action can be undertaken if the rest-of thePhilippines were broken down into critical regions and the model is expanded to a 5-region
model. This is one subject of future research. Other areas of research within the realm of
creating the data base for Philippine SCGE model are: (1) updating of basic database ofmultiregional SAM; (2) more precise estimation of common transport unit transformation
parameter under different simulation assumptions; (3) creation of local government sector foreach region; (4) expansion of spatial dimension into 5-region analysis.
The initial findings of 2-regional SCGE model put together for a developing country like thePhilippines point to important policy directions and policy impact. In the end, through all ofthese discussions, the importance of effective transport infrastructure investment planning isemphasized. This will lower transport margins, which translates to high multiplier effects.
Ultimately, social gains will be maximized and the desired equity and efficiency effects will
be attained.

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