APPLICATION TO THE DEVELOPING COUNTRYOF SCGE MODEL BASED ON 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
Maeda Corporation
Tokyo, 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 bi-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 abi-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infrastructure investment on each region individually and the two-interconnected regions.

2. A BI-REGION SAM FOR THE PHILIPPINES
2.1 Framework of Bi-Region 2- Income Level SAM

A 2-region social accounting matrix (SAM) with two types of households for each region wasconstructed 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 and institutions– 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 flow of income fromproduction units to consuming units is given a spatial dimension. Thus, the bi-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 primaryindustry, secondary industry, tertiary industry and transportation industry. Households areclassified either as high-income households or low-income households in NCR and ROP,
depending on a threshold annual income of PHP 60,000. There is only one government sector
for both regions.
2.2 Methodology for Construction of Bi-Region SAM
The 1994 bi-region (NCR-ROP) interregional input-output (IRIO) table, taken from JSPS
database, was used as basis for constructing the bi-region SAM. This IRIO table traces theflow of goods and services between sectors, intra-regionally as well as inter-regionally. The
4-sector IO table in the SAM is an aggregation of the JSPS IRIO table .The following steps
were undertaken in the construction of 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) generation of
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, the region exports
some of its industry’s output, either to the rest of the Nation or to foreign countries 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 the competitive type generates thebi-region IRIO table. Reconciliation and revalidation of the preliminary 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 inputs of
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 the caseof 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
Industry j
production decisions are made in
two stages. In the first stage, theoverall output level for commodity
Composite Input factor
Intermediate Input
j is decided upon by combining
Leontief
inputs of a composite factor input(made up of labor and capital) andintermediatecommodities23
i =1
Capital
Labor
Goods consumption
consisting of the outputs of other
Cobb − Douglasindustries.
This first stage ischaracterized by fixed proportions
NCR
ROP ・・・ NCR
ROPof the various inputs, so that
Cobb − Douglaschanges in input prices do not
induce any substitution between thevarious input levels, and instead
Figure-1. Nested Structure of Industries’ Behavior
impact only on the overall level of
output. Thus, the first level production decision is specified as of the Leontief type. In thesecond stage, the firm exercises some discretion with respect to (1) the amounts of capital andlabor to be combined to produce the composite input, and (2) the sourcing of the intermediate
inputs from the various industries. These intermediate inputs can come from any of two
regions:
the National Capital Region (NCR) and Rest-of-the-Philippines (ROP).
Substitution in this second 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 thecapital 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 comingfrom 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’ andpurchasers’ price. Production sectors maximize profit under given production technologies:Max:

π sj = p sj X sj − ∑∑ qirs xijrs − (1 + τ sj )(ρ s K sj + ω Lsj )
4

2

i

r

(

)

 f js K sj , Lsj χ ijs  
s.t. X sj = min ,min s 
ia ojs
 aij  ()( ) (L )f js K sj , Lsj = A0s j K sjs
α Kj

χ ijs = Aijs ∏ (xijrs )2

α ijrsss α Ljj(1)(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(5)xijrs =
q irs aijs
 α Ljs ρ s 
Lsj =  s
α ω 
 Kj s
α Kj

a ojs X sj
A0s j

(6)

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

α Ljs

a ojs X sj

(7)

A0s j

where,
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 place ofdestination 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:∑∑ q x + (1 + τ )(ρ K + ω L )(9)s.t. X = T s(i ≠ 4)
Where, total transport service demand of transport agent is shown as follows:

(10)

4

Min:

rs
i

i

4

2

rs
i4

s
4

s

r
s
4

s
4

s
4

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
(8)
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, interms of the income level and region. Eachhousehold attempts to maximize its utility
under static short-run conditions. This
consumption behavior is illustrated in anested structure shown in Figure-2. At thefirst 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.

Disposable income

Future Consumption

Present Consumption

i= 1

2

3

Composite goods

NCR

ROP

・・・

NCR

ROP

Cobb − Douglas

The household derives utility from the
Figure-2. Nested Structure of Households’ Behavior
consumption of various goods, which aredated through time. For simplicity, the consumption of all future goods is aggregated into acomposite called saving. The life cycle model of consumption implies that the household
optimizes utility by evening out consumption through the life cycle, so that the average
propensity to consume (and therefore the average propensity to save, σm) stays constant.
There is thus a fixed proportion between current consumption and the present value of future
consumption, which is encapsulated in saving. This reduces the utility maximization to a
one period model.
Again, it is assumed that there is constant elasticity of substitution between the 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 from the NCR and from outside the NCR. There is, therefore, asecond level sourcing decision, where the second level is likewise characterized by a constantelasticity 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

β ihm

4

( ∑ β ihm = 1 ))β ihrm2( ∑ β ihrm = 1 )

2

i

r

s.t. ∑∑ q irh chirm = y hm

(13)Household’s consumption demand is as follows:β m β rm y mchirm = ih ihrh hqiwhere,
2

4

s

j

[ (

(12)

r

r

4

(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
η sj : Capital consumption rate of industry j in region s()τ 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 somestatistics.
(4) Government Sector
There is only one government sector. The government’s inter-temporal budget constraint is:∑ (H − TrHG )⋅τ
4

m

m

m

d

2

4

s

j

(

)

+ ∑∑τ sj ⋅ ρ s ⋅ K sj + ω ⋅ Lrj + TrOGm

4

= 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 the equation.
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.
3

Min:

E = ∏ (cg i )3（ ∑ β ig = 1 ）

β ig

2

( )

cgi ≡ ∏ cgir2

（ ∑ β igr = 1 ）

β igr

3

2

∑∑ q cg = y
rg
i

i

(20)

r

r

s.t.

(19)

i

i

r
i

g

(21)

r

Government consumption demand (cg) is follows:
β ig β igr y g
rcg i =
qirg(22)

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
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 byhouseholds, 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
2

β iIr

( )

cI i = ∏ cI

r
i

(23)

(24)

2

( ∑ β iIr = 1 )

(25)

r

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 piI bi I

(27)

qirI

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 from abroadplus transfers to government plus transfers to households. This is equal to revenues of foreign
sector, which include exports plus labor income to the Philippines plus transfers to households
plus transfers to government plus overseas savings. These are captured in equation 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:

(28)

 r

import volume： mir = mir *  X i r * 
X
i 

r 5*


export volume: eir = eir *  q i
r5 
qi 


(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 the good i inoverseas in benchmark year

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 Labor: l sj =  sα ω 
 Kj 

sα Kj

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

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 from thezero 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

(

)(

)

34)

] (1 + τ )(ω l + ρ k )(35)

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

P = I − A T ( p)

−1

s
j

s
j

s

s
j

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.

3

p14 ∑ ai111θ i1 ⋅ d 1111
p11 a11L

3

2
21
p 42 ∑ ai21
1 θi ⋅ d

p12 a1121 Li

M
AT ( p ) =O3

p14 ∑ ai114θ i1 ⋅ d 1111
p11 a14
L

i

M

M

O

L p ∑a θ ⋅d
1
4

pa

12 1
i1 i

12

i
3

L p ∑a θ ⋅d

2 22
1 11

2
4

p a

i

M

O

3

1
4

pa

12 1
i4 i

22 2
i1 i

(36)
22

i

M

L p ∑a θ ⋅d

1 12
1 14

M

p 42 ∑ ai214θ i2 ⋅ d 21

p12 a1421 L

i
3

1 12
1 11

3

M
12

O

3

M

L p ∑ a θ ⋅ d 22

2 22
1 14

2
4

p a

i

22 2
i4 i

i

3.4 Equilibrium Conditions
The equilibrium conditions in the market for goods market, transport services market andfactor 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)
Transport service market: X 4 =
r

2
s

2

4

s

j

4

∑∑ x

(37)

4

rs
4j

j

+ ∑ ch4rm + cg 4r + cI 4r + e 4r − m 4r (i = 4)

(38)

m

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 of pricein determining equilibrium values in SCGE model.

4. CALIBRATION OF PARAMETERS AND BALANCING ALGORITHM

The estimation of parameters used in SCGE follows. 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 next year’s data set isreproduced 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, 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.

Table 1: List of Parameters
Model

Production
Function

SectorIndustry

HouseholdConsumption
Expenditure

Government

Investment

Parameter

Dimensiona0s js×j=2×3=6s
aKj

s×j =2×3=6

Ratio of value-added out of total output ofindustry j in region s
Share of Capital Income Out of Total Income

a Ljs

s×j =2×3=6

Share of Labor Income Out of Total Income.

A0s j

s×j =2×3=6

Aijs

s×i×j
=2 ×3×3=18

Intermediate input coefficient of industry j inregion s
Intermediate input coefficient from composite
good i in industry j in region s

α ijrsr×s×i×j=2×2×3×3=36

Technological parameter which should becalibratedτ sj

s×j =2×3=6

Indirect tax of Industry j in region s

β

m
ih

i×m =3×4=12

Allocation Parameter of HH Conspn Expenses

rm
ih

r×i×m=
2×3×4=24

β ig

i =3

β igr

r×i =2×3=6

β iI

i=3

β

r×i =2×3=6

β

r
iI

Description of Parameter

Allocation Parameter of HH Conspn Expenses
by Region
Final consumption goods Purchase ground ()Selection probability
Allocation Parameter of Govt Consumption
Expenses
Allocation Parameter of Govt Consumption
Expenses by Region
Allocation Parameter of Investment Sector
Allocation Parameter of Investment Sector by
Region5. 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 base upgrading.
The assumption is that transport distance becomes long when the traffic volume between orwithin 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 the table below:

Table 2 – Average Annual Growth Rate of Economic Indices (’94-’04)
Annual average growth rate

（％）

Labor

1.658
6.038
4.192

NCR
ROP

CapitalForeign Sector

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 region is the bottleneck of transport. It is a known fact that traffic
infrastructure upgrading enhances productivity in each region and encourages trade throughlower 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.
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

(51)

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
(52)
 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 water transport.
Because the transport sector was not subdivided into previously mentioned categories, theestimated common transport unit conversion parameter was negative and no exact transport
cost could be estimated. It was also impossible to get a significant parameter ofinterconnected regions like NCR to ROP. Therefore, the only parameter estimated was that of
ROP-NCR. This link is the object of transport restriction condition. The estimation result isshown below.
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

6. SIMULATION RESULTS
6.1 Concept of Household Welfare

Welfare is measured in terms of equivalent variation (EV). EV is the change in money incomethat 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 theindustry in the equilibrium condition are all zero and the welfare gains go to the household.
The welfare gains to low-income and high-income households depend on their absolute levels
of income. The utility maximization problem of households yields the equivalent variationof a price change in one of the goods:u m ( N ) − u m (0 ) m (0)EV m = e q (0 ) , u m( N ) − e q (0 ) , u m ( 0 ) =yh(53)
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 to both
low-income and high-income households in rest-of-the-Philippines and positive benefit toNCR households. On the other hand, transport infrastructure improvement in ROP alone,leads to welfare gains in ROP households and welfare losses to NCR households. This lessens
income disparities between NCR and ROP; if transport planning is focused on ROP. This isdeemed 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 inadequatetransport 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 typem 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.

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

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 bi-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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