Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

IDENTIFYING TRANSPORT INFRASTRUCTURE INVESTMENT
WITH MAXIMUM IMPACT: A SAM-BASED SCGE APPROACH
Cristela Goce-DAKILA
Associate Professor
Economics Department
De la Salle University
2401 Taft Avenue, Manila
1004 Philippines
Tel/Fax: +63-2 -303-0867
Email: dakilac@dlsu.edu.ph

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

Abstract: The paper identifies the most appropriate transport infrastructure investment
among three alternative transport modes – land, air, water – across five delineated regions in
the Philippines. The analytical tool used is a spatial computable general equilibrium (SCGE)
model with a five-region social accounting matrix (SAM) as database. An exogenous shockin the form of technological improvement in transport infrastructure is introduced for eachtransport mode in each region. This results to higher output elasticity with respect to transport
input. The transport infrastructure with greatest impact on gross output is then isolated using
a SAM-based SCGE model. The impact on relative welfare of households via equivalentvariation concept and on interregional flows among production sectors via changes in spatial
impedance ratio is then presented. The completion of Skyway project connecting Northern
Luzon to Southern Luzon via National Capital Region is a concrete example of infrastructure
project which meets aforementioned criteria.
Key Words: Transport infrastructure, SCGE, Gross output

1. INTRODUCTION
A substantial amount of scarce resources is invested in transport infrastructure projects in adeveloping country like the Philippines. Thus, the choice of location and type of the transport
infrastructure investment becomes a critical variable in macro level decision-making. The
benefits relative to the costs of such choice have to be evaluated so that optimal decision canbe made.
This study addresses such concern by pinpointing the type and location of transport
infrastructure investment that has the greatest impact on gross output in the whole Philippines.
The analytical tool used is a spatial computable general equilibrium model with a five-regionsocial accounting matrix (SAM) as database. An exogenous shock in the form oftechnological improvement across alternative transport modes (water, air and land) in eachregion is introduced. This leads to higher output elasticity with respect to transport input.
Once the type and location of transport infrastructure improvement with the highest impact on
output is determined, the effect on relative welfare of household income groups across regions
is discussed. Change in welfare is measured in terms of change in equivalent variation. Next,
the impact on interregional flows via a spatial impedance ratio is analyzed. Impedance ratio is
the ratio of interregional trade flows to transport capacity. Better technology of particularregional transport infrastructure investment leads to higher output and consequently higher
interregional trade flows. This causes an increase in the impedance ratio.

376

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

2. SURVEY OF TRANSPORT-ORIENTED GENERAL EQUILIBRIUM MODELSThis section presents a general survey of transport-oriented general equilibrium models
devised for transport policy purposes. Differences in modeling transport in a generalequilibrium setting arise from the way the transport cost concept is conceived.
In 1992, Buckley formulated a transportation-oriented general equilibrium model where
capital and labor cannot shift at the interregional level. Therefore, factor income rates vary
among regions and there is specialization among regions. In 1996, Elbers created a multiregion SCGE where some sectors adopt the Armington assumption and others adopt the rulesof the theory of spatial price equilibrium. He solved SCGE using the rules of spatial priceequilibrium analysis. In 1997, the Van den Bergh model was formulated. This is a multiregion spatial model in which goods produced by the regions differ from each other. The nextclass of general equilibrium models with transport sector is composed of network models.
Network equilibrium models are used in engineering and operations research as a means offorecasting traffic flow. Today, they are integrated in CGE models to obtain estimates of cost
per transport link. The possibility of inserting network equilibrium models into CGE models
was first developed by Harker (1987) and then further elaborated on by Roson (1993) andFriesz (2000). Roson (1993) integrated network equilibrium and Walrasian equilibrium into asingle model. He introduced space in his structure of general equilibrium, wherein a locationin space is uniquely assigned to each economic agent and each movement across space createsa demand for transport. The search for global equilibrium is composed of the search for theequilibrium values of prices, the quantities of the goods and the costs associated with the links
so that there is network equilibrium and economic equilibrium calculated simultaneously. In
1998, Friesz, Suo and Westin formulated an integrated network equilibrium- generalequilibrium model with a transport sector. However, the model contained only one mode oftransport. The demand for transport is non price-elastic and depended only on the productionlevel of each sector.
In Asia in 2001, transport-oriented SCGE models were formulated in Japan by Miyagi, withthe goal of estimating the geographic impact of a major highway link in central Japan. OtherSCGE models for Japan include those constructed by Koike et al in 2000 and Ueda et al in2001 which sought to analyze the economic impact of a major earthquake which damaged thehigh speed rail network to Tokyo. For Korea, the work of Hewings and Kim 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 imperfectly competitive markets(2003). For the Philippines, no SCGE model has been estimated yet and this model is the firstendeavor in such direction. This is the first spatial general equilibrium model devised for thePhilippines. Earlier papers written by Dakila & Mizokami (2006) showed various applicationsof the five region model in estimating impact of different transport-related policy shocks on
significant regional macroeconomic variables. All previous CGE models about thePhilippines were national in scope. These include the Agricultural Policy Experiment (APEX)
model (1992); Bautista C. model (1987 & 1992); Bautista R. model (1986); Clarete (1984 &
1991); Cororaton (1989); (1988); Habito (1984 & 1989) and Jemio & Vos (1993).
In Europe, Brocker developed CGEurope in 1999 which covered 800 regions. Brocker’s
model quantified regional welfare effects of transport related and financial-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); the RAEM model for the Netherlands377

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

(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 that developed by Lofgren and Robinson
in 1999.
Despite the models already devised, general equilibrium models with a transport sector havecertain limitations when used for transport project appraisal. An article by Tavassy, Thissen,
Muskens, Oosterhaven (2002) pinpointed the main limitation of utilizing SCGE models is in
the specification of transport costs within a spatial framework. Other areas of concern in
SCGE were the problem of locational boundedness of economic activities and the impact oftransport infrastructure on passenger flow. They attributed these problems to the lack of
empirical data on the consumption of transport services by various sectors, a possible
mismatch in the definition of transport costs as they are produced in transport models and as
they are inputted in SCGE models. The article also described how the use of four-steptransport models introduces inconsistencies in appraisal results. It stated that the use of
iceberg transport costs is empirically acceptable in one-sector economy but can lead tounderestimation of impact in those sectors most sensitive to the reduction in transport costs
when applied to multi-sector models In the end, they proposed that hysteresis and locationalboundedness of production should be taken into account in modeling transport within SCGE
framework.

3. PRESENTATION OF FIVE-REGION GENERAL EQUILIBRIUM MODEL
3.1 Database
The benchmark data for the study are taken from a five-region social accounting matrix(SAM) constructed by the authors for the Philippines, using 1994 Philippine interregional
input-output data (Mizokami & Dakila: 2005). The delineation of regions is based on thearchipelagic geography of the Philippines. The disaggregation into seven sectors (with threetransport sectors – water, air, and land mode) is done to enable the researcher to look into theimpact of a change in transport capacity on alternative modes of transport and non-transport
sectors. Households are divided into three income groups; namely low-income households,
middle-income households and high-income households. Low-income households are all
those who earn below the official Philippine regional poverty thresholds. The high-income
households are those who earn 250,000 pesos and above annually, which is the highest
income bracket in the Family Income and Expenditure Survey. The rest of the households
comprise the middle-income class.
The foregoing five-region SAM was used to calibrate the baseline values of the SCGE model.
A SAM represents transactions in a complete economic system during an accounting period,
usually one year. It integrates, within a macroeconomic framework, several detailed accounts
for factors of production and institutions—especially households. Round (2003) elaborated
on the main features of a SAM, which are threefold: (1) The accounts in a SAM are
represented as a square matrix, in which the incomings and outgoings for each account are
shown in corresponding matrix rows and columns. The transactions are shown in the cells, so
the matrix displays the interconnections among agents in an explicit way. The corresponding
row and column totals in the SAM must be equal to each other. (2) The SAM iscomprehensive:it portrays all the economic activities of the system (consumption, production,
accumulation and distribution), although not necessarily in equivalent detail. (3) The SAM is
flexible, in that, although it is usually set up in a standard, basic framework. There is a large

378

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

measure of discretion both in the degree of disaggregation and in the emphasis placed on
different parts of the economic system.
Table 1 Disaggregation of model
Production sectors
Agriculture (Ag)Industry (Ind )Water transport (WtrSrv)
Land transport (LndTr)
Air transport (AirTr)
Other services (OtSrv)
Government (Gov)

Regions
National Capital Region (NCR)
Northern Luzon (NOL)Southern Luzon (SOL )Visayas (VIS)
Mindanao (MIN)Households
Low income (Low)
Middle income(Mid)High income (High)When a national SAM is split into regional SAMs (RSAM), the flow of income fromproduction units to consuming units is given a spatial dimension. In line with this objective, afive-region SAM was constructed in order to analyze ripple effects of particular shocks,
particularly on regional income disparity and other aspects of the regional economies. This
RSAM is presented in detail in Dakila, C. and Mizokami, S. (2006). The main data source forthe study is the 1994 five-region Philippine inter-regional input-output (PIRIO) table, which
regrouped the 15 administrative regions of the country in 1994 (now 17) into five greater
regions according to geographic proximity (Secretario: 2002). This regional classification iscarried over to the present paper.
As described in Dakila and Mizokami (2006), there were three major activities undertaken to
derive the RSAM. First, the coverage of the 1994 PIRIO was expanded. In particular, thepersonal consumption expenditures component in the final demands columns was further
disaggregated according to income class. Greater attention was paid to the components ofvalue added across sectors and import transactions were broken down into CIF values andtariffs and import taxes. Second, the PIRIO was transformed from an open-type input-output
model (i.e., one with an exogenous household sector) into a closed input-output model, withthe household sector endogenized within the production system of regional economies. This
closed I-O framework accounted for the balance between the different sources of incomes ofhouseholds. For this, the Philippine Family Income Expenditures Survey (FIES) was theprimary data source. Finally, the multi-region SAM was compiled based on the expanded
PIRIO. The interregional flows derived from multi-regional SAM were verified to be similar
to those in survey of interregional freight and passenger flows conducted by JICA and DOTC
team in the Philippines.(Dakila & Mizokami : 2006)
3 .2 Assumptions and Description of the Model
The assumptions adopted are as follows (1.)All product and factor markets operate under
perfectly competitive conditions. (2) Economic agents like households and firms maximize an
objective function subject to constraints. Households maximize utility whereas firms
maximize profit. (3) Equilibrium is defined as a state where the actions of all agents aremutually consistent and can be executed simultaneously. Quantities adjust in the model andprices follow to equate the notional and effective demand for labor. (4) In this model,
adjustment to equilibrium is implemented by specifying that markets adjust to minimize thesum of excess supplies. (5) Among the seven-production sectors; three belong to the transport
sector, namely, water transport services sector, air transport services sector, land transport
services sector. The demand for services of each type of transport mode is a derived demandassociated with the demand of intermediate production goods. (5) Between the two factors of
production, capital is immobile and labor is mobile among the five regions. (6) The economy

379

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

has 36 markets. This is composed of thirty-four product markets with each of theaforementioned five regions with seven production sectors each (except NCR with 6 sectors),
one capital market and one labor market. There is an insignificant agricultural sector in NCR
and is therefore omitted. The framework takes off from Mizokami model of two region
economy in the Philippines with four production sectors including transport. (Mizokami et al:2005). However, there are variations in specification of the production function. A three-level
nested production function is estimated. The transport sector intermediate input is isolated in
the second level of production function. A more detailed disaggregation of transport sector isdelineated – namely water transport services sector, air transport services sector and landtransport services sector. Furthermore, another point of difference is that households in eachregion are decomposed into three income levels - low, middle and high. Finally, the rest-ofthe-Philippines region is divided into four regions namely Northern Luzon, Southern Luzon,
Visayas and Mindanao vis-à-vis National Capital Region.
The model presented below is a simple representation of an economic system withassumption of perfect competition in goods and factor markets and constant returns to scale inproduction. This is the start of promising experiments into the usage of SCGE modeling fordeveloping countries with limited data like the Philippines. Also, it is recognized that recent
advances in spatial computable general equilibrium (SCGE) modeling have incorporated suchassumptions as increasing returns to scale, agglomeration economies, and imperfectcompetition in the model. Given that SCGE modeling is at its infancy stage in the Philippines,
these modifications will be undertaken in future studies.
The originality of this model lies in the fact that this is the first spatial equilibrium model witha disaggregated transport sector in the Philippines. All Philippine CGE models devised in thepast have been national in scope. This model differs from all previously surveyed as it
manifests its transport-orientedness by the isolation of transport intermediate inputs in thesecond level of three-nested production function. This allows the assessment of the impact ofany exogenous shock on transport-related variables such as transport intensity, environmental
effects via application of emission coefficients and degree of substitution among different
transport modes. This is also a first attempt in constructing a five-region social accounting
matrix as database for a spatial computable general equilibrium (SCGE) model in thePhilippines.
3.2.1 Household Sector
The model distinguishes between 15 representative households, with 3 household types(representing the low, middle, and high income classes) for each of the six regional groupings
distinguished in this paper. The preferences of each household type are summarized by acorresponding Cobb-Douglas utility function:
U h = ∏ Cih

δih

(1)

i

Where δih is the elasticity of the utility of the hth household with respect to consumption of theith good. Each representative household maximizes its utility subject to its income constraint,
which we describe below.
For each region, household labor income is assumed to be equal to the sum of the labor
incomes that each household income group earns from supplying labor within the region. The
endowments of labor of different income classes within a region are taken to be a constant.
This then determines how labor income is distributed within each region.

380

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Since capital is fixed, then each household income group is assumed to own a fixed share of
total capital, and this ratio is maintained through the policy experiments. Household income
is calculated as the sum of labor income ( w i Li ) plus that portion of capital income that
accrues to the households ( λ h Σi ri K i ), plus transfers from government and from the rest of theworld. The latter two are exogenously determined. Thus, if we partition the indices h and i sothat the rth partition belongs to the rth region, then we obtain total income per household typeas:(2)
Yh,r = ωh,r ∑ i∈r w i Li + λ h,r ∑ i ri K i + TrGOV,h,r + TrROW,h,r
where the ω’s are the labor income distribution parameters, and, as indicated, the summation
is for industries belonging to the rth region. Total disposable income is found by subtracting
direct taxes imposed on the household from the foregoing quantity:
Yd h = Yh (1 − τh )(3)
where Yd is disposable income and τh is the direct tax rate imposed on household h. Note thatthe summation now runs within each household type, so that we have dropped the subscript r
referring to the partitioning across regions.
Each household type is assumed to consume a constant proportion of its disposable income.
Thus, households maximize utility subject to the budget constraint(4)∑ i pdiCih =ch Yd h
Where pdi is the domestic price of the good and ch is the average propensity to consume ofhousehold h. Given the Cobb-Douglas utility function, the first order conditions yield thefollowing consumption demands for each commodity by each household type in each region:
Ci,h,r = δi c h,r ⎡⎣ωh,r ∑ i∈r w i Li + λ h,r ∑ i ri K i + TrGOV,h,r + TrROW,h,r ⎤⎦ (1 − τh,r ) / pi
(5)

3.2.2 Production Sector
Production is modeled assuming a three-stage production function. At the first stage, capitaland labor are combined to produce value-added, using a Cobb-Douglas production
technology.
α1−αVi = A i K i i Li i
(6)

Where for sector i and region r, V = value added, K = capital, L = labor, α = share of capitalin value-added, and 1-α = share of labor in value-added. This specification of the CobbDouglas function assumes constant returns to scale. Capital is assumed to be immobile acrosssectors while labor is mobile.
In stage 2 of the production process, value-added is combined with non-transport intermediate
inputs under a Leontief technology, to produce a composite good, which is output net oftransport.
⎡
⎤
X NTi
X 2i
Vi
X iNT = min ⎢X1i
(7)
⎥
a
,
a
,
…
,
a
,
a
1i
2i
NTi
V,i ⎦
⎣

Finally, stage 3 combines output net of transport with transport intermediate inputs under a
Cobb-Douglas production function to yield total output gross of transport of commodity i(XT,i).

(

X T,i = Bi X i) W A LaNT β1i

β2i

i

β3i

i

β4i

(8)

i

381

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

where W, A and La represent the different transport intermediate inputs that go into sector i,
namely, water, air and land transport. This specification allows substitutability between thevarious transport modes. Total output of sector i (Xi) is found by summing together total
output gross of transport of commodity i (XT,i), indirect taxes on i (Tindirect,i), direct taxes
imposed on firms in sector i (Tdirect,i), imports of i (Mi), tariffs imposed on i (Tari), and net
dividends from the foreign sector into sector i (DivFor,i).

Xi = X T,i + TIndirect,i + TDirect,i + M i + Tari + Div For,i(9)

The firm is assumed to maximize profits. Because of the nature of the production function,
profit maximization can be described in three stages. The bottom stage entails choosing the
optimum levels of capital and labor so as to maximize the contribution of value added to
profits. At the second stage, as noted above, value-added is combined with other intermediate
non-transport inputs in a fixed coefficients (Leontief) technology to produce output net oftransport. Finally, the top stage determines the optimal combination of transport inputs todeliver output to the region of destination. Then for commodity j, the optimization problem is
MaximizeΠ j = pd j X j − ∑ pd j Mat i , j − pva j V j

(10)

i

subject to
β1j

β

β

β

X j = B j X NT
W j 2j A j 3j La j 4j
j
X
Vj
⎡X
⎤
X NT
… NTj
= min ⎢ 1jja NTj , a V, j ⎥⎦⎣ a 1j ,α1−αVj = A j K j j L j j(11)

Where Π is total profits, Matij is the matrix of intermediate inputs of each commodity into
commodity j, V represents value added, and pva is its corresponding price.
3.2.3 Government and the External Sector.
The model incorporates a national government sector, i.e., the behavior of local government
units is not considered. Government enters the economy in several ways: it purchases output
from each sector, imposes indirect taxes on production and tariffs on imported goods, anddirect taxes on income of each household type. Government expenditures on each commodity
are taken as exogenous in the model, while taxes are endogenous.
Tariff revenues per commodity equal the product of the tariff rates and import values:
Tari = tari ( mi )(12)

Where Tari and tari are total tariff collections from i and the tariff rate on commodity i,
respectively. Indirect tax collections are given by the product of the indirect tax rate imposedon domestic production and the rate imposed on imports of the product:
TIndirect,i = tind i ( d i + mi (1 + tari ) )(13)
Direct tax collections per household type in the model are computed as:
TDirect,h = Yh − Yd h
At this stage of model specification, imports and exports are taken as exogenous.

382

(14)

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

3.2.4 Investment-Saving Balance
Total household savings in the model are given by the aggregate difference between
household disposable income and consumption expenditures:
Sh = Σ h ( Yd h − C h )(15)
We introduce a balancing factor (φ) to account for any discrepancies between measured
savings and investments.
Total government savings are the sum of the various revenue sources minus total government
purchases of the outputs of the various sectors, total government transfers to households, andtotal net transfers of the government to the foreign sector:
SG = Σi Tari + Σi TIndirect,i + Σ h TDirect,h − Σi G i − Σ h TrGOV,h − TrGOV,FOR(16)

Total foreign savings, SFOR, are given by the current account deficit minus net dividends toforeigners. Therefore, total savings are
STOTAL = Sh + SGOV + SFOR
(17)
Conceptually, total savings should equal total investment. As noted previously, ourframework allows for statistical discrepancy by introducing a factor φ which transforms
savings to investments. Investment distribution per sector is then modeled as constant
proportion of total investment, with the distribution coefficients γi calibrated according to thesectoral distribution of investment in 1994:
Ii = γ i φ ( STOTAL )(18)
3.2.5 Demand
Total intermediate demand for commodities by the firm arises from its maximization of
profits subject to the three-level production function. At the first level, the first ordercondition for profit maximization entails equating the marginal product to the marginal cost oflabor.
∂V
pva i * i = w i∂LiV
pva i (1 − α i ) i = w i(19)Liwhere the marginal product of labor for each production sector is evaluated assuming that
capital is immobile across sectors. For any given employment, equilibrium entails that thecorresponding level of production equal the demand forthcoming at the employment level.
Similar equations hold for the choice between output net of transport and the various transport
inputs, at the third level of the production function. This equilibrium condition together with(11) determines pva. We turn to this in greater detail in the section on prices.
At the second level, each production sector combines value-added and every non-transportintermediate input according to a fixed proportions technology:NT
Mat i, j = a ijX j(20)
where i runs through all the non-transport intermediate inputs and value added for each sector,
j runs through all the production sectors in the economy, Matij is the matrix of interindustry
flows in the economy, aij represents the fixed coefficients technology, and, as before XjNT is
output net of transport for the jth sector.

383

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Final demand in the economy originates from households (consumption demand), firms(investment demand), government spending, and the foreign sector (export demand).
Consumption demand by households originates from the maximization of the utility function,
as described in previous section. Although for simplicity, firms’ investment demands are notdescribed explicitly in terms of optimization, the level of investment is determined by thetransformation of savings into such. Government and export expenditures are taken to be
exogenously determined.
The domestic demand for commodity i consists of the total intermediate demand, plus thetotal final demands for consumption, investment, and government purchases, while the totalcomposite demand, represented by Qi, is the sum of the domestic demand and exports:
Q i = ∑ Mat i , j + ∑ C h ,i + I i + G i + Exports i
(21)
j

h

3.2.6 Prices and Equilibrium
For any given employment level, equilibrium entails that the corresponding level ofproduction should equal the demand forthcoming at the employment level. This requirement,
together with the first order conditions for profit maximization by the firms in each region,
determines the price levels in the economy, relative to the price of labor. The labor price isassumed to be the numeraire, and is thus taken to be fixed. Since capital is a fixed factor, wetake returns to capital as a residual determined by the identity:
pva i * Vi − w i0 Li )
(
ri =
(22)
k i0
The total product cost can then be built up from the components in a standard way. Thus,
average cost per unit isΣ pd Mat j,i + pva i Vi
ACi = j j
(23)
Xi
Where pdi is the domestic (tax-inclusive) price of i. In equilibrium, the average cost equals
the composite price pqi of the commodity (the composite price is the peso price of both
domestically produced and imported commodities).
The excess supply for each commodity is given by:
ESi = X i − Qi

(24)

The model treats all the foregoing relationships as constraints in a nonlinear programming
problem. Markets are assumed to operate so as to minimize the value of sum of squaredexcess supplies for all commodities; i.e., the objective of the programming problem is tominimize the quantity2Ω = Σi pq i * ESi
(25)

(

)

In equilibrium, therefore, the unit cost is divisible into three parts: (1)∑ pd q , where the
j

j

ji

Xi
j’s are the non-transport inputs give the cost of non transport intermediate inputs per unit ofX; (2) the same formula with the j’s taken to be the transport inputs yields the transport
w L + rK
margin; and i i i i is the cost of value added per unit of X.
Xi

In the aggregate, the equilibrium condition is384

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Y = C + I +G + X – M(26)

where Y is aggregate supply, C is total consumption expenditures of the national economy,
I is total investment expenditures of the national economy, G is total government
expenditures of the national economy, X are total purchases of locally-made goods byforeign sector, and M are total purchases of foreign-made goods by domestic residents ofnation4. EMPIRICAL RESULTS

The succeeding results indicate the impact of technological improvement in each transportmode – land, air and water – in each of five regions
4.1 Impact on Gross Output
4.1.1 Impact on Gross Output ofAll
Transport Modes Across All Five Regions
Figure 1 indicates that technological
NCR= National
Capital Region
improvement in NCR land transport services
NOL = NorthernLuzon
had the highest impact on output. Therefore,
SOL = Southern
Luzon
focus should be placed on improving the
VIS = Visayas
MIN = Mindanao
management of land transport services in the
National Capital Region. This includes
building wider road network, faster trains, or
acquiring better land transport traffic
equipments. It is almost double the increase in
output of second and third biggest increment –Figure 1 Change in Gross Output of all Regional
land transport sector Northern Luzon and land
Transport Modes
transport sector Visayas.

4.1.2 Impact on Gross Output Across Regional Production Sectors Due to TechnologicalInnovation in NCR Land Transport Services Sector
Figure 2 indicates that the concentration of benefitsof this exogenous policy shock is in production
sectors in the National Capital Region namely landtransport services, industry and other services
sector. While this reinforces the primacy of the
National Capital Region due to existence of
agglomeration economies, spillover effects inSouthern Luzon’s industrial and agricultural
sectors; and also in Visayas’ industry and otherservices sector also occur. Mindanao’s agricultural
sector also benfitted from this policy shock. This
Figure 2 Change in Cross Output of
indicates that interregional inequity in terms ofRegional
output is reduced with exogenous shock.
4.2 Impact on Relative Welfare

4.2.1 Impact on Relative Welfare Across All Transport Modes In All Five Regions

385

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

A comparative analysis on impact of transport infrastructure improvement on relative welfare
across regions (measured by change in equivalent variation – EV) is undertaken. Fig.3
indicates that technological change in NCR land transport and NCR air transport sectors havethe highest impact on relative welfare across all fifteen regional household income groups.
% ch_EV Lnd NCR% ch_EV Lnd NOL% ch_EV Lnd SOL% ch_EV Lnd VIS% ch_EV Lnd MIN% ch_EV Wtr NCR% ch_EV Wtr NOL% ch_EV Wtr SOL% ch_EV Wtr VIS% ch_EV Wtr MIN% ch_EV Air NCR% ch_EV Air NOL% ch_EV Air SOL% ch_EV Air VIS% ch_EV Air MIN

Figure 3 Impact on relative welfare across all regional HH income groups in all five regions
Focusing on NCR land transport improvements, Figure 4 indicates that improvement in NCR
land transport has significant impact on middle income groups across the five regions. This
maybe because most of them have their own land vehicles for work or leisure or business. The
red middle bar graph in each box in Fig. 4 indicates the middle class’ change in welfare.
4.2.2 Impact on Relative Welfare Across Households of Technological Improvement in NCRLand Transport Sector
The results indicate that the% Change EV - NOL% Change EV - NCR
middle income households in
NCR have the largest gain in
relative welfare, followed by
NCR high income groups andthen NCR low income group.This
affects intra NCR regional equity.
% Change EV - VIS
It is also important to note that % Change EV - SOL
low-income
households
inSouthern Luzon and the Visayas
have the next highest incremental
gain in welfare. This maybe dueto the fact that households inthese regions use intermodal% Change EV - MIN
scheme as in RORO (roll-on rolloff) which is classified as
mobility via road .The red bar
graph in the extreme left
represents welfare gains of the
Figure 4 Relative Change in Welfare due tolowest income groups. Those
Technological Improvement in the NCR Land
households which belong toTransport Services Sector
Southern Luzon, Visayas andNorthern Luzon experienced
relatively high gains in equivalentvariation. These households benefited from spill-over effects due to their proximity to the
NCR. On the other hand, high income group in NCR had the highest relative welfare gain.
LOW

MID

HIGH

HH INCOME GROUP

LOW

LOW

LOW

MID

HIGH

MID

HIGH

HH INCOME GROUP

MID

HIGH

HH INCOME GROUPHH INCOME GROUP

LOW

MID

HH INCOME GROUP

386

HIGH

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

This is explained by the fact that the location of improvement in land transport infrastructurein NCR creates immediate benefit to households residing in that region.
4.3

Impact on Interregional Flows of NCR Land Transport Infrastructure
Improvement
Figure 5 herein shows theimpact on interregional flowsof choosing increase in landtransport infrastructure via aspatialimpedance
ratio.
Impedance ratio is defined as
the ratio of interregional flows
over transport capacity. Thechange in impedance ratio
indicates the direction andrelative magnitude of change ininterindustry flow caused by
the policy shock.
The
following observations are
made: (1) All interindustry
flows
with
originanddestination as NCR landtransport services sector had
positive changes in impedance
ratio which signify trade creation; (2) Most trade flows with air transport services sector as
destination sector in five regions experienced a decline in impedance ratio caused by a decline
in interindustry flows. (3) Trade flows with water transport service sector as origin
experienced the same trend. This may indicate some substitution of land transport in lieu of
air and water transport mode for transport of goods and services across the Philippines (4)Another positive impact is that all traffic flows with destination in agricultural sector inSouthern Luzon, Visayas and Mindanao experienced an increase in trade flow as manifested
by increase in impedance ratio. This augurs well for the agricultural sector which has one of
the highest transport margins among production sectors. (4) Trade creation and not diversion
is manifested by spillover effects as can be seen by increase in flows with Southern Luzon
industry as destination. (5) Many interregional flows in an archipelago like the Philippines aretransported via RORO (roll-on-and roll-off; via both road and water transport modes).
Philippine secondary data classify these interregional flows as transported via land. Hence
there exists substitution in favor of land transport in many interregional flows whentechnological change occurs in land transport infrastructure.

5. CONCLUSION

The above discussion shows how an interregional system-wide model of the economy can beused to pinpoint the transport infrastructure improvement which generates the highest
increment in output in the economy. Technological improvement in transport sector results
too many micro-level changes as in enhancement of speed and capacity which eventuallyleads to lesser travel time. However, given the secondary nature of database constructed, i.e.
the multi-regional SAM, the impact of an exogenous shock on important details such as lesser
travel time cannot be captured in detail at a micro level.

387

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Technological improvements in NCR land transport infrastructure project had the greatest
impact on gross output of most of the thirty –four production sectors. In terms of intraregional
equity, the low-income groups of non-NCR regions like Southern Luzon and the Visayas
registered significant relative welfare gains. There was also an increase in interindustry flows
across regions in the Philippines as manifested by higher impedance ratios especially for
traffic flows with NCR land transport services as destination sector.
Building more NCR land transport infrastructure has significant spillover and equity effects
across the Philippines. It affects the spatial pattern of economic benefits, the value of
economic benefits across production sectors and the distribution of benefits among householdincome groups. A concrete example of land transport capacity enhancement in NCR is thecompletion of the Skyway which will connect Northern Luzon and Southern Luzon bypassing through NCR. This type of land infrastructure improvement will lead to substantial
trade creation among the Luzon regions and NCR. Government is also planning to extend theLight Railway Transit (LRT) so that passengers can travel from Bulacan (Northern Luzon) toBatangas (Southern Luzon) by rail with no need for intermodal transfers.
In the end, targeting the appropriate type and location of transport infrastructure will lead tofavorable results on efficiency of regional economic performance and on intraregional andinterregional equity .

ACKNOWLEDGEMENT

The first author wishes to acknowledge support from the Japan Society for the Promotion of
Science through the Department of Science & Technology, Philippines.

REFERENCES

Bautista,Carlos (1987) Macroeconomic Adjustment: An Applied Non-Walrasian General
Equilibrium Approach ,Unpublished Ph.D. dissertation, U.P. School of Economics.
Bautista, Romeo (1988) General Equilibrium Effects of Increasing Productivity in Philippine
Manufacturing, with Special Reference to Food Processing. Journal of Philippine
Development, Vol.15 No.10, 223-243.
Brocker, Johannes (1999) Trans-European Effects of Trans-European Networks: A Spatial
CGE Analysis. Mimeo. University of Technology, Dresden.
Buckley, Patrick (1992) A Transportation-Oriented Interregional Computable General
Equilibrium Model of the United States. Annals of Regional Science.Vol 26, SpringerVerlag, 331-348
Caspersen, Eriksen & Larsen . (2000) A Spatial General Equilibrium Model to Evaluate theGreat Belt Link in Denmark. AFK, Institute of Local Government Studies,
Copenhagen
Clarete,R. & Warr,P.(1992).The Theoretical Structure of the APEX Model of thePhilippine Economy. Unpublished Manuscript.
Cororaton, Caesar (2000) Philippine Computable General Equilibrium Model (PCGEM).
Philippine Institute for Development Studies. Discussion Paper Series 2000-03.
Dakila C, & Mizokami S. (2006) Reconciling O-D Freight Flow Data and Interregional I-O
SAM Data for SCGE Database in a Developing Country. Infrastructure Planning388

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Review. Vol, 23, No.3, Japan Society of Civil Engineers (JSCE) Publication, Tokyo,
Japan, 725-736.
Dakila C. & Mizokami S. (2006) A SAM Based General Equilibrium Model of the Impact of
the Impedance Factor in Transport on Regional Economies in the Philippines. Paper
presented during the 2006 Intermediate Input-Output Meeting in Sendai City, Japan;
under the auspices of International Input-Output Association, July 26-28, 2006.
Dakila C.G.(2007) Impact of Transport Infrastructure Investment on Interregional
Economic Activity & Regional Welfare: A SAM-Based General Equilibrium
Approach. Dissertation Submitted to Graduate School of Science & Technology,
Kumamoto University, Japan, March 2007.
Friesz T. L., Harker P.T., Tobin R.L. (1998) Integration of Freight Network and Computable
General Equilibrium Models. In Lundquist,L, Mattson L.G., Kim T.J. eds - Network
Infrastructure & the Urban Environment. Springer Verlag, Berlin.
Harker, P.T. (1987) Predicting Intercity Freight Flows. VNU Science Press. Utrecht, The
Netherlands.
Hussain & Westin (1997) Network Benefits from Transport Investments Under Increasing
Returns to Scale: A SCGE Analysis. Umea Economic Studies, Paper No. 432. Center
for Regional Science (CERUM), Umea.
Ivanova, Vold & Jean-Hansen. (2002) PINGO A Model for Prediction of Regional &Interregional Freight Transport. TOI Report 578/2002. Institute of Transport
Economics, Oslo.
Japan International Cooperation Agency (JICA) - Dept. of Transportation & Communication(DOTC).(2004)The Inter-Regional Passenger and Freight Flow Survey in the
Republic of the Philippines – Final Report.
Koike, Ueda & Miyashista. (2000).Spatial Computable General Equilibrium Model for
Passenger Transport Improvement: Evaluation of Japanese New Shinkansen Project.
Presented at the World Conference of RSAI, Lugano. May 2000.
Lofgren & Robinson (1999) Spatial Networks in Multi-region Computable General
Equilibrium Models. TMD Discussion Paper No. 35, IFPRI, Washington, D.C.
Miyagi, T. (2001). Economic appraisal for Multiregional Impacts by A large Scale
Expressway Project: A SCGE Approach. Tinbergen Institute Discussion paper: TI2001 066/3. http://www.tinbergen.nl/home.html
Mizokami, Shoshi. (2002) Utilizing Interregional Input-Output Analysis in Interregional
Transportation Policy: Methodology and Case Study in Japan. In T. Ohmachi & E. Roman(eds.), Metro Manila: In Search of a Sustainable Future. JSPS-Univ. of Philippines
Press, Quezon City, 98-110.
Mizokami S.(2003) An Interregional Freight Demand Model by Spatial Applied General
Equilibrium Analysis. Journal of Eastern Asia Society for Transportation Studies,Vol. 5, 287-299.
Mizokami S, Itose M & Dakila C. (2005) Application to the Developing Country of SCGE
Model Based on 2-Regional SAM. Journal of Eastern Asia Society for Transportation
Studies, Vol 6, 3985-4000.
Mizokami, S. & Dakila C. (2005) An Analysis of the Linkage Between 2004 JICA-DOTC
Commodity-Passenger Flow Survey & Interregional Flows in Multi-Regional SAM in thePhilippines.in CD-Rom of Proceedings of the 31st Conference of Japan Society of Civil
Engineers. Hiroshima University, Japan.
National Statistics Office (1995) 1994 Family Income & Expenditure Survey, Manila.
National Statistics Office (1995)1994 Census of Establishments, Manila, 1995.
National Statistical Coordination Board. (1995) Manual on Philippine System of National
Accounts. Draft paper.
389

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

National Statistical Coordination Board. (1990) 1990 Social Accounting Matrix for thePhilippines. NSCB Monograph.
Round, J. (2003) Constructing SAMs for Development Policy Analysis: Lessons Learned andChallenges Ahead, Economic Systems Research, Vol 15. No. 2, 161-183.
Secretario, F. (2002) 1994 Philippines Interregional Input-Output Table – 5 Region, Japan
Society for the Promotion of Science-Manila Project.
Tavasszy L., Thissen M, Muskens J & Oosterhaven J. (2002) Pitfalls & Solutions in the
Application of Spatial Computable General Equilibrium Models for Transport Appraisal.
Proceedings of the 30th European Regional Science Association Papers. PRTC
Education and Research Services Ltd; London.
Appendix 1 List of Endogenous and Exogenous Variables
Variable namematij
intpnti
intpi
Intptri
Vi
XNTi
XTi
Xi
ESi
GDP
Ω
Imped

Description
Production sector
Intermediate inputs flow from industry i to industry j
Total non-transport intermediate inputs into industry i
Total Intermediate Inputs into industry i
Total transport inputs into industry i
Total value added in i
Output (net of transport) in industry i
Output (gross of transport) in industry i
Total output in industry i
Excess supply of commodity i
Gross domestic productSum of Squared Supplies in the Economy
Impedance ratio (trade flow/capital input or capacity)∆ IMPED Ratio

Change in Impedance Ratio

Li
Ki
YLH
YLR
YL
YK
HHOSi
HHOS
YDH
YH
CH,i
CH
UH
EVH
Utot
Di
Qi
PVA,i
PD,i
PM,i
PQ,i
PX,i

InputsLabor demand in industry i
Value of capital inputs in industry i
IncomesLabor income of household hTotal labor income for the region
Total labor income
Total capital income
Total household share in capital income of industry i
Total household share in capital income
Total disposable income of household hTotal income of household hDemandConsumption by household h of commodity i
Total consumption by household hUtility of household hEquivalent variation for household hTotal utility
Domestic demand for commodity i
Composite (domestic + foreign) demand for commodity i
PricesPrice of value added of commodity iDomestic price of commodity i including tax
Domestic price of imports of commodity i
Composite (domestic + export) price of commodity i
Price of output of commodity i

390

Size
1156
34
34
34
34
34
34
34
34
1
1
34 x 34=17,340
34 x 34=17,340
34
34
3X5=153X5=151
1
34
1
3X5=153X5=153X5X34=510
3X5=153X5=153X5=15
1
34
34
34
34
34
34
34

x

15

x

15

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

Return on capital in industry i
Consumer price index
Government
Direct tax collections from household hTDirect,H
Total direct tax collections
TDirect
Total indirect tax collections from industry i
TIndirect, iTotal indirect tax collections
TIndirect
Tariff collections from inputs into i
TariTotal tariff revenue of government
Tar
Total income of government
YG
Savings
Savings of household hShTotal savings of households
SH
Savings of government
SG
Foreign savingsSF
Total savingsST
Total Endogenous Variable
Exogenous variables and parameters
Production sector
Input-output coefficientaij
Coefficient of value-added in input-output matrix
aVi
Scale parameter in 1st level Cobb-Douglas production function
Ai
Coefficient of inputs in 1st level Cobb-Douglas productionαifunction for i
Coefficient of inputs in 3rd level Cobb-Douglas production
βifunction for i
InputsReal capital in industry i
KRi
DemandImports of commodity i
Mi
Exports of commodity i
Exportsi
Marginal Propensity to Consume of household hCh
Parameter in Cobb-Douglas Utility Function for household h
δih
ri
CPI

wi
PE,i
PM,i
HHOSH
YFORH
λ
TrDirect,H
TrIndirect, iTarri
YGOV,H,R

PricesWage rate in industry iWorld price of exports of i
World price of imports of i
Incomes
Total household operating surplus from informal transactions
Household transfer receipts from foreign sources
Capital income distribution to households
Government
Direct tax rate on household hIndirect tax rate on industry i
Tariff rate on imports of i
Total government transfers to household hTotal exogenous variables

391

34
1
3X5=15
1
34
1
34
1
1
3X5=151
1
1
137,210
20X20=400
20
34
34X2=68
34X4=1363434
34
3 X 5=15
34 X 15=510
34
34
34
3X5=153 X 5=15
34
3X5=1534
34
3X5=151549