DEVELOPING A NEW SPATIAL COMPUTABLE GENERAL
EQUILIBRIUM MODEL FOR NORWAY
Wiljar Hansen
Institute of Transport Economics, Oslo, Norway
1. INTRODUCTION
Larger infrastructure investments often generate considerable debate
between those seeking to justify the investment and those trying to refute the
need for that particular infrastructure extension. The favorable argument
among those justifying the proposed investment is the presents of wider
economic benefits not captured by the traditional cost-benefit analysis
conducted as part the investment appraisal.
With a reasonable degree of perfect competition in most markets, most
transport researchers claim that the cost-benefit framework will capture all
benefits associated with the investment since the users will want to pay
exactly their value of the traffic improvement. However, as also noted by most
transport researchers, most markets are not perfectly competitive. Market
imperfections lead to economic benefits that are not adequately reflected in
the transport appraisal. There is a large literature on the wider economic
benefits of transport infrastructure investments (Mohring Jr and Williamson
1969; SACTRA 1999; Oosterhaven and Elhorst 2003; Vickerman 2007;
Lakshmanan 2010), using a variety of scientific methods where the most
common are (Tavasszy, Thissen et al. 2002):
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Micro surveys with firms
Estimation of quasi production functions
Partial equilibrium potential models
Macro and regional economic models
Land Use / Transportation Interaction models
Spatial Computable General Equilibrium models
The aim of this paper is to propose a new Spatial Computable General
Equilibrium (SCGE) model for Norway. The proposed model is in the new
economic geography (NEG) tradition and an extension of the existing
Norwegian SCGE model, PINGO (Ivanova, Vold et al. 2002; Vold and JeanHansen 2007). SCGE models are spatial and operational extensions of
General Equilibrium models. Modern classics in theoretical general
equilibrium analysis are Debreu (1959) and Arrow and Hahn (1971). A review
of CGE modeling is found in Shoven and Whalley (1992).
This paper is structured into 6 sections. The following section gives the
rationale on why the traditional cost-benefit framework is unable to capture the
wider economic benefits of infrastructure investments. The section discusses
the market imperfections in the transport sector, in the product markets and
the labour market. Section 3 presents SCGE modeling as a tool to quantify
the wider economic benefits associated with larger infrastructure investments.
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Section 4 presents the current Norwegian SCGE model PINGO and its
shortcomings. Section 5 proposes further development to the PINGO model in
order to capture wider economic effects of traffic improvements, and section 6
concludes the paper.
2. WIDER ECONOMIC BENEFITS OF TRANSPORT INFRASTRUCTURE
INVESTMENTS
Vickerman (2007) define wider economic benefits to be all economic benefits
not captured in the direct user benefits of the type which are normally
analyzed in a well constructed transport cost-benefit analysis after allowing for
environmental and other directly imposed external costs.
In transport appraisal it is traditionally assumed that a well specified costbenefit analysis (CBA) will capture all the relevant impacts on the economy.
Simply put; In a CBA one adds up all the benefits associated with a policy
alternative, subtract all the costs, and choose the alternative that maximizes
the net present value. As long as all markets are perfectly competitive the
user benefit will equal the total benefit of the investment (Kanemoto and Mera
1985; Jara-Diaz 1986). Adding spillover effects in a perfect competitive
environment will only result in double counting (Mohring 1993). Neither the
transport sector nor the transport using sectors are perfectly competitive. If the
markets are imperfect, i.e. the price on important market goods exceeds
marginal cost, such a deviation from the first-best solution implies that the
traffic improvement produce impacts in other sectors of the economy not
evening out (Jara-Diaz 1986). Thus, market imperfections may lead to an
under estimation of the user benefit of the project (Venables and Gasiorek
1998; SACTRA 1999). The benefits not captured by the direct change in
consumer surplus are labeled wider economic benefits.
There are many reasons for market imperfections, the most common reasons
being taxes and subsidies and market power, where i.e. economies of scale
may lead to unregulated market power in product markets. The pervasive
departure from perfect competition in the NEG literature is the Dixit-Stiglitz
model of monopolistic competition (Dixit and Stiglitz 1977). Here, product
differentiation allows the producers to charge above marginal cost, but
competition drives the monopoly profits to zero and prices equal to average
cost. The Dixit-Stiglitz model has become the workhorse for most NEG
modeling. The NEG tradition acknowledges that in a perfectly competitive
environment price mechanisms alone are unable to endogenously generate
economic agglomeration. In order to build a model with the purpose of
analyzing the formation of economic agglomeration, one has to depart from
the notion of a perfect competitive economy (Fujita and Thisse 1996).
Lower transport costs due to new or improved transport infrastructure may
lead firms to exploit their economies of scale and hence influence the location
of economic activity. In the NEG literature there seems to be consensus on
the two main forces behind agglomeration and regional dispersion:
1. Increasing returns to scale in production, and
2. market power through product differentiation
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Following the core-periphery model of Krugman (1991), NEG demonstrates
that the interplay between these two main forces and factor mobility and
transport costs give rise to agglomeration in general equilibrium models
(Fujita, Krugman et al. 1999; Fujita and Thisse 2009). In the NEG literature
high transport costs yield equilibrium with even dispersion of economic
activity, while low transport costs give rise to agglomeration. In this setting
transport costs work as a centrifugal force while increasing returns to scale
work as a centripetal force. A synthesis of the pioneering works of NEG is
found in Fujita, Krugman et al.(1999).
In addition to the product market being imperfect, the labour market is
imperfect as well, both at the national and at the regional level. The gap
between the gross wage for the employer and the net wage for the employee,
as well as the regional immobility of workers and inflexibility of wages, indicate
a market with strong imperfection characteristics.
3. SCGE MODELING IN TRANSPORT ECONOMICS
SCGE models are spatial extensions of Computable General Equilibrium
(CGE) models. The MSG model of the Norwegian economy (Johansen 1960)
is widely credited for being the originator of the CGE modeling tradition. A
CGE model builds on a benchmark equilibrium dataset that accounts for all
the economic transactions in a base period. A Social Accounting Matrix (SAM)
is used to represent the equilibrium situation where all the economic agents
and goods are represented. In the SAM matrix the columns typically represent
the economic agents’ accounts while the rows represent markets for goods
and factors of production. A CGE model is then a solvable system of
equations that reproduce the equilibrium dataset. This reproduction is made
by making assumptions on the market structures and functional forms of the
preferences and technologies, and by setting parameter values on the
substitution elasticities. The system of equations describes the behavior of
economic agents (households, firms) and institutions, the structure of the
markets (goods, assets, production factors), and the interaction between
these. The CGE models are then used to estimate the reactions on the
economy of exogenous changes in policy, technology or other external
factors.
The spatial extension of CGE models is achieved by an explicit representation
of the transport of each commodity within each region and between all pairs of
regions in the model. Multiregional SCGE models typically aim at quantifying
regional effects of transport infrastructure investments or changes in transport
policy. An infrastructure investment or policy change lead to changes in the
trade costs which produce repercussions in the transport using sectors and
other related markets. SCGE models embrace the entire economy, making
these models specially suited for analyzing wider economic benefits of
transport investments through the link between the transport sector and the
transport using sectors, acknowledging that an exogenous change in one
sector may produce repercussions throughout the economy.
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The main advantages of SCGE modeling for transport appraisal lies in the
ability to compare outcomes of different equilibrium states (Tavasszy, Thissen
et al. 2002), such as (Oosterhaven and Knaap 2003):
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Benefits of generalized transport cost reductions due to changing
prices, production, consumption and trade, while holding the number of
firms and workers per region constant; showing what could be labeled
as short-run effects;
Benefits of transport cost reductions when the number of firms per
region is allowed to change; showing medium term effects;
Benefits when the number of workers is allowed to change too;
showing the long run effects of new transport infrastructure.
The model in Bröcker (1998) was the first European example of an SCGE
model and has been an inspiration for many following European SCGE
models. In the Bröcker model regional welfare effects of transport related
policies are quantified. An example of a sophisticated European SCGE model
is the Dutch RAEM model (Ivanova, Heyndrickx et al. 2007).
Bröcker and Mercenier (2009) provide a tutorial on the theory behind SCGE
modeling in transport economics.
4. THE PINGO MODEL AND ITS IDENTIFIED SHORTCOMINGS
PINGO is a SCGE model for prediction of regional and interregional freight
transport in Norway. The model has been developed in two stages at the
Institute of Transport Economics in Norway by Ivanova, Vold et al.(2002) and
Vold and Jean-Hansen (2007). PINGO is a static SCGE model where space is
explicitly represented in the form of freight transport costs between the
regions in the model. The original PINGO structure is part of a neo-classical
general equilibrium modeling tradition assuming constant return to scale and
perfect competition in all markets. This implies that all prices are equal to
marginal cost and that technical development or other changes that leads to
lower unit costs automatically leads to lower prices, hence, all cost reductions
are passed on to the consumers. In a perfect competitive economy with
constant returns to scale there are no wider economic benefits of investments
meaning that price mechanisms alone are unable to endogenously generate
economic agglomeration (Starrett 1978).
The initial development of PINGO was based on the models proposed by
Hussain (1996) and Bröcker. However, PINGO differs from these models in
the representation of the transport sector and transport costs. The transport
costs in Bröcker (1998) is based on Samuelson’s (1954) iceberg model where
transport costs take the form of shrinkage en route. In PINGO the goods
specific freight transport costs between pair of regions are calculated in the
Logistics model, based on (deJong, Grønland et al. 2005; deJong, Baak et al.
2007; deJong, Ben-Akiva et al. 2008). PINGO and the Logistics model together
yield a national freight model system for Norway with elastic demand, where
the Logistics model is used as a sub model to PINGO and vice-versa. The
Pingo model is predicting future demand both in the base and alternative
scenarios between different regions, given exogenous growth from a national
applied general equilibrium model, MSG6 (Heide, Holmøy et al. 2004). The
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supply part of the national freight modeling system consists of the Logistics
model.
In the version 2 of PINGO (Vold and Jean-Hansen 2007) the number of
regions are extended. In this second version of the model the Norwegian
economy is represented by 21 regions covering the 19 Norwegian counties,
one off-shore zone and one zone representing foreign trade. Each region
shelters nine different production sectors producing 32 commodity groups, as
well as six service groups and six investment types. In addition there are
sectors for private and public consumption, as well as a specific government
sector at national level for taxes/subsidies and sectors for export/import.
Interregional trade in PINGO is modeled via the so-called pooling concept.
The Chenery – Moses model, (Chenery 1953) and (Moses 1955), introduced
the pooling approach in interregional trade. This approach is often chosen in
general equilibrium modeling in order to keep the data requirements low.
According to the pooling concept no direct link exists between the producer
and the consumer since all commodities produced by a sector in a region is
pooled by a transport agent prior to their delivery for final or intermediate use.
Each region in the model harbors such transport agents who are responsible
for pooling commodities.
Figure 1 illustrates the structure of the PINGO model.
Figure 1: The structure of the PINGO model
PINGO is implemented in the GAMS/MPSGE programming package
(Rutherford 1999). The PATH solver (Ferris and Munson 2000) is employed to
provide solutions to the Mixed Complementary Problems (MCP).
Ivanova, Vold et al (2002) identify a range of shortcomings to the first version
of the PINGO model. The authors provide the following list of further possible
developments in order to improve the reliability of the model.
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Estimation of elasticities
Improve the description of import
Mobility of physical capital and labour
Segmentation of household groups
Include economies of scale effects
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Better forecasts.
The second version of PINGO (Vold and Jean-Hansen 2007) does little to
mend these shortcomings. Version 2 of PINGO mainly focuses on further
disaggregation of sectors and commodity groups, and updating the SAM
matrix to 2003 as the base year.
5. NEW DATA AND PROPOSED CHANGES TO THE MODEL
In this section of the article the proposed changes to the PINGO model are
presented. However, one has to bear in mind that an expansion of the model
increases the data requirements, the variables involved in the calibration
process, and the equations. There is a trade-off between wanting to expand
the model into yet another dimension and the possibility of not reaching
equilibrium at all.
New data: Commodity flow survey
The new commodity flow survey provided by Statistics Norway enables us to
update the benchmark dataset used in the PINGO model to 2008 as base
year. The new data will be used to establish new origin-destination matrices in
the Logistics model and hence provide equilibrium transportation costs
between pairs of regions for all the commodity groups in the model. In order to
maintain the consistency in the input data in the model all the data will be
updated to 2008 as base year.
In the commodity flow survey conducted by Statistics Norway each
responding company is asked to provide the following information for every
delivery point in 2008:
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The postal code
Aggregated weight or volume
Unit of measurement (tons or m3)
Number of deliveries
Turnover or value of goods
Who paid for the transport (buyer or seller)
This new data will provide new and valuable information on the geographical
origin of the intermediate products in the different sectors.
Market imperfections and increasing returns
The perfect competition structure of the PINGO model makes it unsuitable to
study wider economic benefits of transport infrastructure investments. In order
to build a model with the aim of analyzing such indirect economic effects one
has to depart from the assumptions of constant returns to scale and perfect
competition. The Dixit-Stiglitz model of monopolistic competition is the
pervasive departure from perfect competition in the NEG literature. This model
offers an alternative to the Arrow-Debreu framework by integrating imperfect
competition and increasing returns.
Monopolistic competition was first established by Chamberlin (1933) who
wanted to formalize a industry configuration where
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Each firm faces downward sloping demand
There is no profit
A price change made by one firm has negligible effect on the demand
faced by the other firms.
Monopolistic competition assumes that each sector is characterized by a
number of homogenous firms operating under the same technology producing
slightly differentiated products. Every firm produce a single variety under
increasing returns to scale. And there is free entry and exit to the markets,
hence, zero profits. One of the characteristics of the monopolistic competition
model that makes it especially appealing is the large group assumption that
abstracts the model from strategic interaction between firms.
Fujita (1988) was the first to apply Chamberlin’s monopolistic competition and
Dixit-Stiglitz’ product differentiation in a spatial setting
In the Dixit –Stiglitz version of monopolistic competition a representative
consumer who embodies the aggregated preferences of the population is
used as a simplifying approach. This representative consumer has symmetric
preferences, i.e. no product can be ranked over another product based on the
price. This form of product differentiation is in contrast to the Armington
approach since there is no preference of one product over another meaning
that no product can be preferred based on its origin.
The substitution elasticity of a sector measures the degree of monopolistic
competition in that sector. If the substitution elasticity of a sector moves
towards infinity the degree of competition tends towards being perfect. In
Sundberg (2009) the specification of the transport agents’ technology allows
parameterization between perfect and monopolistic competition. Hence, in the
market clearing, as the elasticity between varieties goes to infinity, the
demand system moves toward a standard Armington approach under perfect
competition.
The level of competition in each sector is an issue of discussion when
modeling the demand structure in the model. In the monopolistic competition
approach commodities produced in different regions, and abroad as well, are
perfect substitutes in the consumption bundle.
Most domestic SCGE models have a foreign region for imports and exports
and the substitution elasticities between domestic and foreign products are
treated differently in different models. The RAEM model assumes Armington
elasticities of substitution in international trade meaning that imported and
domestic products are imperfect substitutes. On one hand the Armington
assumption in international trade is a realistic assumption since there is a
tendency towards predominance of domestic products in a consumers
consumption bundle. On the other hand one can argue that the consumers
often are unaware of the origin of the products consumed and even if the
consumer has a preference for domestic products the domestically branded
product is often assembled from imported parts.
In this paper it is proposed to depart from the assumptions of constant returns
to scale and perfect competition found in the two first versions of PINGO. The
strength of the “ love of variety” approach is often overrated in the standard
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monopolistic competition approach (Ardelean 2006) in light of this result a
parameterization of the substitution elasticities in the model will be
investigated.
Integrating passenger transport in the model
In order to analyze the short run effects on the labour market of an
infrastructure investment it is necessary to include passenger transport and
commuting in particular in the model.
There are several possible ways to include passenger traffic in the model. The
Dutch RAEM model (Ivanova, Heyndrickx et al. 2007) separate between
business, commuting, shopping, education and travel purposes of the
passenger trips. The amounts of trips associated with each of these purposes
are generated according to a set of trip generating functions. These trip
generating functions take into account time and money costs of the travel as
well as a set of attraction factors.
Another option for passenger transport integration that could be investigated
is to include the national passenger modeling system with the national freight
modeling system. That is to expand the dimensions of PINGO by including the
generalized passenger transport costs calculated in the national passenger
transport model in PINGO in the same manner as the freight transport costs
from the Logistics model are included. The national passenger transport
system consists of two models, NTM5 for long distance passenger travel and
RTM for short distance passenger travel.
As noted earlier, changes in the transportation costs as a result of an
infrastructure investment may lead to changes in the location of economic
activity. These agglomeration effects are results of market power and
increasing returns to scale. Changes in the location of economic activity will
lead to changes in the commuting patterns, in addition to the direct changes in
economic benefits resulting from decreased generalized passenger
transportation costs. Integrating changes in the generalized passenger
transport costs in the SCGE model enables us to quantify the wider economic
benefits from an infrastructure investment found in changes in the commuting
patterns.
Segmentation of households
The development of the model will investigate segmentation of the
households according to their income deciles. The total income per household
is typically calculated as the sum of its labor income and capital income,
taking account for the regional labor supplied in other regions. The
households’ consumption budget consists of their net income plus social
transfers and unemployment benefits, minus savings and money spent on
transport.
The current version of PINGO follows the traditional assumption of one
representative consumer in each region with aggregated preferences.
Segmenting the households after income level will enable a better description
of the variance in consumption preferences and enable a better modeling of
the labour market effects of an improvement in infrastructure.
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Migration
Migration is a long run effect of changes in the localization patterns of
economic activity. Agglomeration or regional dispersion as a result of
infrastructure improvements may in the long run lead to migration. In addition
to this, lower generalized transportation costs may lead to an enlargement of
the catchment area by migration to regions with lower housing costs. The
trade off between commuting and migration is affected by the cost
characteristics in the different regions, i.e. the housing costs etc., as well as
the generalized transportation costs. Studies also show that people with
higher education are willing to commute longer distances than people with
lower education (Harsman and Quigley 1998; Trendle and Siu 2005).
The proposed modeling of migration will at large follow Ivanova, Heyndrickx et
al. (2007). A separate migration model that clears the labour market will be
investigated. Migration could be modeled as a nested logit model where the
upper level of the discrete choice model decides on migration or not, while the
destination for the migration is decided on level two. A similar proposition was
made in Ivanova and Eriksen (2004) in a one region model, where
households’ choices on resident-job pairs of location are represented using a
logit model. In their model, households’ location choices are influenced by
prices of goods, availability of housing, location specific wage levels as well as
commuting times between all pairs of locations.
6. CONCLUDING REMARKS
Market imperfections lead to economic benefits not adequately reflected in the
cost benefit analysis traditionally used in transport appraisal. The current
structure of the Norwegian SCGE model PINGO unable us to study the wider
economic benefits from transport infrastructure investments or transport policy
changes. This is due to the perfect competition and constant returns to scale
assumptions in the model. The current version of PINGO is also unable to
show the indirect repercussions of an infrastructure investment on the labour
market and the location of economic activity.
The following table summarizes the identified shortcomings in the PINGO
model and the proposed changes presented in this paper.
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Table 1: Identified shortcomings in PINGO and proposed changes to the
model.
Current PINGO model
Perfect competition and constant
returns to scale
One representative consumer with
aggregated preferences.
Immobility of labour, medium term
effects.
Immobilty of labour, long-run
effects.
Proposed changes
Monopolistic competition and
increasing returns to scale.
Parameterization between
monopolistic and perfect
competition.
Segmentation of households after
income deciles.
Integrating passenger transport
Migration sub model to PINGO
The use of SCGE models for transport appraisal has gained popularity as the
computational power provided to researchers has grown and with the
development of robust and efficient algorithms for solving the mathematical
problems associated with SCGE modeling. Plans for larger infrastructure
investments often result in considerable public debate on the methodology
used in the transport appraisal. Those in favor of the investment often refute
the traditional CBA used in investment evaluation. Development of a modeling
framework with the ability to capture possible wider economic benefits of
infrastructure investments may contribute to enlightening this debate.
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