Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
Dynamic spatial equilibrium models for social cost benefit analysis
of transport projects and policies: implementations for Japan, the
Netherlands and Hungary
Lóránt A.Tavasszy, TNO Delft / Radboud University, Nijmegen, The Netherlands
Lori.tavasszy@tno.nl or l.tavasszy@fm.ru.nl
Atsushi Koike, Tottori University, Japan
Attila Varga, University of Pécs, Hungary
Short abstract
During the late 80’s the so-called SCGE (Spatial Computable General Equilibrium) models were
introduced within the research community. These models were cross-sectional in nature and were most
interesting from the perspective of showing the consequences of changes in interregional interactions;
in other words, typically macro economic aspects were lacking. Since then, new models have been
developed since that include dynamic properties of spatial economic systems and have an improved
connection to macro-economic models. This paper reports on 2 new such models, one with applications
in Japan and the Netherlands and one with cases in Hungary.
Abstract
During the late 80’s the so-called SCGE (Spatial Computable General Equilibrium) models were
introduced within the research community. These models were cross-sectional in nature and were most
interesting from the perspective of showing the consequences of changes in interregional interactions;
in other words, typically macro economic aspects were lacking. Since then, new models have been
developed since that include dynamic properties of spatial economic systems and have an improved
connection to macro-economic models. This paper reports on 2 new such projects in Japan/the
Netherlands and Hungary.
The Japanese and Dutch model are both similar in structure and are presented as one: the RAEM-Light
model. They are dynamic, spatial computable general equilibrium models on a multiregional and
multisectoral basis. Typical cases for which this model was applied, where dynamic effects matter, are
the impacts of earthquakes in Japan, and the effect of gradual increase of congestion in the Netherlands.
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
The Hungarian model is a a macro-regional economic model built for the government for development
policy analysis. The model consists of a macroeconometric (the “macro block”), a spatial computable
general equilibrium (the “regional block”) and a Total Factor Productivity (the “technology block”)
sub-models. The regional block was derived from the Japanese/Dutch Spatial CGE model. The unique
feature of the integrated macro-regional model is thus that it endogenizes technological change in a
spatial context building on the findings of the sizeable literature of the geography of innovation and
regional innovation systems. As such this model can be used for policy analyses on the effectiveness of
various economic development tools targeting productivity increase such as infrastructure development
(e.g., highway building), human capital or R&D investments. Not only the macro and regional effects
of the actual amounts spent are investigated but also the effects of different geographical distributions
of the funds can be analyzed by the model.
We introduce the main features of the RAEM-Light model and the Hungarian and provide examples of
applications of both. The application for the Netherlands shows well how interregional dynamics work
in situations where resistances for transportation between regions change. The second application, for
Hungary, concerns the impacts of regionally differentiated scenarios for investments using European
structural funds. We conclude that the combination of dynamic, spatial and sectoral elements is a
critical feature for analysis of regional development policies.
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
1.
Introduction
The New Economic Geography theory offers an elegant framework for the mathematical description of
spatial economic systems in connection to transport systems. During the late 80’s the so-called SCGE
(Spatial Computable General Equilibrium) models have been introduced within the research
community. These models were cross-sectional in nature; new models have been developed since that
include some simple dynamic properties of spatial economic systems. In addition, sectoral economic
models were joined with macro-economic models. This paper reports on 2 new projects in Japan/the
Netherlands and Hungary, that were developed independently but proved to be complementary, to the
degree that they could be joined in one overarching model that combines dynamic, intersectoral,
interregional and macro-economic considerations in one empirical application.
The paper is built up as follows. In section 2 we explore the evolution of transport-economic modelling
tools along the spatial and sectorak dimension. We focus in on the latest development of a dynamic
SCGE model for the Netherlands. From another angle, important theoretical and empirical work was
done on macroeconomic/TFP models in Hungary. We describe how this model was integrated with the
dynamic SCGE model to form a completely new model (Section 3). We sketch applications of both
models in section 4 and conclude the paper in section 5.
2.
Sectoral and regional interactions and productivity
When analysing regional effects of transport policies, tools are required that explicitly describe the
economic functioning of regions. Several models are available for dealing with aspects of the broad
issue of economy-wide effects of transportation. Here we distinguish the following model types: LUTI
models, I/O models, non-spatial General Equilibrium (GE) models and Spatial Computable General
Equilibrium models.
The evolution of spatial models into the SCGE form has been a logical one from the perspective of
adding behavioural richness and regional detail. General equilibrium models can be considered as I/O
models with flexible (variable and endogenous) coefficients. A crucial feature of SCGE models is that
the spatial behaviour of producers and consumers is explicitly described, and endogenously determined
using econometric production and consumption functions. This implies that market related efficiency
measures – and changes therein - can be explicitly derived from these models. This is the case for the
intensity as well as the location of the activity. Knaap et al (1998) provide a comparison of the LUTI
and SCGE model types. Although the main focus of SCGE models has been on the economic effects of
3
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
changes in transport network performance, applications of these models can naturally go well beyond
the field of transport and infrastructure (van den Bergh et al. (1996) list other applications).
Table 1: evolution of spatial modelling
Location of
Interaction between activities
Intensity of economic
activities
Spatial interactions
Sectoral interactions
activities
Land Use models
Transportation
Input/Output models
Equilibrium models
models
LUTI models
Multi-regional I/O models
General Equilibrium models
Spatial Computable General Equilibrium models
In Europe, the earliest example of a full and empirically developed SCGE model was the CGEurope
model developed by Bröcker (see Bröcker, 1988). In the The Netherlands, the Dutch SCGE model
RAEM was constructed and applied (Knaap and Oosterhaven, 2000). Theoretically more simplified or
empirically less developed SCGE models were developed in Denmark (the BROBISSE model;
Caspersen et al., 2000), Sweden (Hussain and Westin, 1997; Nordman, 1998, Sundberg, 2002), the
PINGO model in Norway (Ivanova et al., 2002) and Italy (Roson, 1995). A recent overview of this
work can be found in Gunn, 2005 and Sundberg, 2005. Outside Europe, SCGE models have recently
been developed in the US (e.g. Löfgren and Robinson, 1999), where relevant research has also been
performed by Lakshmanan and Anderson (2002). In Japan SCGE models have been used (see Koike et
al., 2000 and Ueda and Koike, 2000) to analyse the potential impact on the Japanese economy of major
earthquakes. Miyagi (2001) has used an SCGE model to appraise the indirect economic impacts of a
large expressway project.
SCGE modelling has largely been restricted to comparative static modelling. New advances in dynamic
SCGE models (Koike et al, 2004; Sundberg, 2005; Ueda and Koike, 2006) have been made since. One
concrete model, the RAEM Light model was developed in collaboration between Japanese and Dutch
researchers and applied to study dynamic effects of increasing congestion levels in the Netherlands
(Koike et al, 2006, Koike et al, 2007). In this model, the propagation of growth through the time and
space dimension has 3 main drivers:

The agglomeration effect: the efficiency of each firm depends on the population distribution. In this
way, high population density region gathers high productivity firms.

The cost of living effect: People have the tendency to migrate to areas with little competition for
land and housing.
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies

The capital accumulation effect: The economic growth is explained both of exogenous population
growth and endogenous capital accumulation.
The method chosen to describe time related behaviour in this model was the naïve approach, assuming
that yearly adjustments of choices are made by all agents, using information from the past year. More
advanced model forms (including forward looking expectations, habit formation or lagging responses)
require substantially more detail in real word observations than is available at present. The resulting
implementation is a recursive model. Other simplifications and compromises to the original SCGE
model (the RAEM model) included replacement of CES functions for trade by Logit type functions, a
perfectly competitive labour market and iceberg type transport costs.
The estimation and validation of the dynamics of the model is a complex task. An estimation of the
model was done on time-series of various regional economic aggregates for the Netherlands. The results
of these estimations and the validation of the model will appear in a future publication.
3.
Joining with macro type models of productivity and economic growth
In relation to economic growth the geographical dimension of innovation systems became crucial for a
number of reasons. First, because the role of space was assumed essential in accessing knowledge
during innovation (Jaffe, Trajtenberg and Henderson 1993, Audretsch and Feldman 1996, Anselin,
Varga and Acs 1997, Varga 2000, Koschatzky 2000, Keller 2002). Second, since agglomeration was
seen as a determinant in the accumulation of technological knowledge and, eventually, growth
(Feldman 1994, Fujita and Thisse 2002, Varga 1999 and 2000, Caniels 2000). Essential elements of this
“geographical growth explanation” are rooted in three separately developed recent literatures (Acs and
Varga 2002): the endogenous growth theory (Romer 1990, Aghion and Howitt 1998), the systems of
innovation school (Lundvall 1992, Nelson 1993), and the new economic geography literature (Krugman
1991, Fujita, Krugman and Venables 1999, Fujita and Thisse 2002). In spite of the lack of a full
theoretical integration a modeling framework reflecting the above three literatures can already be
outlined (Varga 2006a). Within such a framework the following sub-modeling demands needed be
fulfilled:
1.
Explicit modeling of the geographical aspect of technological change (following the
framework of the innovation systems literature);
2.
Modeling of agglomeration economies and the resulting cumulative spatial processes (and
as such empirically implementing insights of the new economic geography);
3.
Modeling the resulting macroeconomic effects of geographically explained technological
change.
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
The resulting model needed to explicitly treat the geography of technological change in a dynamic
manner to account for various cumulative processes inherent in macroeconomic growth explanation.
Current econometric models widely used in development policy analysis such as the HERMIN model in
Europe (Bradley, Whelan and Wright 1995, ESRI 2002) or the REMI model in the United States (Treyz
1993, Fan, Treyz and Treyz 2000) have moved into the direction of incorporating geography and
technological change into their basically demand-driven systems, however, they are not yet fully
developed according to the criteria listed under points 1 to 3 above. The EcoRET model (Schalk and
Varga 2004, Varga and Schalk 2005) directly integrates the geographic dimension, but the dynamic
manner in which space contributes to macroeconomic performance is not modeled there.
A new development concerns the GMR-Hungary model (a Geographic Macro and Regional model for
Hungary) a complex macro and regional model developed for the economic policy analysis of EU
Community Support Framework (CSF) interventions on the Hungarian economy for the planning
period of 2007-13. The complex model is the extension of EcoRET (Schalk and Varga 2004, Varga and
Schalk 2005) – a macroeconomic model used for ex-ante impact analyses during the design of the 1st
National Development Plan for Hungary – into the regional and the sectoral directions. For the regional
extension EcoRET is integrated with RAEM-Light model (Koike and Thissen 2005).
The unique feature of the integrated macro-regional model is that it endogenizes technological change
in a spatial context drawing from the findings of the sizeable literature of the geography of innovation,
regional innovation systems, new economic geography and the endogenous growth theory. As such this
model can be used for policy analyses on the effectiveness of various economic development tools
targeting productivity increase such as infrastructure development (e.g., highway building), human
capital or R&D investments. Not only the macro and regional effects of the actual amounts spent are
investigated but also the effects of different geographical distributions of the funds can be analyzed by
the model.
In this complex model all the above three demands are accounted for in three interconnected submodels. In policy simulation the TFP equation plays a crucial role in the complex model system.
Regional values of policy variables (like R&D and infrastructure investments) are plugged into the
equation to calculate the likely change in the TFP growth rate. This estimated change in the TFP growth
rate enters the SCGE sub-model to generate regional values of TFP as a result of agglomeration effects
as well as employment, wages, investment and output. TFP values generated by the SCGE sub-model
will enter the macroeconomic model to account for the macro level outcomes of CSF interventions.
Figure 1 outlines how TFP-related development policy interventions are simulated in the complex
model.
6
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
Effects on spatial
structure
SCGE
sub-model
(regional model)
Macroeconomic
effects
Macroeconomic
sub-model (demand,
supply, income
distribution)
TFP sub-model
(Regional model)
Dynamic TFP changes
Static TFP changes
Economic policy instruments:
infrastructure, R&D and
education
Figure 1: Regional and national level short run and long run effects of TFP changes induced by TFPrelated CSF interventions (Varga et al, 2006)
Resulting from development policy interventions, changes in regional TFP affect regional level
equilibrium values (employment, demand, production, utilities, investment etc.) both in the short run (in
the same year) as well as in the long run (during the coming years). As such one-time changes could
generate a cumulative long run process. This process is detailed more concretely in the following steps:
1. Assuming that the intervention occurs in a specific region, the change in regional TFP level in
the relevant sectors) generates the following effects in the short run: f.o.b price of the good
decreases that induces a decrease in the demand. At the same time the effect on interregional
trade is positive as well as the impact on output resulting in an increase in the demand.
Additionally, the decline in prices induces an increase in regional demand resulting in higher
utility levels.
2. Interregional restructuring in utility levels is followed by labor migration in the next period
(next year). There is also an effect on the interregional re-allocation of capital. Labor movement
results in changes in regional technology both in the short run as well as in the long run.
3. Changes of TFP values induce a long run cumulative causation process invoking changes in the
geographical structure of the whole economy.
7
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
Note that the mechanism of the impact of a change in TFP on output follows the same logic both at the
macro level and at the regional level. In both models the impact depends on the relative strengths of the
output and substitution effects.
In the short run (practically within a year) the SCGE sub-model simulates the regional effects of policy
instruments on the equilibrium values of production, employment, investment and wages as well as
population. In the long run (gradually over the period of years), differences across regions with respect
to utility levels induce inter-regional migration (followed by the spatial re-shuffling of investment and
production). As such this model is designed to endogenize the spatial structure of an economy. The
theoretical underpinnings are developed in new economic geography models. According to these
models geographical structure results in the interplay of two types of forces: centrifugal (transportation
costs and congestion in the SCGE model) and centripetal forces (agglomeration effects in localized
technological change).
By integrating RAEM-Light into the framework of EcoRET the dynamic effects of regional level
interventions (infrastructure, R&D and education support) are simulated both at the regional as well as
the national levels. While the TFP sub-model incorporates agglomeration effects in a static manner the
dynamism of the process is added by the integrated regional SCGE model. Regional level interventions
affect inter-regional differences in utilities that result in a changing geographical structure of the
economy. Consequently, changes in agglomeration influences both regional and national TFP. This
certainly has an impact on macro and regional variables such as employment, investment or production.
Equilibrium regional TFP values that constitute positive agglomeration effects induced by CSF policy
shocks and being shaped by centripetal and centrifugal forces inherent in the system are aggregated to
the national level in each year. Aggregation is carried out by weighted averaging regional TFP values
with weights of regional employment to account for agglomeration effects.
Further details about the model, its estimation and its performance can be found in Varga (2006b).
4.
Applications
4.1
Impacts of congestion on growth (RAEM Light)
We applied RAEM Light to study the impacts of traffic congestion on the economies of regions in the
Netherlands. This was a study using RAEM Light exclusively, which implies that the primary drivers
for growth were those of interregional interactions, i.e. results are based on responses of the sectoral
production and consumption functions to changes in interregional trade, investment, labour access and
migration.
8
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
The area of the case study is as shown in Figure 1. The Netherlands was divided into 12 provinces
Industry was aggregated in eight sectors, where 2 of which concerned mostly physical interactions i.e.
goods transport and 6 concerned mainly services. The traffic congestion scenario for the simulation was
as follows. (1) Intra and inter regional travel time within and in connection to Zuid-Holland and NoordBrabant (the most congested regions) was assumed to increase by 3% per year. (2) Intra and inter
regional travel time in the rest of the Netherlands increased by 1% every year. In empirical study we
analyzed what economical damage from traffic congestion would result in these regions.
Friesland
Groningen
Frevoland
Utrecht
Drenth
Noord-Holland
Overijssel
Zuid-Holland
Gelderland
Zeeland
Noord-Brabant
Limburg
Figure 3 Study Area
The outcomes showed expecting signs and magnitudes for all economic aggregates. After 10 years the
impact on GDP was in the order of -0.5% per year. The most interesting result of this exercise was the
dynamic impact on regional development by sector. The graph below indicates that one of the 2 heavily
congested regions, which neighbours the other heavily congested region, has temporary positive effects,
due to disagglomeration of its competitive neighbour. Although the net impact is negative, this only
shows after 7 years. Looking at the sectorally disaggregated picture in Figure 5, we can see that this is
mainly due to the quick rise of output of the services sector. Still the impact is negative, as the decline
of the manufacturing and agricultural sectors is stronger, which pushes growth downward.
9
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
1
2
3
-250
4
5
6
-300
7
8
9
-350
10
N oord-B rabant
F levoland
O verijssel
-200
N ood-H olland
-150
D renthe
-100
Friesland
-50
G roningen
0
G elderland
50
Lim burg
U trecht
100
Z eeland
（1,000 E uro）
Z uid-H olland
150
-400
Figure 4 Gross Regional Product Changes (million Euros, 2000)
0.06
(%)
0.04
A griculture
T ransport
G overnm ent
M anufacturing
S ervices
O thers
C om m erce
Insurance
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.1
-0.12
1
2
3
4
5
6
7
8
9
10
Figure 5 Percentage changes in output from various sectors in the Noord Brabant region
4.2
Application of the GMR model for Hungary
It is a particular feature of the GMR model that it simulates the macro and regional effects of TFPrelated development policy instruments in a spatial setting. As such this model is capable of estimating
the likely effects of not only the different mixtures of policy instruments but also their various possible
geographic allocations. In the remaining of this section we provide a demonstration of how the
geography of development policy interactions influences their effectiveness.
Three scenarios were studied for the planning period of 2007-2013. For all the scenarios the total
amount spent as well as their internal structure (i.e., the shares of infrastructure investment, education
10
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
and R&D promotion) remains the same. Effectiveness is measured by policy impacts on the GDP
growth rate. The following possible allocations of CSF1-funds are considered:
1. gradually increasing the share of Budapest, Hungary’s capital as the most advanced regional
economy of Hungary;
2. raising the portion of expenditures spent on the five most industrialized counties in Northern
Transdanubia; and
3. increasing support to lagging counties East to the river Danube.
Figure 6 (See ANNEX 1) provides a comparative picture on how the change in the national TFP
growth rate relative to the baseline (i.e., no CSF intervention) is affected by the different scenarios.
Three observations are apparent from the figure:
-
the effects increase over time resulting from continuous interventions
-
the speed of increase is the highest in Budapest, which suggests the presence of positive
agglomeration effects
-
with 50 percent of the support concentrated in Budapest the resulting effect is 7 percent higher at
the end of the planning period than without interventions whereas 70 percent of resources allocated
to Northern Transdanubia has an effect of 2.5 percent and a 90 percent concentration in the East of
Danube region also results in a 2.5 percent change in the macro level economic growth rate.
Elasticities in Figure 7 (See ANNEX 1)
describe the effects of increased CSF spending on induced
change in macroeconomic growth rate relative to different spatial distribution of the funds. It is clear
from the figure that the effects increase both parallel to increasing concentration and with time.
However, these effects are much more pronounced in Budapest than in the rest of the regions.
Figure 8 (See ANNEX 1)
depicts growth rate change elasticities with respect to the spatial
concentration of CSF support by regions and for the time period considered. The “persistency effect”
(i.e., increasing impact in case interventions are persistent) is clear again. The observation that policy
effectiveness (measured by macroeconomic growth effects) increases with the concentration of the
system of innovation is again clear from the figure. It is also interesting that as time goes on the curve
describing the effects in Budapest becomes quasi-concave by the end of the planning period suggesting
that negative externalities (congestion) increase in strength with growing spatial concentration.
1
EU Community Structural Funds
11
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
5.

Concluding remarks
This paper reports on developments that add to the uptake of SCGE modelling in the last
decade: improved representation of dynamics and improved connection to macro economic
modelling.

We have introduced the main features of the RAEM-Light model and the Hungarian and
provide examples of applications of both.

The application for the Netherlands shows well how interregional dynamics work in situations
where resistances for transportation between regions change.

The second application, for Hungary, concerns the impacts of regionally differentiated
scenarios for investments using European structural funds.

We conclude that the combination of dynamic, spatial and sectoral elements is an interesting
and relevant addition for the analysis of regional development policies.
6.
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
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Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
ANNEX 1
8,00
7,00
5%
10%
6,00
15%
5,00
20%
4,00
25%
30%
3,00
35%
2,00
40%
45%
50%
1,00
0,00
2007
2008
2009
2010
2011
2012
2013
3,00
2,50
25,0%
30,0%
2,00
35,0%
40,0%
45,0%
50,0%
1,50
55,0%
1,00
60,0%
65,0%
0,50
70,0%
0,00
2007
2008
2009
2010
2011
2012
2013
3,00
2,50
45%
50%
2,00
55%
60%
65%
70%
1,50
75%
1,00
80%
85%
0,50
90%
0,00
2007
2008
2009
2010
2011
2012
2013
Figure 6: CSF induced GDP growth rate changes (relative to baseline in percentages)
by geographic concentration of funds. From top to the bottom: Budapest, North Transdanubia, East of
Danube.
16
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
12.00
5%
10.00
10%
15%
8.00
20%
25%
6.00
30%
35%
4.00
40%
45%
2.00
50%
0.00
2009
2010
2011
2012
2013
7.00
6.00
25.0%
30.0%
5.00
35.0%
40.0%
4.00
45.0%
3.00
50.0%
55.0%
2.00
60.0%
65.0%
1.00
70.0%
0.00
2009
2010
2011
2012
2013
6.00
45%
5.00
50%
55%
4.00
60%
65%
3.00
70%
75%
2.00
80%
85%
1.00
90%
0.00
2009
2010
2011
2012
2013
Figure 7: Elasticity of GDP growth rate changes with respect to CSF spending by geographic
concentration of funds. From top to the bottom: Budapest, North Transdanubia, East of Danube.
17
Dynamic spatial equilibrium models for social cost benefit analysis of transport projects and policies
1.80
1.60
1.40
2008
1.20
2009
1.00
2010
0.80
2011
0.60
2012
0.40
2013
0.20
0.00
10%
15%
20%
25%
30%
35%
40%
45%
50%
0.50
0.40
2008
0.30
2009
0.20
2010
0.10
2011
2012
0.00
2013
30.0%
35.0%
40.0%
45.0%
50.0%
55.0%
60.0%
65.0%
75.0%
-0.10
-0.20
0.50
0.40
0.30
2008
2009
0.20
2010
2011
0.10
2012
2013
0.00
50%
55%
60%
65%
70%
75%
80%
85%
90%
-0.10
-0.20
Figure 8: Elasticity of GDP growth rate changes with respect to spatial concentration of CSF spending
by geographic concentration of funds. From top to the bottom: Budapest, North Transdanubia, East of
Danube.
18