wider economic benefits; definition - himolde · wider economic benefits : all economic benefits...
TRANSCRIPT
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Assessing Wider Economic Benefitsby using Spatial Computable General Equilibrium (SCGE) models
Wiljar Hansen
Institute of Transport Economics
Oslo
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Wider Economic Benefits; Definition
�Wider economic benefits: 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 (Vickerman 2007)
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So just to be clear;
� In CBA, as long as the markets are perfectly competitive the user benefit will equal the total benefit of the investment (Kanemoto and Mera, 1985) (Jara-Diaz, 1986), and adding spillover effects will only result in double counting (Mohring, 1993)
�However, market imperfections in the transport using sectors will, independent of the market structure in the transport sector (SACTRA 1999), produce utility effects not cancelling out
� When the price of important market goods in secondary markets exceeds marginal cost, the traffic improvement will produce repercussions in other sectors of the economy not evening out (Jara-Diaz, 1986)
�Market imperfections may lead to an under estimation of the user benefit of a project (Venables and Gasiorek, 1998) (SACTRA, 1999)
� Taxes and duties, market power, information asymmetry, etc.
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� Assuming that the benefits are correctly measured and that all external effects are incorporated, than WEB are defined as Total economic benefits / total user benefits > 1 (B** > 1 in the table)
� In the table we see that B**>1 occurs when P>MC for the transport using sectors, regardless of the sign on the LRMSC in the transport sector
� Meaning that wider economic benefits may occur when there are market imperfections in the secondary markets
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Transport and the economy
� A large literature on the economic and social effects 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 (Tavasszy, Thissen et al. 2002):
� 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 (SCGE)
� It is argued that the focus should be on the last two of these approaches since they are considered to be the approaches best suited to analyze the rippling effects in the economy of large infrastructure investments. Spatially detailed models are the only adequately way to model the economic impacts of new transport infrastructure that are likely to have impacts on the economic structures in a given area (Oosterhaven, Knaap et al. 2001; Oosterhaven and Knaap 2003; Tavasszy, Thissen et al. 2002; Simmonds and Feldman 2011)
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SCGE-modelling� Spatial Computable General Equilibrium models = general equilibrium
models, with a spatial dimension, that are numerically solvable
� In contrast to partial equilibrium models that only consider a single market, take all other prices in the economy for given and disregard the effects in the rest of the economy, general equilibrium models capture the interplay between all sectors in the economy.
� Builds on a reference data set containing all transactions in the economy in the base year
� The model is a solvable system of equations that reproduce the equilibrium dataset through assumptions on market structures, functional forms for the product- and utility functions, and parameter values for the elasticities in the model
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SCGE-modelling cont.
�The system of equations describes the behaviour of economic agents (households, firms) and institutions, the structure of the markets (goods, assets, production factors), and the interaction between these.
�The interplay between the actors in the economy are captured through the prices.
� The vector of prices that clears all markets simultaneously
defines the equilibrium in the model
�The spatial dimension is obtained by an explicit representation of the commodity flows between and within all zones in the model, and the transport costs for all trade relations
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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 through out the economy
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)
Long run indirect economic effects requires a dynamic model with factor mobility
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Building a new Norwegian SCGE-model that is able to capture the WEB of infrastructureinvestments
�Ongoing work financed by The Norwegian Public Roads Administration through the Ferry-free E39 project
�Collaboration between TØI and TNO in Holland
�Builds upon my phd work financed by the SMARTRANS Programme of the Research Council of Norway and co-funded by the Norwegian Public Roads Administration through the project “Logistics in Norway”
�Replacing the PINGO-model in the national freight modellingsystem for Norway
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New model cont.
� The model under development is part of the RAEM-family of models inspired by RAEM 3.0 (Ivanova, et al, 2007), RHOMOLO (Ferrara, Ivanova et al. 2010; Brandsma, Ivanova et al. 2011), ISEEM (Heyndrickx, Ivanova et al. 2009) and TIGER (Heyndrickx, Koops et al. 2011
� Main features in the new SCGE-model
� Regional and inter-regional freight flows
� Recursive dynamics
� Monopolistic competition and increasing returns to scale
� Inter-regional migration
� Inter-regional passenger transport (commuting in particular)
� Implemented in GAMS
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New model – new data� 2008 as base year for the reference data set
� New growth rates from the MSG model
� Able a flexible base year for introduction of new infrastructure
� The Norwegian commodity flow survey (CFS)
� Performed by Statistics Norway
� Produce new information on the geographical origin of the intermediate products in the different sectors of the economy
� The data from the CFS are used as
input matrices in the Logistics model,
producing trade flows and transport costs
� Enables modelling with trade data
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SCGE-model
Logistics model:•Commodity flows •Transport costs for each trade relation
Reference data set•SAM•Regional production data
RTM-5•Passenger transport flows after travel purpose•Transport costs
MSG growth rates• gross production• Exports • Imports• consumption • (employment)• (productivity)
Migration matrices•Domestic migration between pairs of regions
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Stepwise model development
Step 4: + inter-regional
migration
Step 3: + commuting
Step 2: Monopolisticcompetition in
”modern” sectors
Step 1: Perfect competitionconstant returns to scale
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�Model structure
� Regions: 19 Norwegian counties
� 1 foreign region for imports and exports
� 2 production factors, labor and capital
� Intermediate inputs
� Economic agents: regional households, regional sectors, federal
government, virtual investment bank, virtual agent for transport
and trade margins.
� 30 sectors
� 47 commodities
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MODEL
DESCRIPTION
(basic structure)
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� Capital and labor composite combined with intermediate inputs to produce the gross domestic output XD, with Leontief technology.
� Domestic production by commodity (XDD) is transformed to exports (E) and domestic production delivered to domestic markets (XXD)
� The sectoral split of the regional demand for commodity i produced in region o and sold in region d is given by XDDED
� The domestic composite (XX) is combined with imports (M) to produce the composite good for domestic consumption (X)
� The composite good (X) is distributed among household consumption (Qh), government consumption (QG), Investments (I), changes in inventory (SV), transport and trade margins (TMX) and intermediate use (II).
