Abstract
A congestion charging scheme may be a good alternative for infrastructure investment to improve the allocation of road usage over different user groups, thereby enhancing economic welfare. However, a congestion charge also increases the costs of traveling, thereby possibly inducing agglomeration effects (Arnott in Congestion tolling with agglomeration externalities, paper presented at Conference in Honor of Kenneth A. Small, University of California, Irvine, 2007). Agglomeration effects in relation to infrastructure investment can be substantial (Venables in J Transp Econ policy, 2007) and they can be estimated using a spatial computable general equilibrium model (Bröcker in Ann Reg Sci 32, 367–387, 1998). Agglomeration effects of congestion charging are, however, difficult to assess using this approach because the government revenue from the congestion charge should be redistributed to the population. This redistribution of government revenue may be either agglomeration- or dispersion-augmenting and thereby change the agglomeration effects significantly. In this paper, we propose a methodology based on a spatial computable general equilibrium model to estimate the agglomeration effects of congestion charging with an agglomeration-neutral redistribution effect. In this manner, we do take the redistribution effect into account without letting it interfere with the effect of the congestion charge. This is important because we only want to evaluate the congestion charge, independent of the chosen redistribution scheme. In our application of the proposed methodology for the Netherlands, agglomeration benefits are found to be substantial and have, in general, the opposite sign as the relative cost change.
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Abbreviations
- A i,s , α i,s and β i,r,s :
-
Cobb–Douglas scale and share in region i and sector s
- \({\phi _{j,r}}\) :
-
Cobb–Douglas budget share in consumption in region i and sector r
- \({\varphi_{ii}}\) and η :
-
Constant and sensitivity parameter for commuting
- Lm i :
-
Housing stock indicator in region i
- κ i :
-
Regional amenities factor
- ζ i :
-
Spatial distribution of transport production
- σ s :
-
Substitution elasticity in sector s
- tr i, j, s :
-
Transport cost mark-up of a sector s good produced in i and consumed in j
- tw i, j, s :
-
Shopping cost mark-up of a sector s good produced in i and consumed in j
- tp i, j :
-
Commuting costs from region i to region j
- y i, s :
-
Production in region i and sector s
- l i, s :
-
Labor demand in region i and sector s
- q i, s, r :
-
Intermediate demand for sector s goods in demanding region i and demanding sector r
- w i, s :
-
Wage rate in region i and sector s
- p i, r :
-
Producer price of sector r goods in region i
- \({p_{j,r}^d }\) :
-
Dixit–Stiglitz sector r price index in region r
- \({p_{i,j,r}^t }\) :
-
Price at destination j of a sector good r from region i
- c j,r :
-
Consumption of sector goods r in region s
- T I j :
-
Total income in region j
- \({d_{i,j,r}^t }\) :
-
Trade in sector r goods from region i to region j
- f i, j :
-
Commuting matrix from region i to region j
- un i :
-
Unemployed in region i
- v j :
-
Vacancies in region j
- o i :
-
Labor supply in living region i
- \({\bar{o}}\) :
-
Exogenous total labor supply
- u :
-
Utility
- \({u_j^c }\) :
-
Utility derived from consumption in region j
- \({u_i^l }\) :
-
Utility derived from amenities (including housing price) in region i
- T :
-
Total transport production and nominal demand
- ben :
-
National unemployment benefit
- t :
-
Income tax rate
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Thissen, M., Limtanakool, N. & Hilbers, H. Road pricing and agglomeration economies: a new methodology to estimate indirect effects applied to the Netherlands. Ann Reg Sci 47, 543–567 (2011). https://doi.org/10.1007/s00168-010-0394-4
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DOI: https://doi.org/10.1007/s00168-010-0394-4