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Measuring The Long-Term Regional Economic Impacts of High-Speed Rail in China Using a Dynami

2. Literature Review

The traditional approach to economic impact analysis of high-speed rail infrastructure is
benefit-cost analysis (BCA). The method has been widely adopted particularly for an ex ante
evaluation of HSR (Janic, 2003; De Rus and Nombela, 2007; Brand et al. 2014). The key process
of BCA was to justify the value of HSR investment through comparing all the benefits and costs
generated from the new developed infrastructure system. For instance, De Rus (2011) considers
HSR investment in Spain was a second-best alternative based on a BCA. This is because a
positive economic impact is expected given the considerations of levels of modal substitution,
traffic volumes and operating costs. However, using BCA to evaluate large-scale infrastructure
projects such as HSR, and particularly for a long-term assessment, can be problematic and
challenging, as pointed out by Vickerman (2007), due to the uncertainties of project financing
in a relative long-term period and the difficulties of selecting an appropriate discount rate to
convert future benefits and costs into present terms for a comparison. In addition, BCA also
has a limitation in incorporating the wider economic impacts such as agglomeration effects and
spatial spillover effects as a result of improved transportation accessibility (Venables 2016;
Button, 2017). As a result, the approach was more often applied for a project-level assessment
in a short-run rather than a true “social and economic” assessment with a focus on a long-term
period.
The second frequently adopted approach to evaluate economic impact of large-scale
transportation infrastructure system is econometric analysis, which often follows the tradition
of neoclassical growth theory. The key assumption is that transportation infrastructure can
be considered as a separate input in addition to capital and labor in a standard production
function Y = AF (K, L), where Y often denotes gross domestic product (GDP), A, K, and L
represents level of technology, the share of capital and the share of labor, respectively. The
output elasticity of transportation infrastructure is then estimated using regression models
based on either a time-series or panel dataset. The estimated output elasticities are often
found to vary substantially with a range between -0.15 and 0.56, due to the differences in
the data and specific modeling forms (Melo et al. 2013). In the case of China, the average
output elasticity of Chinese transportation infrastructure was found to be around 0.13 in a
meta-analysis by Chen and Haynes (2017). Despite econometric analysis is able to identify the
statistical association between infrastructure input and regional economic output from a long-
term perspective, the evaluation outcomes using such an approach can still be incomplete due
to the implicate assumption of a constant demand as a response to infrastructure change during
the investigation period. The indirect impacts on the economic system as a response to demand
change cannot be captured due to the lack of a feedback mechanism in regression analysis. In
order to fully capture the effects of infrastructure system improvement from both the demand
and the supply side, a general equilibrium assessment with a structure of simultaneous equation
systems is needed.

The state-of-the-art approach to regional economic impact assessment is computable general
equilibrium (CGE) analysis. The model, which is essentially a simultaneous equation system that
involves thousands of equations and variables, uses actual economic data in an input-output
format to simulate the interactions between the economy and changes in policy, technology or
other external factors, the latter of which is often considered as a “shock”. After all parameters
were calibrated in the initial simulation, the model then calculates an optimized solution (also
known as equilibrium solution) given the introduction of a shock to the economic system. With
the improvement of computer technology, CGE has been more frequently adopted for impact
assessment of large-scale infrastructure systems. Depending on the regional scale and the
consideration of temporal effect, CGE models can be classified into four types (shown in Table
2): a static single-region model, a dynamic single-regional model, a static multiregional model
and a dynamic multi-regional model. The first two types of models were generally applied for

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