ABSTRACT
Computable general equilibrium (CGE) models have been widely used to assess the economic impact of natural disasters, but the models have not been fully validated by applying them to real disasters. This study focuses on validating a model for use in a short-run case in which the functional recovery of infrastructure and businesses occurred on a time scale of a few months. A special attempt is made to determine the parameter values of elasticity of substitutions, which play an important role in the effect on supply chains. In this study, a spatial CGE model, in which Japan is divided into nine regions, is constructed and applied to the case of the 2011 Great East Japan Earthquake and Tsunami. Through this application, the best estimates of the elasticity parameters generated relatively consistent estimates of production change compared with the observed change, both in severely affected regions and in other regions.
Acknowledgements
Eelier versions of this paper are presented at the 23rd and 24th International Input–Output Conferences. We thank participants as well as the editors and anonymous referees for their comments that improved our research.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 A ‘long-run’ study, such as the analysis of the negative regional impact of debts incurred by local governments and households, is also critical in estimating longer term impacts. However, this type of study is beyond the scope of this paper.
2 According to Romanoff and Levine (Citation1986), a series of developments (publications) on SIM began since 1977. In their study, capacity limitations and inventory are incorporated in a time-phased production system.
3 Putty-clay models assume different substitutability of production factors before and after the capital is installed. According to Atkeson and Kehoe (Citation1999), the model was initially set up by Johansen (Citation1959).
4 The downward rigidity of the price of labor is employed by Rose and Guha (Citation2004) to model the decrease in labor inputs.
5 In fact, Taylor and Lysy (Citation1979) investigated the effects on income redistribution via a one-sector model that is characterized by fixed capital, exogenous investments, and nominal changes in prime cost (Keynesian); they demonstrated that the model produces relatively insensitive functional income distribution.
6 This type of external setting of final demand is viewed as a type of Keynesian closure by Robinson (Citation1989).
7 Because the use of imports is not separated into intermediate and final demand in the original I–O table, one Armington composite for imported and domestic goods is used for both intermediate and final demand, as shown in the bottom terms. In total, the technology tree is consistent with the Japanese interregional I–O table that is used in this research.
8 The redistribution of income among regions can occur through policies, such as tax and social security spending.
9 The alternative approach could be that PCLR is reflected in only the hypothetical capital and labor losses. However, we reflect the impacts of infrastructure disruptions on the efficiency parameter because the interpretation is easier on the capital and labor losses.
10 For a comparison, automobile parts, passenger cars, and other finished transportation machinery products are aggregated by the weights of value added.
11 In fact, the large supply-chain impacts for Transportation machinery resulted from damage to a semiconductor company, which is classified as Electronics. This type of supply-chain impact must be analyzed using a different framework, such as using a micro business transaction dataset.
12 All effects are negative because of the assumption that factor endowments are fully utilized (maximum production is achieved) and unchangeable among regions and sectors. However, in actuality, some of the sectors in non-damaged regions benefit from a disaster. This phenomenon can be understood by considering an idle capacity before a disaster, but the degree of idle capacity must be explored in further research.
13 For example, Saito (Citation2015) analyzed interfirm transaction data from 800,000 firms in Japan and investigated the key firms that can affect widespread disaster impacts, such as the Great East Japan Earthquake.