Abstract
Computable general equilibrium (CGE) models are promising for estimating the economic losses of natural disasters. This type of model has a sound theoretical foundation and can explain both forward and backward linkages in an economy; hence, it is suitable for predicting the economic impact of supply and demand shocks during a disaster. Spatial and sector classifications for the CGE model are key elements that affect the performance of the model. Although physical damage to an area by a hazard is local, the damage induces higher-order effects on flows that can spread to other areas, and constructing the CGE model on a fine spatial scale is necessary for describing these effects. Sectoral disaggregation would also improve the quality of the model if key industries that have low substitutability and cause supply chain impacts are separated from other sectors with higher substitutability. This study validates the spatial and sectoral disaggregation effects of the CGE model through a case study of the Great East Japan Earthquake and Tsunami in 2011. In addition, this study examines whether two patterns of the elasticity of substitution parameters for interregional trade contribute to improving the forecasting capability of the CGE model.
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- 1.
Input elasticities concern the substitutions among factor inputs or intermediate inputs, whereas import elasticities involve the substitutions among imports from different regions.
- 2.
Putty-clay models assume different substitutability of production factors before and after the capital is installed, with substitutability usually being zero for clay. On the other hand, putty-putty models assume substitutability both before and after and often the same level of substitutability.
- 3.
To be more rigorous, this assumption and the degree of substitutability have to be verified by observations, but we assume that the substitutability between capital and labor becomes very low under difficult conditions.
- 4.
Taylor and Lysy (1979) investigated the effects on income redistribution with a one-sector model characterized by fixed capital, exogenous investments, and nominal changes in the prime cost (Keynesian), and they demonstrated that the model produces a relatively insensitive functional income distribution.
- 5.
In our model, personal income directly affects household consumption levels. Fixing the income in a model works to slow the change in consumption levels after the disaster. This assumption may also need to be modified based on further empirical study.
- 6.
The downward rigidity of labor cost is also used by Rose and Guha (2004) to model the decrease in labor input.
- 7.
Because the use of imports is not separated into intermediate and final demand in the original input–output table, one type of Armington composite for imported and domestic goods is used for both intermediate and final demand, as shown in the bottom layers. In total, the technology tree is consistent with the Japanese interregional input–output table used in this research.
- 8.
Income may be redistributed among regions through policies such as tax and social security spending.
- 9.
In reality, consumption patterns are likely to change during disaster and recovery periods (e.g. spending money on necessities rather than other commodities, such as avoiding entertainment). The effects of these shocks on the demand side have to be explored in a future study.
- 10.
Here, we assume that different types of labor exist in different sectors and are immobile between sectors.
- 11.
“Factor damage” is the case where damages to production facilities and labors are considered, and “lifeline impact” is the case where the impacts of lifeline (electricity, water, and gas) disruption duration are considered. Different types of engineering-based models, such as a fragility curve, are employed to calculate the impacts of each source on production capacities.
- 12.
One of the alternative approaches could be that PCLR is reflected in only the hypothetical capital losses. However, we reflect the impacts of infrastructure disruptions on the efficiency parameter because the interpretation is easier if the capital losses are induced by facility damage and recovery and the total productivity factor is reduced due to lifeline damage.
- 13.
The substitution parameter is set to 0 only for the automobile parts sector for the nine-region/30-sector model and in the combined automobile parts and passenger cars sector for 9-region/29-sector and 47-region/29-sector models.
- 14.
For example, grid search (setting several parameter values that are allocated by the same small interval in the possible range of each parameter and trying all combinations) would help to find better parameter values, but the computation time is likely to be very high.
- 15.
For comparison, automobile parts, passenger cars, and other finished transportation machinery products are aggregated by the production quantity weights.
- 16.
Set T includes the following elements: March, April, and May of 2011.
- 17.
For the nine-region model, Kajitani and Tatano (2018) found a better estimate for the case of even smaller elasticities of substitution in interregional trade (0 for automobile parts and 1/3 as the normal case for other sectors). Nonetheless, the RMSE in the best 47-region model in this study (RMSE = 0.1118) is slightly smaller than that in the best 9-region model (RMSE = 0.1126).
- 18.
We omitted the case of the 9-region/29-sector model because it does not exhibit a change from the 9-region/30 sector model.
- 19.
More investigations are needed, but models at a finer spatial scale may require higher substitution parameter values depending on distances from the disaster hit area if production outsourcing occurs at a closer distance.
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Acknowledgements
We would like to thank the editors and two anonymous reviewers for their constructive comments. We also acknowledge Taylor and Francis and the International Input-Output Association because this chapter is derived in part from an article published in Economic Systems Research (28 Sep 2017, copyright: International Input-Output Association, available online: http://www.tandfonline.com/doi/full/10.1080/09535314.2017.1369010).
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Kajitani, Y., Tatano, H. (2019). Advantages of the Regional and Sectoral Disaggregation of a Spatial Computable General Equilibrium Model for the Economic Impact Analysis of Natural Disasters. In: Okuyama, Y., Rose, A. (eds) Advances in Spatial and Economic Modeling of Disaster Impacts. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-030-16237-5_13
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