Abstract
Japan is a country that faces many natural disasters. These disasters adversely affect the economy and are difficult to predict. This study aims to analyze the impact of such disasters on the local economy by using the Monte Carlo experiment.
In this study, we use an inter-regional input–output table consisting of two regions: Fukuoka prefecture and other prefectures. Fukuoka prefecture (Fukuoka-ken) is located on Kyushu Island where a large earthquake occurred in 2005 (Fukuoka Prefecture Western Offshore Earthquake). This region also faces frequent heavy rains and typhoons that cause severe damage.
Based on the table mentioned above, we constructed a CGE (Computable General Equilibrium) model. We then conducted simulations of natural disasters under the assumption that they will act to destroy productive inputs and efficiency. In order to establish a set of shocks representative of the sudden decrease in capital stocks and labor accompanying a natural disaster, we undertook Monte Carlo experiments. CGE simulations were then conducted using the outputs of the Monte Carlo experiments under four alternative scenarios. Results for macroeconomic and industry variables are presented, showing maximum, minimum and average effects, together with their standard deviation.
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Notes
- 1.
The previous version is from Sakamoto (2019) where the method of simulation was slightly revised.
- 2.
The model of Hosoe et al. (2004) solves an optimization problem from the production function including TFP (Total Factor of Productivity) and constructs a nonlinear model. The GTAP model also derives a cost function from the optimization problem and constructs a linear model through logarithmic conversion. The linear model is then solved using software which computes a nonlinear solution using the Euler method or similar (Harrison and Pearson 1996). Rutherford (2010) treats GTAP models as nonlinear models. This is because many researchers of CGE models use GAMS (General Algebraic Modeling System) as a computational tool. However, as shown in Fig. 17.1, while different solution methods are used for the each of the three models, the basic structure of the three models are similar. For instance, all have the same production technology and output transformation functions as depicted in Fig. 17.1.
- 3.
The substitution relationship is focused on whether capital and labor can be replaced in production. Needless to say, labor can replace capital equipment such as machines. Furthermore, even the same intermediate goods can be replaced if they are produced in different regions (Armington 1969). However, it is assumed that intermediate goods and value-added goods are not substitutable. The general form of the substitution relationships is a CES function. In the special case where the substitution elasticity is 1 the CES function reduces to a Cobb-Douglas function, while for a zero elasticity it reduces to a Leontief function which prevents substitution. In the case of perfect or infinite substitution, the form is A+B.
- 4.
In this study, no detailed assumptions are made for final demand, intermediate goods, and exports. A static model is assumed, and investment does not take a form that affects production in the next period. The General Equilibrium Model is constructed from the point of view that the production value should be balanced.
- 5.
In reality, both the price and quantity (stock) of labor and capital fluctuate, but in the model, it is easier to construct by fixing one of them. By fixing the stock, it is impossible to indicate excess labor (employment), but in the model, excess labor is absorbed implicitly by changing prices.
- 6.
p is added before the variables for price variables.
- 7.
- 8.
Capital depreciation is not a stock value. However, assuming that the relative price of capital is 1, it can be assumed that it is capital stock. Labor stock is the same procedure.
- 9.
This is very rough calculation, but at least it was estimated to be higher than the substitution between labor and capital.
- 10.
Improving the accuracy of setting the probability of occurrence and the scale of damage will be the issue for the future. In the author’s earlier study (Sakamoto 2019), we assumed a slightly higher damage than this.
- 11.
Given that a maximum of 40% of the damage occurs with a 50% probability and a maximum of 20% of the damage occurs with a 100% probability, the idea is that multiplying the two probabilities is equivalent.
- 12.
- 13.
The number of variables that change in a single Monte Carlo experiment is 3538 (42L + 42K + 42 × 42 × 3XM = 5376 minus zero data 1838), and a corresponding number of random numbers are generated.
- 14.
Unlike Excel, random number generation in GAMS always generates random numbers with the same pattern (pseudorandom number), so it has high reproducibility due to recalculation.
- 15.
The frequency distribution was counted at an interval of 0.001, and a weight of 0.1 was applied to the frequency two before and two after it, a weight 0.2 was applied to the frequency one before and one after it, and a weight 0.4 was applied to the frequency.
- 16.
- 17.
- 18.
Cabinet Office of Japan, “Kenmin Keizai Keisan (Prefectural economic accounts),” (https://www.esri.cao.go.jp/jp/sna/sonota/kenmin/kenmin_top.html). The figures for 2016 have not been published so nominal values for 2015.
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Acknowledgment
We would like to thank Editage (www.editage.jp) for English language editing.
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Sakamoto, H. (2020). Unexpected Natural Disasters and Regional Economies: CGE Analysis Based on Inter-regional Input–Output Tables in Japan. In: Madden, J., Shibusawa, H., Higano, Y. (eds) Environmental Economics and Computable General Equilibrium Analysis. New Frontiers in Regional Science: Asian Perspectives, vol 41. Springer, Singapore. https://doi.org/10.1007/978-981-15-3970-1_17
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