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Supply Constraint from Earthquakes in Japan in Input–Output Analysis

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  • Michiyuki Yagi
  • Shigemi Kagawa
  • Shunsuke Managi
  • Hidemichi Fujii
  • Dabo Guan

Abstract

Disasters often cause exogenous flow damage (i.e., the [hypothetical] difference in economic scale with and without a disaster in a certain period) to production (“supply constraint”). However, input‐output (IO) analysis (IOA) cannot usually consider it, because the Leontief quantity model (LQM) assumes that production is endogenous; the Ghosh quantity model (GQM) is considered implausible; and the Leontief price model (LPM) and the Ghosh price model (GPM) assume that quantity is fixed. This study proposes to consider a supply constraint in the LPM, introducing the price elasticity of demand. This study uses the loss of social surplus (SS) as a damage estimation because production (sales) is less informative as a damage index than profit (margin); that is, production can be any amount if without considering profit, and it does not tell exactly how much profit is lost for each supplier (upstream sector) and buyer (downstream sector). As a model application, this study examines Japan's largest five earthquakes from 1995 to 2017 and the Great East Japan Earthquake (GEJE) in March 2011. The worst earthquake at the peak tends to increase price by 10–20% and decrease SS by 20–30%, when compared with the initial month's prices/production. The worst damage tends to last eight months at most, accumulating 0.5‐month‐production damage (i.e., the sum of [hypothetical] differences in SS with and without an earthquake [for eight months] is 50% of the initial month production). Meanwhile, the GEJE in the five prefectures had cumulatively, a 25‐month‐production damage until the temporal recovery at the 37th month.

Suggested Citation

  • Michiyuki Yagi & Shigemi Kagawa & Shunsuke Managi & Hidemichi Fujii & Dabo Guan, 2020. "Supply Constraint from Earthquakes in Japan in Input–Output Analysis," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1811-1830, September.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:9:p:1811-1830
    DOI: 10.1111/risa.13525
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    Cited by:

    1. Yagi, Michiyuki & Managi, Shunsuke, 2023. "The spillover effects of rising energy prices following 2022 Russian invasion of Ukraine," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 680-695.
    2. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. He, Kehan & Mi, Zhifu & Coffman, D'Maris & Guan, Dabo, 2022. "Using a linear regression approach to sequential interindustry model for time-lagged economic impact analysis," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 399-406.
    4. Yagi, Michiyuki & Managi, Shunsuke, 2021. "Global supply constraints from the 2008 and COVID-19 crises," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 514-528.

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    More about this item

    JEL classification:

    • D57 - Microeconomics - - General Equilibrium and Disequilibrium - - - Input-Output Tables and Analysis
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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