The long shadow of a major disaster: Modeled dynamic impacts of the hypothetical HayWired earthquake on California’s economy
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Abstract
We develop and apply a dynamic economic simulation model to analyze the multi-regional impacts of, and mechanisms of recovery from, a major disaster, the HayWired scenario — a hypothetical Magnitude 7.0 earthquake affecting California’s San Francisco Bay Area. The model integrates loss pathways: capital stock damage, labor supply shocks due to short-term population displacement and longer-run out-migration from damaged areas, and the exacerbating effects of damage to transportation infrastructure capital, as well as various aspects of static and dynamic economic resilience. With input substitution-based static inherent resilience and dynamic resilience in the form of optimal intertemporal and spatial investment allocation, gross output losses range from 0.5 percent to 6 percent across regions, and welfare losses are 0.4 percent statewide but can be ten times as large in hardest-hit areas. Large-scale reconstruction investment is supported by substantial interregional transfers of resources through intra-state trade. Increased output via firms engaging in the key adaptive resilience tactic of production recapture can alleviate a substantial fraction of losses—but only if upstream and downstream barriers to recovery can be lowered quickly.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | The long shadow of a major disaster: Modeled dynamic impacts of the hypothetical HayWired earthquake on California’s economy |
| Series title | International Regional Science Review |
| DOI | 10.1177/01600176231202451 |
| Volume | 47 |
| Issue | 5-6 |
| Publication Date | October 04, 2023 |
| Year Published | 2024 |
| Language | English |
| Publisher | Sage Journals |
| Contributing office(s) | Western Geographic Science Center |
| Description | 42 p. |
| First page | 655 |
| Last page | 696 |
| Country | United States |
| State | California |
| Other Geospatial | San Francisco Bay area |