A Bayesian Modeling Approach for Estimating Earthquake Reconstruction Behavior. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Wilson, Bradley
• Is Part Of: Annals of the American Association of Geographers. Volume 111:Issue 1(2021)
• Page Start: 283
• Page End: 299
• Publication Date: 2021-01-02
• Subjects: Bayesian modeling -- disaster recovery -- earthquake reconstruction -- item response theory -- Nepal earthquake; 贝叶斯模型 -- 灾害恢复 -- 地震重建 -- 项目反应理论 -- 尼泊尔地震; modelado bayesiano -- reconstrucción sísmica -- recuperación del desastre -- teoría de respuesta itemizada -- terremoto de Nepal; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2020.1756207
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : Rebuilding and repairing damaged physical infrastructure is a primary source of disaster aid spending following major earthquakes. Although aid distribution is monitored, it is not well understood how economic support and technical assistance affect reconstruction behavior. This study develops and evaluates a Bayesian item response theory modeling framework for estimating the probability of reconstructive action from household-level survey data. Household responses on reconstruction status, aid received, and willingness to commit additional resources from Inter-Agency Common Feedback Project surveys ( n = 5, 913) collected eleven, twelve, and fourteen months after the Gorkha, Nepal, earthquake are used to estimate the probability of reconstructive action. Results show differences in marginal reconstruction probabilities ranging from 2 to 78 percent across varying combinations of aid receipt and household willingness to commit additional resources. Estimated reconstruction probabilities are lowest for households with low willingness to commit additional resources and households that have not received a reconstruction-related engineering consultation. All model results showed strong variability with geographic location. These findings provide detailed quantitative estimates of earthquake recovery that have not previously been available and offer a promising methodology for using future postdisaster household-level survey data.