Leveraging data-driven approaches to explore the effect of various disaster policies on post-earthquake household relocation decision making

Abstract

Earthquake events can cause affected households to relocate. Post-earthquake relocation disrupts displaced householdsﾒ social ties as well as their access to affordable services. Simulation models that capture post-earthquake relocation decision-making can be useful tools for supporting the development of related disaster risk reduction policies that aim at mitigating disaster-induced relocation. Yet, existing versions of these models focus particularly on housing-related factors (e.g., housing repair costs), which are not the sole driver of post-earthquake relocation. In this paper, we integrate data-driven approaches and local perspectives into an existing simulation-based framework to holistically capture various context-specific factors perceived as being important to household relocation decision-making. The enhanced framework is used to quantitatively assess the effectiveness of various disaster risk reduction policies - both 'soft' (e.g., post-earthquake livelihood assistance funds) and 'hard' (e.g., upgrading existing infrastructure facilities to higher building codes) - in reducing post-earthquake household relocation, with an explicit focus on low-income households. We demonstrate it using a possible future (50-year) projection of ﾓTomorrowvilleﾔ, a synthetic expanding urban extent that imitates a Global South setting. Our analyses suggest that livelihood assistance funds are more successful and pro-poor when it comes to mitigating positive post-earthquake relocation decision-making than hard policies focused on strengthening buildings (at least in the context of the examined case study).