Disasters are often followed by a large-scale stimulus supporting the economy through the built environment, which can last years. During this time, official economic indicators tend to suggest the economy is doing well, but as activity winds down, the sentiment can quickly change. In response to the damaging 2011 earthquakes in Canterbury, New Zealand, the regional economy outpaced national economic growth rates for several years during the rebuild. The repair work on the built environment created years of elevated building activity. However, after the peak of the rebuilding activity, as economic and employment growth retracts below national growth, we are left with the question of how the underlying economy performs during large scale stimulus activity in the built environment. This paper assesses the performance of the underlying economy by quantifying the usual, demand-driven level of building activity at this time. Applying an Input–Output approach and excluding the economic benefit gained from the investment stimulus reveals the performance of the underlying economy. The results reveal a strong growing underlying economy, and while convergence was expected as the stimulus slowed down, the results found that growth had already crossed over for some time. The results reveal that the investment stimulus provides an initial 1.5% to 2% growth buffer from the underlying economy before the growth rates cross over. This supports short-term economic recovery and enables the underlying economy to transition away from a significant rebuild stimulus. Once the growth crosses over, five years after the disaster, economic growth in the underlying economy remains buoyant even if official regional economic data suggest otherwise.
Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.
Orientation: Large-scale events such as disasters, wars and pandemics disrupt the economy by diverging resource allocation, which could alter employment growth within the economy during recovery.
Research purpose: The literature on the disaster–economic nexus predominantly considers the aggregate performance of the economy, including the stimulus injection. This research assesses the employment transition following a disaster by removing this stimulus injection and evaluating the economy’s performance during recovery.
Motivation for the study: The underlying economy’s performance without the stimulus’ benefit remains primarily unanswered. A single disaster event is used to assess the employment transition to guide future stimulus response for disasters.
Research approach/design and method: Canterbury, New Zealand, was affected by a series of earthquakes in 2010–2011 and is used as a single case study. Applying the historical construction–economic relationship, a counterfactual level of economic activity is quantified and compared with official results. Using an input–output model to remove the economy-wide impact from the elevated activity reveals the performance of the underlying economy and employment transition during recovery.
Main findings: The results indicate a return to a demand-driven level of building activity 10 years after the disaster. Employment transition is characterised by two distinct periods. The first 5 years are stimulus-driven, while the 5 years that follow are demand-driven from the underlying economy. After the initial period of elevated building activity, construction repositioned to its long-term level near 5% of value add. Practical/managerial implications: The level of building activity could be used to confidently assess the performance of regional economies following a destructive disaster. The study results argue for an incentive to redevelop the affected area as quickly as possible to mitigate the negative effect of the destruction and provide a stimulus for the economy. Contribution/value-add: This study contributes to a growing stream of regional disaster economics research that assesses the economic effect using a single case study.
