The impact of the 2016 Kaikoura earthquake on government productivity in Wellington (NZ)

Kaylene Sampson, Joanne Stevenson, Erica Seville, Nicola Smith, Garry McDonald, Morag Ayers, Charlotte Brown, October 2017

QuakeCoRE Project E6471-AF5/ NHRP 2017-ROR-01-NHRP


An Alpine Fault earthquake poses a serious threat to much of the South Island of New Zealand and the lower North Island including the nation’s capital, Wellington. Current economic models being used to assess the impact of a significant earthquake on Wellington are not well calibrated for disruptions to the productivity of the government sector. When an industry suffers a loss in productivity, one of the ways this is translated through an economic system is through price changes.

A shortage of supply in a good or service relative to demand brings about an increase in the price for that good or service. In the case of the Government sector, however, price changes are unlikely to be a realistic response. Many of the services performed by government are not priced in markets in the same manner as other goods and services, and where it is possible Government departments may not see it as ethical or politically expeditious to adjust prices. Yet, even a small change in productivity could have a resulting large economic impact, and therefore must be considered further in economic modelling of hazard disruptions.

This report summarises the initial findings of a project designed to enhance our understanding of the potential impacts of an Alpine Fault earthquake on Government productivity. In this first portion of the project, we examined the productivity impacts of the 14 November 2016 Kaikoura earthquake on four New Zealand Government agencies based in Wellington. The Kaikoura earthquakes caused significant damage to buildings across the city and outlying areas, necessitating temporary or permanent relocation of thousands of workers. Infrastructure outages were relatively contained, but did temporarily disrupt the city in the immediate aftermath of the earthquake and during the response and recovery phases.

As a result of the Kaikoura earthquake, the four case organisations in our study experienced reductions in both input (i.e., labour hours) and output. Every case study agency experienced some degree of building disruption. Although, in every case the reduction in buildings was temporary, in some cases organisations operated days or weeks from fewer buildings.

Each agency found ways to help employees continue working without access to their primary premises. Staff either worked from another department or agency’s premises or worked remotely (from home). The results of the case studies have also shown that one of the principal means by which organisations coped with temporary losses in productivity is through the later recapture of production. For this to be possible, staff must often work harder and longer. In some cases, staff are compensated by additional payments (i.e. overtime, additional contracted hours), but importantly not all additional work is remunerated.

Another response to earthquake generated changes to demand for services was to reprioritise staff members to critical or emergency functions, losing productivity in less critical parts of the organisation. In some cases, non-critical functions were suspended entirely for weeks following the earthquake. It is also important to note that while Government agencies are often pursuing quite different outputs, the sector has several collective oversight bodies. For example, the Government Property Group manages buildings owned or leased across over 60 Government agencies, and assigns space priorities for agencies based on their criticality and dependence on a physical location to conduct their core work. Ultimately, all four organisations reached ‘business as usual’ levels of productivity within days or weeks of the earthquake. Preliminary results suggest that productivity losses were not fully recaptured by the case study organisations.

In the next stages of this project we will be running a simulation of an Alpine Fault event and evaluating the economic consequences using the MERIT suite of models. The primary sources of disruption in our sample of government organisations following the Kaikoura event all occur from damage (or potential damage) to buildings within which organisations operate and consequential disruption to building occupancy. Alpine Fault scenario modelling will derive the information on building disruptions from the RiskScape model. RiskScape contains building asset information for the whole of Wellington, and calculates the level of building damage (i.e., using a five-state classification system) based on the nature of the hazard event, and the ‘fragility’ of assets.

The case studies have highlighted several factors that will help us to calibrate the way MERIT accounts for the impacts of Government productivity changes. From the results of the case studies, we have concluded that a priority for the next stage of the project is to extend the reporting indicators to better capture the nature and distribution of costs. When undertaking economic impact assessments (EIAs) the two most common types of indicators reported are GDP (and ‘value added’ if GDP is broken down by industry groups) and employment. For MERIT modelling we usually just report the former given that short-term disruptions do not tend to have noticeable impacts on numbers of people employed. We will investigate the development of a utility (welfare) indicator in addition to GDP. GDP is predominantly a measure of the size of an economic system, and will be subject to very little change if most lost production can subsequently be recaptured by staff overtime.

Additionally, the focus on staff overtime or increased staff workload as a common adjustment mechanism for affected agencies highlights the importance of considering not only who bears the costs of disruptions (i.e., distribution and equity implications), but also whether the indicators reported in economic evaluations adequately capture and describe the nature and relative distributions of costs.

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