Canterbury earthquakes made household incomes rise even as recovery costs piled up unevenly

When a magnitude 7.1 earthquake tore through Canterbury, New Zealand, in September 2010, and a far deadlier aftershock struck Christchurch five months later, economists expected household finances in the region to crater. Instead, a new analysis of more than a decade of national survey data linked to administrative records has found the opposite: household incomes in the earthquake zone rose by roughly 7,000 to 7,900 New Zealand dollars relative to comparable households elsewhere in the country, while total spending barely moved. The real story, the researchers show, lies hidden beneath those stable averages, in a dramatic reshuffling of what households actually paid for.

The study, published in the journal Natural Hazards and Earth System Sciences, was led by Quanfu Zhang of Victoria University of Wellington together with Ilan Noy and Yigit Saglam. The team exploited New Zealand’s Integrated Data Infrastructure, a government-run data platform that connects survey responses to tax, welfare and address records. By linking the Household Economic Survey to administrative address histories, they could reconstruct where each household’s reference person lived in the critical window between January 2010 and 22 February 2011, the day the shallow magnitude 6.3 Christchurch earthquake killed 185 people and levelled much of the city centre.

That reconstruction mattered because the survey records alone could be misleading. Households move, and a family interviewed in Auckland in 2015 might well have been living in Christchurch when the ground shook. The researchers combined five administrative address sources, including Inland Revenue and social welfare records, and even relaxed timestamp rules for households whose only address evidence came from tax data, reaching back more than a decade to recover pre-earthquake residences. Each treated household was then assigned a shaking-intensity value by matching its meshblock, a small statistical geography, to GeoNet seismic measurements of the February 2011 quake on the Modified Mercalli Intensity scale.

To isolate the earthquakes’ effect, the team used a propensity-score-matched difference-in-differences design, a workhorse of modern econometrics. Each exposed Canterbury household was paired with two demographically similar households from the North Island, which had never been exposed to the sequence, matched on age, sex, household size and education within each survey wave. Standardised mean differences below 0.1 confirmed the groups were well balanced. Event-study regressions and joint pre-trend tests found no evidence that the two groups had been drifting apart before the disaster, lending credibility to a causal reading of the post-earthquake gaps.

The income results were striking in their uniformity. Total household income rose by an estimated 7,043 NZD in low-intensity areas and 7,252 NZD in high-intensity areas, while regular income, which excludes one-off payments, rose by roughly 7,900 and 7,400 NZD respectively. Because the gains appeared in regular income and were similar regardless of local shaking intensity, the authors argue they reflect the region-wide reconstruction boom rather than insurance cheques. The Canterbury rebuild generated years of strong construction labour demand, and regional census data show Canterbury recorded the second-fastest median household income growth of all sixteen New Zealand regions between 2006 and 2013.

Total expenditure, however, showed no statistically significant average change, with imprecise positive estimates of around 2,000 to 3,400 NZD. That headline stability concealed a frenzy of category-level adjustment. In the most heavily shaken areas, the magnitude of recorded receipts-and-refunds entries, a survey category that captures insurance reimbursements and related inflows, more than doubled, rising by an estimated 107 percent. General insurance spending rose by about 101 percent, transportation by 49 percent, telecommunications by 31 percent, diary-recorded day-to-day purchases by 17 percent, and household maintenance by 63 percent. Fees and subscriptions climbed in both intensity groups, suggesting households clung to services that provided continuity and social connection during a disruptive recovery.

One result defied simple intuition: housing costs rose significantly, by about 36 percent, in low-intensity areas rather than the hardest-hit zones. The authors suggest this may reflect displaced residents from devastated neighbourhoods pushing up rents in neighbouring districts, along with repairs and temporary accommodation in moderately damaged areas. In the worst-hit zones, severe damage may have delayed reconstruction or channelled spending through institutional repair programmes rather than direct household outlays, muting the measured response.

The recovery was also starkly unequal across the income distribution. Above-median-income households adjusted broadly, with insurance-related entries rising by an estimated 183 percent in high-intensity areas, general insurance spending up 144 percent, and significant increases in housing, maintenance, telecommunications and subscription costs. Below-median-income households showed a much narrower pattern, concentrated in refunds, medical and health spending, routine purchases and travel, with their receipts-and-refunds increase reaching only about 41 percent. The gap, the authors note, is consistent with differences in asset ownership and insurance depth: wealthier households simply had more valuable, insurable possessions and greater capacity to navigate claims and repairs, echoing international evidence that reconstruction benefits often flow disproportionately to those already better resourced.

Migration added a further layer of divergence. Households that left Canterbury after the earthquakes did not differ significantly from those who stayed in total income, regular income or total spending once age, sex and household size were accounted for. But their financial lives had been restructured: relocators faced housing costs roughly 24,900 NZD higher than stayers, while stayers carried mortgage and loan payments about 9,600 NZD larger. The authors stress these are descriptive associations rather than causal effects of moving, since relocation was self-selected, but the pattern fits international findings that younger, less housing-secure households are the most mobile after disasters.

The broader lesson is a caution against judging disaster recovery by aggregate income alone. Rising incomes in Canterbury coexisted with housing damage, displacement, higher living costs and a welfare-uneven distribution of compensation, meaning measured income growth may reflect costly reconstruction rather than genuine wellbeing gains. The authors also flag limitations: the survey is a repeated cross-section rather than a panel, expenditure data were collected consistently in only four waves, and pre-earthquake addresses could not be recovered for 15 to 19 percent of households, a shortfall mirrored in census linkages. Yet the robustness checks, including stricter matching calipers, one-to-one matching and alternative weighting estimators, left the core conclusions intact. As climate-driven hazards intensify worldwide, the Canterbury experience shows that what a disaster does to household finances cannot be read from any single number, and that the families least able to adjust are often the ones whose struggles averages never capture.

Subject of Research: Household income and expenditure effects of the 2010–2011 Canterbury earthquakes in New Zealand

Article Title: The impact of the Canterbury earthquakes on household income and expenditure in the Canterbury region in New Zealand

Article References: Zhang, Q., Noy, I., & Saglam, Y. (2026). The impact of the Canterbury earthquakes on household income and expenditure in the Canterbury region in New Zealand. Natural Hazards and Earth System Sciences, 26(10), 4937-4975. https://doi.org/10.5194/nhess-26-4937-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4937-2026

Keywords: Canterbury earthquakes, household income, household expenditure, disaster economics, difference-in-differences, propensity score matching, New Zealand, Christchurch, insurance, relocation, Integrated Data Infrastructure, earthquake recovery