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Record W3125281113

Fault Lines: Earthquakes, Insurance, and Systemic Financial Risk

2016· article· en· W3125281113 on OpenAlexaboutno aff
Nicholas Le Pan

Bibliographic record

VenueC.D. Howe Institute Commentary · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSystemic riskMoral hazardBusinessFinanceFinancial crisisShock (circulatory)Actuarial scienceEconomicsIncentiveMarket economy
DOInot available

Abstract

fetched live from OpenAlex

The fault lines from a major earthquake in Canada could quickly spread through the insurance industry and have a systemic financial impact. Policymakers should take several steps now to avert this chain of events. Since the financial crisis of 2007/08, policymakers have focused on systemic risk to financial and economic systems, with most of the attention on the banking system. The framework for these efforts has been to build resiliency and shock absorbers to minimize the impact of financial shocks on the real economy. The inevitability of an earthquake in Canada poses a similar systemic financial risk for the insurance industry and the economy as a whole, and similar remedial efforts are required. A federal emergency backstop arrangement for property and casualty insurers, properly designed, would minimize the systemic financial impact resulting from such a catastrophic and likely uninsurable event on those affected and on the economy at large. The moral-hazard implications appear small compared to the benefits of avoiding serious systemic risk. The backstop arrangement should, however, apportion costs, including a possible tranche of further contingent risk-sharing with industry in a way that lessens moralhazard issues. A federal last-resort backstop guarantee could kick in beyond an industry-wide trigger of expected losses, say those associated with a one-in-500-year earthquake – currently approximately $30 billion to $35 billion. This loss estimate would be updated periodically, and the trigger could be set somewhere in excess of the one-in-500 threshold to promote further industry risk-sharing. That said, as part of any Canadian reform package, it is important to bolster the Property and Casualty Insurance Compensation Corporation to deal with insurance industry problems and reduce systemic impacts from severe catastrophes. This would also reduce the likelihood that a federal financial commitment would be triggered and, if triggered, would have minimum costs. Having more tools available in advance to deal with catastrophic events would reduce post-catastrophe disaster claims. This Commentary recommends the following: • Strengthen PACCIC so it can intervene before insurance companies in financial difficulty become insolvent. • Ensure PACCIC has the capability to borrow to reduce its liquidity needs in a crisis. • Following these structural changes, PACCIC should rerun its scenario models to examine how much that could increase resilience to extreme events. Furthermore, insurance industry bodies, as well as the federal and provincial governments, should undertake awareness programs to enhance homeowners’ understanding of catastrophe risks. This should encourage Canadians to evaluate the merits of disaster insurance coverage, particularly in the Quebec City-Montreal-Ottawa corridor where such insurance penetration is far too low. Finally, the insurance industry, under active OSFI supervision, should further develop its models for setting aside adequate capital and claims-paying capacity. Regulators should ensure there is an adequate degree of conservatism and that models are as up to date as possible. OSFI should regularly assess the adequacy of major insurers’ models, as they have done in the banking industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.257
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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