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Record W4392772581 · doi:10.1111/risa.14285

Influence of a private–public risk pool and an opt‐out framing on earthquake protection demand for Canadian homeowners in Quebec and British Columbia

2024· article· en· W4392772581 on OpenAlexafffundabout
Howard Kunreuther, Lynn Conell‐Price, Bohan Li, Paul Kovacs, Katsuichiro Goda

Bibliographic record

VenueRisk Analysis · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsWestern UniversityCanadian Chiropractic AssociationAdvantage Forensics (Canada)Scanimetrics (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPurchasingOddsActuarial scienceBusinessFraming (construction)Private sectorPublic economicsEconomicsGeographyEconomic growthMarketingLogistic regression

Abstract

fetched live from OpenAlex

This article describes the design and analysis of web-based choice experiments that examine how the demand for earthquake protection in Quebec and British Columbia (BC), Canada, is influenced by the default option and the structure of the insurance plan. Homeowners in both provinces were given the opportunity to purchase protection against earthquake losses when presented with one of the following options: the current private insurance plan and proposed public-private Risk Pools with different levels of the public layer. The default frame was changed so the homeowner could either opt-in by purchasing this coverage or opt-out of being given this protection and receiving a premium discount. Assigning participants to the public-private Risk Pools rather than the current private insurance plan increases the likelihood of purchasing earthquake insurance protection by an odds ratio of 2.7 or greater in BC and Quebec. Furthermore, opt-out enrollment design substantially increases take-up of earthquake protection relative to opt-in enrollment. The policy implications of these findings are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.203
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2024
Admission routes3
Has abstractyes

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