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Record W4386941197 · doi:10.7202/1091746ar

The Impact of Regulation on theAvailability and Profitability of AutoInsurance in Canada

2013· article· en· W4386941197 on OpenAlexaffvenueabout
Mary Kelly, Anne Kleffner, Si Li

Bibliographic record

VenueAssurances et gestion des risques · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsWilfrid Laurier UniversityUniversité LavalUniversity of Calgary
Fundersnot available
KeywordsUnderwritingProfitability indexVolatility (finance)EconomicsBusinessExpense ratioMonetary economicsActuarial scienceFinancial economicsFinanceMarket liquidity

Abstract

fetched live from OpenAlex

This article investigates the impact of automobile insurance regulation on the size of the involuntary insurance market as well as the level and volatility of auto insurance loss ratios in Canada. We find that rate reduction orders, product reform and a pricing “Grid” that establishes maximum premiums increase the size of the involuntary market, while prior approval does not have any significant effect. In addition, unlike U.S. studies, we find that prior approval does not significantly impact loss ratio volatility. Our models also incorporate the impact of macroeconomic variables that proxy for the underwriting cycle and investment returns. The results suggest that the insurance underwriting cycle and stock market returns appears to be as important in determining insurers’ usage of the involuntary market as regulation. Taken together, our results suggests that regulatory interventions aimed at addressing affordability issues may have the unintended consequence of aggravating availability issues, and underlying market conditions may exacerbate this effect.

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 score0.439
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.238
Teacher spread0.213 · 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

Citations2
Published2013
Admission routes3
Has abstractyes

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