The importance of efficiency for life insurer profit regarding Canadian life insurers
Bibliographic record
Abstract
Purpose- This study examines 1) how the efficiency of life insurers influences their profits, 2) the influence of exogenous variables such as debt ratio on profits and 3) the critical phenomenon of how feasible it is for a life company to improve its profits via efficiency improvements versus changing other characteristics of its business. Methodology- This study uses stochastic frontier analysis along with data from Canadian life insurers to calculate the required efficiency values along with the above effects and possibilitie Findings- The results are that it is much easier for life insurers to increase profit via efficiency improvements versus improving other business aspects that it can control such as debt ratio or percent of new business written. Conclusion- The results point to the key conclusion that to increase profit, or regain the profit lost due to inefficiency, for the most part and conceivably totally the best, easiest and quite possibly only way for life insurance companies to influence their profit is through improving their efficiency, especially in the vital long-term Keywords: Life ınsurance, efficiency, profit, stochastic frontier analysis, exogenous variables JEL Codes: G22, H21, G28
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".