Determinants of Demand for Life Insurance: The Case of Canada
Bibliographic record
Abstract
This study examines the determinants of demand for life insurance in Canada. Lewis (1989) model is used to identify the determinants of life insurance demand. However, based on the findings by Stock and Watson (1988) of possible spurious regression, especially in light of our limited dataset, we focussed on testing for co-integration to establish long-run equilibrium among identified variables rather than estimating a demand model. The Johansen co-integration methodology was applied. The results confirm that education, income, inflation, social security, interest rates, dependency ratio, financial development and life expectancy have long term equilibrium relationship with life insurance. An interesting result was that co-integration between income and demand for life insurance occurred after a 3-year lag period. On the basis of the permanent income hypothesis, an interpretation of this result could be that people wait to make sure that their increase in income is permanent before they increase their spending on certain items, including life insurance. While this study does not produce a definitive structural demand model for life insurance, the results provide a valid basis for governments and other life insurance policy makers across the globe to focus on certain key variables as potential drivers of demand for life insurance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".