Public policy, occupational pension plans, and alternative retirement savings programs
Bibliographic record
Abstract
Last decade witnessed rapid growth of Registered Retirement Savings Plans (RRSPs) and slow decline of occupational pension plans, particularly the defined-benefit plans. Meanwhile federal Income Tax Act and provincial Pension Standards Legislations have been reformed several times. The trend from traditional defined-benefit plans to more flexible defined contribution plans and group RRSP plans has also spurred interest in its impact on labour market, employment relations, and human resource management. To date, no thorough evaluation has been done on the determinants of pension coverage in Canada. Also, research has neither comprehensively assessed the productivity enhancing features of defined benefit plans nor provided a strong test of their labour market impact in comparison to other deferred compensation arrangements. Making use of two nationally representative surveys of Canadian workers and workplaces, my program of study is intended to advance the knowledge of current pension industry and fill the above gaps in the pension literature. Major findings are: (1) Public policies, such as higher marginal taxes, earlier vesting, and more permissive eligibility rules are found to increase pension coverage, while a ban on mandatory retirement is found to have a negative yet insignificant effect, consistent with the institutional view of pensions. (2) Pension plan and hybrid plan coverage (both pension and group RRSP coverage) are found to have significant quit reduction effects, and hybrid plan coverage has the strongest impact. Group RRSPs have small, positive effect on employee quit probability. This evidence suggests that implicit contract theory and expected pension capital loss are the major mechanisms as opposed to labour market sorting. (3) Both pensions and group RRSPs are found to have productivity enhancing effects but for different reasons, and hybrid plans have largest positive impact. Productivity gains from pension plans work more through the channels of rigorous employee selection, quit reduction, and employer-sponsored training, in line with the training and shirking models, while productivity gains through group RRSPs are achieved more through layoff deterrence and greater work effort, consistent with the information asymmetric hiring/selection model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".