Voluntary Job Separations and Traditional versus Flexible Workplace Savings Plans: Evidence from Canada
Bibliographic record
Abstract
In this article, we provide new insights into the longstanding empirical issue of whether the type of workplace savings plan offered by employers affects voluntary job separations by employees. In particular, we compare traditional registered pension plans (RPPs)—commonly DB plans with lock-in provisions and benefit schedules that are back-loaded in job tenure—and flexible group registered retirement savings plans (group RRSPs)—low-cost, portable, DC plans, as well as plans that offer a hybrid arrangement of the two. To this end, we use a Canadian employer–employee matched dataset that provides information on both job transitions and the types of workplace savings plans held by employees and offered by employers. This dataset makes it possible to control directly for a potential confounder that besets the related literature, namely that workers with different propensities to stay in their jobs may self-select into firms on the basis of the type of savings plan offered, by accounting for firm-level provisions and including firm fixed effects. The standard prediction from implicit contract theory is that traditional pensions reduce quit rates but flexible plans have little effect because of their portability. The results are partially consistent with this prediction. In particular, not having a workplace savings plan increases the quit rate by around 1.5 percentage points—an approximately 20 percent increase relative to belonging to a traditional pension plan. We observed little difference between having a flexible plan versus no coverage at all, on average, although imprecision of the estimator means the possibility that flexible plans induce staying behaviour also cannot be rejected. We found that hybrid arrangements, which are relatively common in Canada compared with flexible plans and are designed to jointly offer elements of traditional coverage and greater flexibility to workers, affect quitting the same as traditional pensions only. The results are robust to using different estimation techniques and controlling for the possibility that workers do not fully understand their plan type by accounting for the availability of plans within firms as reported by employers. Implications of these findings for current public policy are discussed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".