General Equilibrium Effects of Pension Reforms to Increase Retirement Income in Canada
Bibliographic record
Abstract
It is generally acknowledged that the Canadian retirement income system (RIS) has successfully contributed to poverty alleviation among seniors and helped maintain an adequate and relatively stable income replacement rate. However, increased longevity, the trend decline in registered pension plans (RPP) coverage and lower returns to individual savings could compromise the adequacy of income replacement rates for particular groups of future retirees among lower and middle-income earners. In this context, this paper examines the long-run economic and welfare impact of making the retirement income system more generous via different policy options. The analysis is conducted using a life-cycle computable general equilibrium (CGE) model with endogenous time allocation decisions and human capital accumulation. The three-tier Canadian RIS is represented in the model. In particular, retirement benefits from private pension plans are derived from a fully funded pension scheme and a PAYG system. The model is calibrated with Canadian data and generates a baseline solution which accounts for population ageing. Simulation results suggest that the necessary changes in pension contribution rates would cause some macroeconomic adjustments over the medium to long-run. Overall, better economic and welfare outcomes are achieved when pension increases are financed through a fully-funded pension scheme, because it results in less distortions on labour supply decisions. Sensitivity analyis to alternative model assumptions suggest that the impacts of higher contributions are underestimated when workers do not adjust their hours of work or time allocated to human capital activities. Another aspect to consider is the interaction effect with the Guaranteed Income Supplement and the potential impact of alternative options on retirement decisions by skill level.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".