i How Do Public Pensions Affect Retirement Incomes and Expenditures? Evidence over Five Decades from Canada
Bibliographic record
Abstract
National Institute on Aging. The findings and conclusions expressed are solely those of the author(s) and do not represent the views of SSA, any agency of the Federal Government, or the NBER. We thank Michael Baker and Jonathan Gruber whose previous work we build on with this paper. ii We study the income and expenditures of Canadian elderly families over the five decades from 1960 to 2010. We first document the tremendous changes in income and expenditures using available microdata sources. Through simulations, we then display the impact of the public pension system over a fifty year period. Our analysis reveals three important findings. First, we document that the expansion of Canada’s public pension system over the last 50 years has coincided with a large improvement in elderly living standards, measured by income or consumption. Second, we causally relate these changes using an instrumental variables strategy exploiting variation across ages and years in the Canadian system. We find strong evidence that public pensions have lifted income. For expenditures, the evidence is more mixed but there is strong evidence of improvements in reducing expenditure poverty. Third, taking our estimates on the effect of the pension system on income poverty, we perform counterfactual simulations by applying the public pension system of different decades to data from the 2000s. We find that the
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| 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) | 0.003 | 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".