HOW ACCURATELY DOES 70% FINAL EMPLOYMENT EARNINGS REPLACEMENT MEASURE RETIREMENT INCOME (IN)ADEQUACY? INTRODUCING THE LIVING STANDARDS REPLACEMENT RATE (LSRR)
Bibliographic record
Abstract
Abstract Will 70% of a worker's final annual employment earnings sustain living standards after retirement? Despite increasing skepticism, the most dominant measure of retirement income adequacy by financial planners, pensions plan advisors, academics and public policy makers is the “final employment earnings replacement rate”, where 70% is considered the right target to ensure living standards remain at approximately the same level after retirement. Using Statistics Canada's LifePaths dynamic population micro-simulation model, this paper asks whether those individuals from the 1951–1958 Canadian birth cohort who attain roughly a 70% final employment earnings replacement rate (as conventionally measured) at retirement do, in fact, achieve approximate continuity in their living standards. We find that the conventional final earnings replacement rate measure has little predictive value for living standards continuity between working-life and retirement. The primary reason is that employment earnings in a single year is not a reliable representation of a worker's standard of living — it relies on an inadequate pre-retirement measurement period, does not incorporate important components of consumption sources (such as home equity), and ignores household size (particularly children). As a result, we find that the correlation between the conventional earnings replacement rate and actual living standards continuity is relatively low (0.11). The paper therefore suggests an alternative metric for assessing how well a worker's living standard is maintained after retirement — i.e., the Living Standards Replacement Rate, or the LSRR. The LSRR provides a more accurate, understandable and consistent measure of retirement income adequacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".