Should Retirees Hedge Inflation or Just Worry About It?
Bibliographic record
Abstract
(Canada), historical data from Ibbotson Associates, helpful comments from Kristen Moore and editing/research from Anna Abaimova. The inflation rate for typical U.S. retirees is different from and mostly higher than the inflation rate for the population. The Consumer Price Index (CPI) has a lesser-known relative, the CPI-E (for the elderly) with sub-component weights based on the consumer expenditure survey for Americans above 62 years of age. Indeed, inflation doesn’t seem to age well. This suggests that CPI-linked investments such as TIPS, I-Bonds and other real-return mutual funds, may not be the best hedge for individual retirees ’ increasing cost of living over an uncertain retirement horizon. To rigorously argue this point, our paper goes back to dynamic portfolio principles. We derive the optimal asset allocation between a CPI-linked bond fund and a generic investment fund, but for an aging retiree facing an exogenous liability stream that is imperfectly correlated to the real return fund. Our model trades-off the benefits of an imperfect insurance hedge against the risky potential for equity investment growth. Technically, our objective function is to minimize the probability of outliving one’s wealth, which is not an unreasonable desire. Our numerical results indicate that although CPI-linked products are the bedrock of an optimal portfolio, the allocations can vary widely. Retirees who are concerned with maximizing lifetime sustainability – which is equivalent to minimizing retirement ruin – should expose themselves to nominal equity-based assets with potential for growth. The CPI as calculated may not be a conspiracy, but it’s definitely a con job foisted on an unwitting public by government officials who choose to look the other way...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".