Bibliographic record
Abstract
In recent years, members of Congress and academia have repeatedly urged the U.S. Treasury to issue some portion of its debt in the form of inflation indexed bonds. With an indexed bond, the interest and maturity value are adjusted by the rate of inflation over the life of the bond. Because the cash flow of an indexed bond is adjusted for inflation, the bond’s real value does not vary with inflation, protecting investors and issuers alike from inflation risk. Inflation indexed bonds would be a fundamental innovation in U.S. financial markets, providing benefits to investors, the Treasury, and policymakers. Despite the potential benefits, the U.S. Treasury has never issued indexed bonds. In fact, only a handful of industrialized countries, including the United Kingdom and Canada, have issued inflation indexed government bonds. This article discusses the benefits of inflation indexed Treasury bonds and points out some of their limitations. The first section shows how indexed bonds differ from conventional bonds. The second section discusses why investors, the Treasury, and policymakers would benefit from adding indexed Pu Shen is an economist at the Federal Reserve Bank of Kansas City. Corey Koenig, an assistant economist at the bank, helped prepare the article. bonds to the spectrum of U.S. Treasury debt instruments. The third section discusses some of the technical limitations of the bonds. The article concludes that, if carefully designed, inflation indexed Treasury bonds are likely to be beneficial.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".