Social Security Reform: Sovereign Wealth Funds as a Model for Increasing Trust Fund Returns
Bibliographic record
Abstract
This article addresses the question of whether foreign sovereign wealth funds (SWFs) should serve as a model for the United States in managing the Social Security Trust Fund. The last ten years has seen a significant shift in the way countries manage public pension and social insurance reserve funds. Rather than invest solely in government bonds, many countries now use modern portfolio techniques to diversify assets and earn higher rates of return for their reserve funds. Even after considering the losses incurred during the 2007-2009 financial crisis, some funds have managed competitive returns. Well-run funds include those found in Canada, New Zealand, Norway and Australia. Curiously, the U.S. has not followed suit even though the long-term benefits of a diversified portfolio are well-known. The reason for this economically irrational behavior is likely rooted in political beliefs about the role of government as an owner of private enterprise. Institutional studies suggest that the rules constraining government investment are not likely to change rapidly given the constraints of path dependence theory. However, the U.S. has seen incremental change in terms of attitudes towards government ownership of private enterprise. Many states run venture capital funds and government employee pension funds have been successful as apolitical state investment entities. Moreover, attitudes towards foreign SWFs have shifted from fear and anxiety over politically motivated investments to a greater acceptance of sovereign investors as wealth-maximizing entities. Crisis also drives change. The Social Security Trust Fund is now expected to be depleted by 2036. Diversifying the Trust Fund could eliminate as much as 30 percent of Social Security’s funding deficit and do so without raising taxes or reducing benefits. Foreign sovereign wealth funds that were created for the purpose of funding national pension systems provide a model for the U.S. to form an independent entity that is apolitical yet able to be held accountable for its actions. As politicians grasp for solutions to Social Security’s funding problems that minimize tax increases and benefit cuts, they should consider adopting the successful diversification models employed by other countries.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".