Is Defined Contribution a Panacea for Defined Benefit Social Security Funding Problems? Lessons from Two Countries
Bibliographic record
Abstract
Many countries are changing their social security retirement program from a defined benefit (DB) to a defined contribution (DC) basis. Other countries, such as the United States, are discussing the introduction of a DC component. The replacement of a DB social security retirement system by a DC system raises many important social and economic issues. Thoughtful consideration must be given to the choice of criteria for prioritizing objectives and outcomes, as well as in weighing the advantages and disadvantages between different cohorts. For example, if any DB obligations are not fully funded at transition, a double burden will rest with transition generation(s). Moreover, economists tend to assess the value of the system based on measures of economic efficiency and the lack of impediments to a freely operating labor market. But such an assessment may not give adequate recognition to factors such as individual wealth/poverty an individual’s ability to make optimal investment decisions, and transaction costs associated with operating individual accounts. Indeed, some countries have suggested that notional defined contribution (NDC) accounts may be the best way to address such issues. Focusing on the adoption of a funded DC social security retirement program in Chile and the adoption of a pay-as-you-go NDC social security retirement program by Sweden, this research identifies factors of financial markets, economics, and demographics necessary to enable a move to DC accounts. In addition, it identifies the characteristics of the financial markets necessary to support payments (wealth transfers) to retirees from a DC social security retirement program. The paper considers the questions of social security funding and plan type (DB vs. DC) and attempts to assess the suitability of certain reform options for the United States. It approaches the issue by identifying the features of each type and the strengths and weaknesses associated with those features. A significant part of this analysis is the illustrative description of two real-world plans, Chilean and Swedish. It then uses the theoretical considerations and the experience of those plans to draw conclusions about reform proposals in the United States, particularly the President’s Commission to Strengthen Social Security Model 2.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".