The Analysis of International Experience of Pension Funds and Main Development Trends
Bibliographic record
Abstract
Key words: pension fund, pension, accumulation, funded system, pay-as-you-go system, private savings, system participant This paper analyzes the international experience of pension funds and the main trends in their development, considering global challenges such as population aging, increased pressure on social security systems, and limited state financial capacities. The study examines three primary models: pay-as-you-go (PAYG), defined contribution (DC), and defined benefit (DB) with comparative analysis from countries including Australia, Canada, Japan, the Netherlands, the United Kingdom, and the United States. The findings highlight a global shift from DB to DC systems, driven by economic sustainability concerns, demographic trends, and the need for investment flexibility. The case of Armenia is examined as a country aligning with global practices through the implementation of a mandatory funded pension system since 2014. A more detailed examination shows that long-term pension system sustainability depends on effective regulation, diversified investment strategies, and public trust in financial institutions. Transparency, risk mitigation, and independent oversight are crucial, while countries adapting their systems to macroeconomic shifts achieve greater stability and stronger protection of future pension rights.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".