How Fiscally Tolerable Is Thailand’s Social Security Pension Fund to Early Retirement Decisions?
Bibliographic record
Abstract
This research paper assesses the fiscal tolerability of the Thai Social Security Pension Fund to early retirement decisions, particularly among the workforce aged 50-54. Starting from 2014, the Social Security Pension Fund is due to pay regular monthly pension benefits to eligible insured persons. There has been increasing concern over the potentially high proportion of early retirees opting for one-time lump-sum old-age benefits instead of the more modest amount of monthly retirement pension. This can create severe shocks to the system. Forecasts and sensitivity analyses under alternative scenarios are conducted using an actuarial method. The estimation employs the latest 2010 National Economic and Social Development Board population forecast. In the worst case scenario, with an early retirement rate of 9 percent or higher per year, the tolerability of the system can be maintained for no longer than 25 years from now. The future generations risk facing a situation in which the old-age benefits may not be promptly received in the expected amount. This points to the important policy precaution that the currently high level of reserves in the Social Security Pension Fund does not ensure fiscal sustainability and tolerability as commonly believed. The result also implies that withdrawal from the social security pension fund by the government for other purposes is fiscally detrimental to life of the fund.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".