Risk Attitude towards Mandatory Retirement Protection in Hong Kong: Why Are Risky Investments More Attractive?
Bibliographic record
Abstract
The government of Hong Kong Special Administrative Region (HKSAR) launched the Mandatory Provident Fund (MPF) scheme as a major source of retirement protection. Now, over 2.6 million employees and self-employed persons are currently making contribution to this scheme and they are required to decide on allocation of their MPF contributions to different MPF choices. However, we know little about how their decisions are formed. One major contribution of this study is to investigate people’s perceptions of MPF and their preferences of MPF choices. And, this study has adopted the predictions of prospect theory, in order to explain why risky choices are more attractive to the public. Findings indicated that more individuals perceived MPF contribution as a loss, than those who perceived it as a gain; those who perceived MPF as a loss generally made higher contributions to the risky fund.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".