Payment Persistence of Participants in Turkish Private Pension Scheme and Gender Differences
Bibliographic record
Abstract
<p>By considering the gender differences, this paper investigates the impacts of socioeconomic and demographic attributes on the persistence of individuals’ payments to their own private pension schemes. With separating the individuals according to their genders, we study totally 6,025 participants from 2004 to 2012. For men, it is found that amount of payment, age, marital status, education, being located in the industrial and financial center of Turkey, higher risk tolerance and total period remained in the system are all positively associated with the likelihood of being a persistent payer. For women, the findings for all the attributes align to those for men except for the marital status and being located in the industrial and financial center of Turkey. Overall, our results are plausible for financial institutions and policy makers that are typically sensitive to the payment persistence of the participants to the private pension schemes.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".