Filipino Elderly Living Arrangements, Work Activity, and Labor Income as Old-age Support
Bibliographic record
Abstract
This paper explores how elderly labor income can be expanded as a financing source for elderly consumption in the future through increase in elderly work activity. It examines elderly living arrangements and other factors that may influence elderly participation in work activities. The prospects of increasing elderly work activity in the future is assessed based on past and possible future trends in the following three factors, among many others: elderly health status, household headship by the elderly, and employment opportunities for the elderly, particularly household entrepreneurial activities.Alternative scenarios of increases in elderly labor force size (based on assumed changes in the factors) were used in simulations and results show that the higher the increase in labor force size (1) the higher the increase in aggregate labor income, (2) the higher the proportion of consumption that can be covered by own labor income, (3) the higher the elderly deficit age cut-off, and (4) the larger the decline in the aggregate lifecycle deficit of the elderly. What can government do to encourage more elderly to continue working? Government action can focus on two areas: elderly health and well-being, and elderly employment opportunities and enabling environment. The government can finance and fully implement provisions in existing laws and public programs that address the two areas such as those articulated in the Senior Citizen's Acts (1992 Republic Act 7432 and 2003 RA 9257) and the Philippine Plans of Action for Senior Citizens (1999-2004 and 2006-2010).
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".