Poverty Among Elderly in Indonesia: Extent, Determinants, and Policy Implications
Bibliographic record
Abstract
ABSTRACT This study investigates the extent and determinants of poverty among the elderly in Indonesia, a country facing rapid demographic aging with limited social protection coverage. Using pooled data from the National Socioeconomic Survey (Susenas) for the years 2018, 2020, and 2022, the analysis applies a binary logistic regression model to identify factors associated with elderly poverty. Results indicate that elderly individuals living in rural areas, without pension or health insurance, with limited education, or facing physical or emotional difficulties are significantly more vulnerable to poverty. Interestingly, contrary to common assumptions, elderly women and those living alone do not appear to be the most at risk. The study also highlights the persistent urban–rural poverty gap and the critical role of pensions in reducing household‐level poverty among older adults. Policy implications include expanding pension and health insurance coverage, investing in elderly friendly infrastructure, and promoting inclusive economic empowerment programs. The findings contribute to a deeper understanding of elderly poverty in middle‐income countries and offer insights for more targeted and equitable aging‐related policies.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".