Poverty Among Elderly in Indonesia: Extent, Determinants, and Policy Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it