Improving income protection for the elderly poor in Ecuador
Bibliographic record
Abstract
A series of social benefits targeting vulnerable groups, such as the elderly population, has been implemented in Ecuador over the last few decades. Elderly adults living in vulnerable conditions and not affiliated with social security are entitled to noncontributory pension assistance under the Human Development Transfer program. However, over one quarter of old-age beneficiaries still live in poverty and the recent fall in oil prices has put increasing pressure on government expenditures to deliver such schemes. This paper aims to assess the current needs of old-age adults based on expenditure data, and makes use of microsimulation techniques to evaluate the effect of covering those needs through an increase in pension assistance. Our results show that increasing pension assistance to match the level of the poverty line in Ecuador would reduce elderly poverty by 40% and would take 18% of old-age beneficiaries out of poverty. We analyze the effect of additional hypothetical reforms and discuss the importance of using microsimulation techniques, in particular to assess the effect of budget neutral reforms in a macroeconomic environment with low oil prices.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".