Philippines 2007 National Transfer Accounts: Financing Consumption and Lifecycle Deficit by Income Group
Bibliographic record
Abstract
The NTA flow accounts for the Philippines for the year 2007 include not only national level estimates but also estimates by income group. Three income groups are defined, referred to as income terciles. This paper examines the financing of consumption by income group. One source of financing is own labor income. But for age groups whose labor income is not sufficient to cover their consumption, mainly the young and elderly, the difference or the lifecycle deficit is financed by resources reallocated between age groups. The income groups differ in the manner the lifecycle deficits are financed.Some key findings include: (1) for the young dependent age group deficit is financed by public alongside private transfers for the bottom tercile while it is almost entirely private transfers for the top tercile; (2) for the young elderly (under 79 years old) financing of deficit is by asset reallocation and a small proportion by public transfers for the bottom and middle terciles, and by asset reallocation and private transfers for the top tercile; and (3) for the older elderly (age 79 or older) deficit is financed by public transfers (small proportion), private transfers, and asset reallocation for the bottom and middle terciles, and by private transfers and asset reallocation for the top tercile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".