Global Economic Crisis and the Philippine Economy: A Quantitative Assessment
Bibliographic record
Abstract
This study analyzes how the global crisis may have affected the Philippine economy. To the extent that the Philippines is more globally integrated through trade and labor flow channels than the financial sector, it is expected that impact of the global crisis will weigh heavily on the “real” side of the economy. To assess the likely impacts, a counterfactual “crisis” simulation analysis is undertaken by using a dynamic computable general equilibrium (CGE) model linked to a microsimulation module in order to trace effects: from the macro-economic to the microeconomic level; from output and factor supplies and demands to commodity and factor prices; and from household incomes to levels of poverty and income distribution. Simulation results suggest that all households experience a significant reduction in real income. Both inequality and poverty increase, with urban dwellers experiencing a higher increase in poverty relative to their rural counterparts as most export-oriented industries are located in the urban areas and returns to factors intensively used by these industries fall. 1 This research was carried out with financial and technical support from the Poverty and Economic Policy (PEP) research network, which is financed by the Australian Agency for International Development (AusAid) and the government of Canada through the International Development Research Centre (IDRC) and Canadian International Development Agency (CIDA). 2 Respectively PhD candidate, Centre of Policy Studies (CoPS), Monash University; and Assistant Professor, Economics Department, De La Salle University-Manila. ii Table of
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".