Philippine Export Efficiency and Potential: An Application of Stochastic Frontier Gravity Model
Bibliographic record
Abstract
This study was conducted to investigate the issue of what Philippine merchandise trade flows would be if countries operated at the frontier using gravity model. The study sought to estimate the coefficients of the gravity equation. The estimated coefficients were used to estimate merchandise export potentials and technical efficiency of each country in the sample and these were also aggregated to measure impact of country groups. Result of the estimated coefficients of the gravity equation shows that merchandise export flows of the Philippines to trading partners is significantly positively affected by income and market size of the importing partner. The income elasticity of merchandise exports is 0.69%. A 1% increase in market size increases export flow by 0.24%. Distance was estimated to reduce export flow by 1.22% in every 1% increase in distance. The technical efficiency for all sample countries is not so high; it ranged from 38 to 42% with standard deviation of 30. The most efficient countries in the sample which recorded more than 80% efficiency were Singapore (100%), New Zealand (97%), HongKong (97%), USA (96%), Australia (96%), Canada (96%), UK (93%), Denmark (93%), Japan (87%), Malaysia (85%) and S. Korea (81%). Countries with larger markets emerge as high export potentials such as USA, China and Japan with potential ranging from 10 to 30 Trillion US dollars. These potential has been changing within the period. Result of technical inefficiency model reveals that these potential is increased by membership of the Philippines to ASEAN, APEC and WTO. Reduction of corruption and freer labor market in the importing country enhances export potential of Philippine merchandise exports. Commonality of language also enhances these potential.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".