The Determinants of Pakistan Exports of Textile: An Integrated Demand and Supply Approach
Bibliographic record
Abstract
The determinants of demand and supply of textile and clothing exports of Pakistan are examined for seven major trading partners (US, UK, Canada, Italy, France, Japan and Spain) over the period 1972 to 2013. The simultaneous equation model is estimated by Generalised Method of Moment to handle simultaneous equation bias and for consistent and more precise estimates classical Empirical Bayes technique is applied. The results reveal that income of trading partners and devaluation policy has important and significant role in explaining exports performance of textile and clothing of Pakistan. As regards the supply side, the relative prices and capacity variable are important in determining the textile and clothing exports, however, the real wages have significant but small effect on textile and clothing exports supply. The removal of quantitative restrictions fails to provide incentives to the suppliers. The high income elasticity for the demand suggests that focus should be on raising the factors which can help in expansion of textile and clothing products in local market and marked countries. Keywords: Textile and Clothing Exports of Pakistan, Simultaneous Equations, Real Effective Exchange Rate, Agreement on Textile and Clothing
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".