Two way Panel Causality Analysis for Degree of Trade Openness and Size of City
Bibliographic record
Abstract
The relationship between trade openness and economic size of city has long been a subject of much interest in international literature of trade. Trade Openness might increases the economic size of city by increasing the significance of transportation modes which are mostly present in urban areas and raising the demand for marketing, financing and communication. In contrast some literature argue that protectionism generates large cities as firms cluster in an urban area to minimize its unit cost via sharing of intermediate goods, labour pool and knowledge spill-over. Thus, there exist an important causal connection between the economic size of the city and its contribution in international trade. This paper is designed to explore these causal connections using panel causality analysis. The panel consists of fourteen major cities of Pakistan and 14 years commence from 1999-00 till 2012-13. The result affirms a positive two-way causal relationship between a cities' economic size and its degree of trade openness.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".