Poor Predictive Power and the Unrealism of International Trade Models: Proposing a More Realistic (Behavioral Economics Based) Model
Bibliographic record
Abstract
Beginning with David Ricardo, if not Adam Smith, economists have developed numerous models to explain, and predicts, trade among nations. As I will demonstrate, these models have had poor predictive powers. It is possible to argue that neither gravity model, nor different versions of the comparative advantage doctrine, or even the more recent model developed by Paul Krugman, could explain international trade during the great recession that began in August 2007. For example, these models could not explain why between the first quarter of 2008 and the first quarter of 2009 global GDP fell by 4.5 % while world exports declined as much as 17%. The scale and speed of that trade collapse poses a challenge to various international trade models. As I will demonstrate, this problem very much stems from lack of realism on the part of the assumptions of those models, the inadequacy and incompleteness of the causes of specialization in those models, or the neglect of trade finance in all those models. In this paper, attempt is made to develop a more realistic model that would overcome the shortcomings of the above international trade models. Prior to the development of my proposed model, I will review all of the above models and discuss the causes of specialization in them.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".