Export Changes Due to the 2011 Great East Japan Earthquake and Tsunami: Expanding the scale of shift-share analysis
Bibliographic record
Abstract
Introduction: Disasters can impact national economies in many ways, one of which is through international trade components such as export. with estimated direct damages of over US$211 billion, the Great Eastern Japan earthquake of 2011 has been the costliest disaster in the history of Japan. Although this disaster occurred in an area with lower share in the national and global economy, many Japanese and non-Japanese firms outside the affected area were affected by the ripple effects of this disaster. This study aimed to investigate the extent to which changes in post disaster exports of Japan can be attributed to this disaster. Methods: A modified version of shift-share method was employed to examine the impacts of the disaster on Japan’s export. Considering the regional economic analysis, shift-share analysis is often used to compare regional economic changes with regard to national changes. It decomposes the regional economy changes into universal, industry mix, and competitive advantage components. In this study, an up-scaled shift share analysis was conducted that examined the changes of Japan export versus the world. Two datasets were used in this study. The first dataset included Japan’s exports and the other contained the world’s exports. . The World Trade Organization (WTO)’s online database constituted the main data source. Results: According to the shift-share analysis, Japan experienced some increase in export during the study years, which was due to the overall universal export increases. In the same period, Japan lost some of its exports due to the industry mix component. The results showed that the 2011 Great East Japan disaster had a substantial impact on Japan’s exports through the export reduction caused by the competitive effect. Conclusions: Large-scale disasters may have a significant impact on the overall national economy and export. The results of this study highlighted that the disaster had a negative competitive advantage for all economic sectors of Ja
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".