What Explains German Export Performances? Spatial Econometric Evidences: 1995 to 2014
Bibliographic record
Abstract
German exports achieved outstanding performances, yet there is lack of research utilizing spatial econometric evidences. This paper explores four explanations and evaluates their empirical contributions: (i) German exports were highly correlated to its imports. Thus, its exports built upon bilateral trade flows. (ii) German exported to countries with high GDP per capita with the capability and the demand of high-quality and less price-elastic goods. (iii) It exported to countries with economic integration with other countries such as free trade agreements. (iv) Its exports broadened from Europe to other countries in America and the Asia Pacific region with increasing total export-volume growth. Thus, German exports benefited from the free trade flow to a few EU member countries, those are close geographically and culturally to Germany. The empirical evidence also points out that the changing geospatial distribution of German exports is another key factors to its export success. The spatial Durbin model was identified to be the best fit model of all after a series of tests. Decisive determinants of its exports performance were found through the estimation besides geospatial analyses of its exports by employing Moran’s I.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".