Exports and growth: a causality analysis for the pulp and paper industries based on international panel data
Bibliographic record
Abstract
This study examines the causal relations between exports and domestic production in the pulp and paper industries. The issue is whether exports are the engine of growth, or whether exports follow growth. The data were time-series of the 15 main exporting countries between 1961 and 1995. The method was Granger-causality analysis with error correction, based on models estimated in three ways: ordinary least squares by country, least squares with dummy variables (LSDV), and seemingly unrelated regression. Regardless of method, the strongest relation was an instantaneous (within a year) feedback between exports and production. The LSDV results implied average multipliers across countries of 1.2 to 1.4 from exports to production, and 0.20 to 0.25 from production to exports, in both industries. Experiments with monthly data on the pulp industries of Canada and the USA showed that temporal aggregation could affect the Granger-causality test results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".