Dynamic of Brazilian Foreign Trade by Technological Intensity
Bibliographic record
Abstract
This paper aims to investigate the long-run relationship between the real exchange rate and the Brazilian trade balance disaggregated by technological intensity classification, i.e., High Tech, Medium-High Tech, Medium Low Tech, and Low Tech, using monthly data from the period of January 2000 to December 2022. To achieve this aim, time-varying cointegration methodology is used, as it is understood that a linear approach is not well suited for developing economies, that face much influence from external events and internal turmoils, which is just the case of Brazil. It was found that, from the 4 sectors, only the low-tech sector has the usually expected signals, that is, a benefit from exchange depreciation, but even for this industry, this positive effect has been diminishing since 2009, due to structural changes in the Brazilian agricultural sector, which accounts for much of the added value in the low-tech goods. The dynamic real exchange rate elasticities for the medium-high and medium-low tech industries oscilated much throughout the time frame of the study, revealing the great influence of external and internal shocks in the change of the trajectory of these elasticities. The high-tech sector presented opposite signals in the estimated elasticities, revealing that exchange rate appreciations benefit it, probably due to its dependency on imported inputs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".