Is High-Tech Trade a Threat to Employment in Brazil?
Bibliographic record
Abstract
Unemployment is one of the primary economic and social challenges, particularly for developing regions. The automation of processes and production techniques, along with the digitalization of services, has long instigated fear and uncertainty due to their potential to induce what is commonly referred to as “technological unemployment.” From the perspective of international trade, the trade in high-tech products can lead to unemployment regardless of the factor endowments prevalent in the region in question. This paper analyses the effects of traditional and high-tech trade on the Brazilian state’s labor market. We utilized a panel data framework that includes information for the Brazilian states, covering the period from 2000 to 2020, to estimate dynamic labor demand equations using the System-GMM estimator. The results indicate a positive relationship between traditional trade and employment, regardless of the trade proxy adopted and the economic sector investigated. However, this relationship becomes negative when high-tech trade variables are employed. This paper examines how high-tech trade in Brazil impacts employment within a regional and sectoral context, providing important insights for the formulation of public policies aimed at mitigating unemployment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".