Clubes de convergência de renda na América: uma abordagem através de painel dinâmico não-linear com variável limiar
Bibliographic record
Abstract
The main objectives of this work are to test empirically the hypothesis of income convergence process among American countries, to classify this convergence process as either absolute or conditional and to determine if this process happens in either a linear or non-linear manner. Estimations were made through both TAR (threshold autoregressive) panel and linear autoregressive panel and the results were compared to each other. The sample of countries are composed by Argentina, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Nicaragua, Peru, Trinidad and Tobago, Uruguay, USA and Venezuela. The sample period is 1953-2003 and data are in annual basis. Results held for this sample show no evidence of convergence in both TAR and linear models. Additional estimations were made in sub-samples of countries that compose three American free trade agreements. The porpoise was testing two different hypotheses. The first one is that convergence process occurs in clubs. The second one is the theoretical hypothesis that foreign trade leads to convergence among countries involved in it. The three free trade agreements widened were Nafta, CAN and Mercosul. Results held for Nafta also show no evidence of convergence in both models. CAN’s results show empirical evidence of convergence, as TAR model concludes for absolute convergence in one of two regimes. Results held for Mercosul sample show stronger evidence of convergence process. Both linear and TAR models conclude for absolute convergence, the former in both regimes. Even using a different methodology than conventional β-convergence and σ-convergence, results are in consonance with those found in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".