THE EFFECTS OF NAFTA ON ECONOMIC GROWTH
Bibliographic record
Abstract
ABSTRACTThis paper evaluates the effects of the North American Free Trade Agreement on the economic growth of Mexico, the United States and Canada by means of an augmented Solow growth model. Such a model is estimated with panel data through two econometric methods: 1) the Arellano-Bond dynamic panel Generalized Method of Moments, and 2) Feasible Generalized Least Squares. The two techniques are consistent in indicating that trade raises Gross Domestic Product (GDP) per capita, controlling for physical capital stock per capita, human capital formation, total factor productivity, and the capital depreciation rate. However, the most important source of GDP per capita growth is human capital formation, which highlights the need to promote trade while investing more in long-term formal education, short-term training programs and the whole process of knowledge transferring. LOS EFECTOS DEL TLCAN EN EL CRECIMIENTO ECONÓMICORESUMENEsta investigación evalúa los efectos del Tratado de Libre Comercio de América del Norte en el crecimiento económico de México, Estados Unidos y Canadá mediante una versión ampliada del modelo de crecimiento de Solow. El modelo se estima con datos en panel mediante dos métodos: 1) el método generalizado de momentos de Arellano y Bond, el cual se aplica a un panel dinámico y 2) mínimos cuadrados generalizados factibles. Ambos indican que el comercio incrementa el producto interno bruto (PIB) per cápita, controlando para el stock de capital físico y humano, la productividad total de los factores y la tasa de depreciación del capital. Sin embargo, la principal fuente de crecimiento económico es la formación de capital humano, por lo que se debe estimular el comercio internacional e invertir más en educación formal de largo plazo, programas de capacitación de corto plazo y todo el sistema de transferencia del conocimiento.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".