Capital Humano y Crecimiento Económico en México (1970-2000). Human Capital and Economic Growth in Mexico
Bibliographic record
Abstract
En los ultimos anos, se han realizado diversos estudios a nivel mundial utilizando el modelo de crecimiento y convergencia economica, a traves de paises y de regiones. Para el caso de Mexico, hasta la segunda mitad de los noventas comienza el analisis empirico de la convergencia economica a nivel regional. En los ultimos 30 anos se observan disparidades regionales que han surgido en Mexico y han alterado el pensamiento convencional con respecto a la persistencia de la convergencia regional en Norteamerica. En el presente estudio, se desarrolla el modelo de crecimiento economico y convergencia utilizando variables de capital humano. Posteriormente, se analiza empiricamente la evolucion de las disparidades regionales en terminos de ingreso per capita en Mexico, condicionando el estudio a las variables de capital humano. Se obtiene un resultado de convergencia en el ingreso condicionado al capital humano para el periodo 1970-2000. El estudio compara los resultados de Mexico con los resultados mas importantes del modelo de crecimiento y convergencia economica en los Estados Unidos, Canada y Europa. This paper applies the regional convergence hypothesis of economic growth based on the neoclassical model. During the period 1970-2000, Mexico shows state convergence in per capita income conditional on the fundamental human capital variables. The study compares the results with previous studies in Mexico, The United States, Canada and Europe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".