Cambio tecnológico y desigualdad de ingresos en México
Bibliographic record
Abstract
El objetivo del trabajo es analizar la desigualdad de ingresos en México durante el tercer trimestre de 2018 y 2021 con información de la Encuesta Nacional de Ocupación y Empleo (ENOE) del Instituto Nacional de Estadística y Geografía (INEGI). Se examina el vínculo entre los ingresos de los trabajadores y una variable de tecnología, construida a partir de las probabilidades de automatización que estiman Frey y Osborne (2017). Se consideran variables de control como el sexo, escolaridad, tipo de empleo, estructura regional y sectorial de la economía mexicana. Se instrumenta un procedimiento para corregir el problema de sesgo por autoselección muestral. Las estimaciones indican que quienes desempeñan ocupaciones con nivel medio y alto de automatización, ganan menos con respecto a quienes exhiben un bajo nivel de automatización. Durante 2018 los porcentajes fueron de menos 25% y 30% y durante 2021, de menos 23% y 28% respectivamente.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".